R&D Management in industrial companies: Tools and

R&D Management in industrial companies: Tools and organizational

changes. A case study.

TESIS DOCTORAL POR COMPENDIO

Mención Doctorado Industrial

Programa de Doctorado

“Gestión Estratégica y Negocios Internacionales”

Directores de Tesis

Prof.a Dra. Joaquina Laffarga

Prof. Dr. José Luis Galan Gonzalez

Prof. Dr. Juan Luis Ramos Martin

Tutor Industrial

D. José López Dominguez

Doctorando

Miguel Valdivia Borrero

Sevilla, 2020

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Esta tesis se ha desarrollado en el marco del Programa de Doctorado Gestión Estratégica y Negocios Internacionales de la Universidad de Sevilla y, por su ámbito de investigación, en la línea de la gestión y dirección de empresas. Además, opta a la Mención de Doctorado Industrial al haber sido realizada en colaboración con una empresa con la que el doctorando mantiene una relación laboral desde hace más de 9 años; habiendo estado el doctorando involucrado en los temas de análisis e investigación que se presentan en esta tesis.

Como se desarrolla a lo largo de los capítulos de la tesis, el estudio analiza y profundiza sobre el sistema de gestión integral de proyectos de I+D dentro de una compañía multinacional, desde el análisis técnico a su enfoque a mercado. Esta cuestión de investigación constituye un reto y problema para la empresa, en la medida que se trata de una compañía que ha experimentado una profunda transformación, pasando de una firma industrial tradicional a una basada en la tecnología y la innovación. En consecuencia, el estudio debe permitir comprender los puntos críticos del proceso de cambio y desarrollar instrumentos que permitan una eficaz gestión de la I+D.

Como parte de las herramientas para la gestión de la I+D, la tesis analiza en mayor detalle el modelo desarrollado para la valoración de proyectos de I+D e innovación, que pretende ser un avance respecto a los modelos actuales, al incorporar mayor capacidad analítica y amplitud de aplicación. Igualmente, se analiza la transformación de la compañía, desde una empresa típicamente de base industrial a una compañía tecnológica, analizando cuáles han sido sus fortalezas y sus inconvenientes. En consecuencia, esta investigación comparte y satisface, en cierta medida, el interés de la compañía para analizar, comprender y mejorar los cambios y modelos de gestión implementados en la empresa.

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Introducción

La I+D y la innovación son elementos claves, necesarios y relacionados para que las empresas puedan ser competitivas en todo tipo de mercados y sectores (Shavinina, 2003; Cefis and Orietta, 2006; Tohidi and Jabbari, 2012). A la hora de afrontar cómo innovar, las empresas deben tomar decisiones acerca de la estrategia de implementación de la I+D (Kodama, 2003; Zhang et al., 2007; Johnson et al., 2008), la gestión de esta función o recurso y el objetivo donde enfocar la innovación. No es lo mismo gestionar y organizar la I+D en la búsqueda de innovaciones disruptivas que incrementales; innovar en producto en un mismo mercado o en la búsqueda de nuevos mercados; o innovar en procesos organizativos en lugar de productos. Las aproximaciones que las organizaciones pueden utilizar varían en función de su objetivo e incluso se pueden emplear varios enfoques a la vez (Green et al., 1994; Nieto, 2003).

Sobre la gestión de la I+D e innovación existe una amplia literatura (Coombs, 1996; Argyres and Silverman, 2004; Adams et al., 2006; Albors-Garrigos et al., 2018; Tidd, 2018), debido a su importancia teórica y práctica, ya que la inmensa mayoría de organizaciones se han dado cuenta de la importancia de innovar para alcanzar sus objetivos. Existen sectores en los que las empresas históricamente han tenido que ser innovadoras para mantenerse en la vanguardia de sus competidores, como, por ejemplo, la industria aeronáutica o farmacéutica, entre otras; por otro lado, hay sectores tradicionales o industriales, asentados, donde las innovaciones o las inversiones en I+D eran limitadas o casi inexistentes, como sucedía en el sector textil o agroalimentario, pero que cuyas empresas están sintiendo la importancia de la innovación para ser competitivas. Si nos fijamos en los sectores de la energía y medioambiente, que constituyen el contexto industrial en el que esta tesis está enfocada, la necesidad de innovación es más acuciante, ya que en las últimas dos décadas la conversión hacía fuentes de energías renovables y sostenibles ha empujado la aparición de nuevas tecnologías y productos, por la creciente preocupación con el cambio climático (la OCDE ya califica estas industrias como de tecnología alta y media-alta) (Bointner, 2014).

Estos cambios tecnológicos han impulsado a muchas empresas del sector, de distinto tamaño, mercado o servicios, a aprovechar las oportunidades que han surgido, y están surgiendo, en el negocio de las energías renovables y medioambiente, para realizar cambios en sus estrategias que permitan que las compañías tengan acceso a estos nuevos productos. En España, los casos de Gamesa, en la producción de aerogeneradores, o de Iberdrola, en la generación de energía eléctrica a partir de fuentes renovables, son ejemplos ilustrativos, pero representan sólo la punta del iceberg de una miríada de nuevas empresas y de corporate venturing que están modificando sustancialmente el sector.

En muchos casos, son empresas con una potente base industrial, con productos maduros donde la innovación es limitada y la inversión en I+D reducida o casi inexistente. ¿Cómo conseguir que estas empresas tengan acceso a nuevos mercados emergentes donde la base tecnológica es crítica? En el nuevo contexto tecnológico, estas compañías requieren una profunda transformación tanto de sus operaciones como de sus estrategias, organización y cultura (Kodama, 2003). En esta tesis se describe y analiza, mediante la metodología del caso longitudinal, la transformación

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de una empresa con profunda base industrial en una empresa tecnológica, explorando diferentes aspectos que este cambio radical conlleva. Estos aspectos se sitúan en niveles diferentes de la organización y abarcan cuestiones diversas. En este estudio se abordan tres de esas cuestiones, que definen los objetivos generales que se persiguen con la investigación.

En primer lugar, y al nivel superior de la organización, estas profundas trasformaciones se inician por decisiones de los órganos de gobierno de la compañía, en especial su Consejo de Administración, dando instrucciones u orientaciones al poder ejecutivo (Consejo de Dirección, Comité de Estrategia, Comité Operativo) para implantar dicho cambio estratégico (Guerras y Navas, 2015).

Las primeras decisiones deben marcar el nivel o grado de cambio y los pasos o fases del proceso a desarrollar ((Johnson et al., 2006). También es preciso establecer cuál va a ser la importancia de la I+D e innovación para la Compañía, no solo para el departamento o unidad de I+D en cuestión, sino también su función como vector de crecimiento y base de la ventaja competitiva de la compañía.

En este caso, el nivel jerárquico y ejecutivo del máximo dirigente de la I+D en la compañía señala claramente la orientación de la empresa en el ámbito de la innovación. Por tanto, la relevancia del generalmente denominado CTO (Chief Technology Officer) define internamente, y también externamente, cual es la importancia que la compañía quiere darle a la investigación y la innovación (Smith and Smith, 2003; Borden and Weig, 2018)

Como parte fundamental de la transformación de la empresa, y desde el punto de vista organizativo, es preciso considerar la propia estructura de la I+D, si se define de forma centralizada versus descentralizada. Esta decisión determinará en gran medida los resultados y procesos operativos de la compañía y vendrá condicionada por el propio objetivo que la empresa fije para la investigación (Zhang et al., 2007; Tirpak et al., 2015).

El primero de los objetivos que persigue esta tesis es comprender el proceso del cambio de una empresa industrial a una tecnológica, analizando y mostrando las ventajas e inconvenientes de esa transformación. El estudio de caso longitudinal se centra en una empresa que, además de un aumento notable de sus inversiones en I+D, incorporó el puesto de CTO a su comité de dirección y consejo de administración, y que tomó decisiones relevantes sobre la estructura organizativa de la I+D de la compañía. A partir del estudio de esta compañía se alcanzan conclusiones que consideramos aplicables a otras empresas que desarrollan, o están pensando iniciar un proceso similar.

En segundo lugar, más allá de querer ser una empresa de I+D, que representaría la intención o propósito estratégico (Hamel and Prahalad, 1991), está la propia gestión de la I+D dentro la organización. En una empresa privada esta gestión persigue objetivos de retorno distintos a los que tienen las universidades o centros públicos de investigación.

La I+D, como cualquier otra función, proceso o proyecto dentro de una compañía, necesita un método de gestión para alcanzar sus objetivos (Argyres and Silverman, 2004; Zhang et al., 2007). La gestión de proyectos de I+D se puede hacer por

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Introducción

distintas metodologías ya conocidas y descritas en la literatura (Cooper, 1990; Nieto, 2003; Yongtae and Seonwoo, 2005).

Aunque los proyectos de I+D, por su propia naturaleza, tienen unos riesgos intrínsecos e incertidumbre distintas a los proyectos ya comercialmente maduros, deben ser gestionados con recursos, plazos e indicadores que permitan medir el desarrollo de dicho proyecto. Dentro de los sistemas de gestión de proyectos de I+D, se pueden mencionar algunos de los más conocidos como el Stage-Gate (Cooper, 1990, 2008), Key Indicators Performance (KPIs), entre otros. No obstante, más allá de las herramientas propias de los proyectos, los gestores de las compañías necesitan cuantificar la rentabilidad potencial de la inversión esperada en un proyecto de I+D para obtener un producto comercial o, en otras palabras, cuál es el retorno esperado para los recursos destinados a dicho proyecto de I+D. Para este cálculo existen los llamados modelos de valoración de I+D (Luo et al., 2008; Farrukh et al., 2009; Lambert et al., 2015; Fernandes et al., 2016).

La valoración de la inversión en I+D presenta determinadas particularidades si la comparamos con la inversión con un proyecto maduro. Los inputs de los modelos de valoración, es decir, los plazos, la incertidumbre en mercados y productos, los recursos, suelen tener un alto grado de variabilidad en los proyectos de I+D y, además, varían en función del estado de madurez de los proyectos.

La literatura recoge distintas metodologías de valoración de proyectos o de un porfolio de proyectos, como son el Valor Neto Presente (NPV, por sus siglas en inglés), los arboles de decisión o las opciones reales (Real Options). Todas ellas presentan sus ventajas e inconvenientes, pero son escasamente aplicadas en la industria (Poh et al., 2001). En un caso por ser demasiado determinista para un proyecto de I+D, como sucede con la metodología de NPV, que no es capaz de reflejar la incertidumbre asociada a los proyectos de I+D (Wang and Halal, 2010). En otro caso, su aplicación práctica resulta complicada, como sucede con las Opciones Reales, que permiten obtener un valor a futuro para continuar o no con el proyecto, pero sus dificultades prácticas provocan que los gestores de I+D o gerentes de las compañías no apliquen esta metodología, ya que es difícil analizar y tomar decisiones a partir de sus resultados (Santos et al., 2014). Otra de las metodologías de valoración comunes es el árbol de decisión, que permite definir distintos escenarios que son usados en los métodos de valoración, pero también presenta dificultades en el caso de escenarios múltiples, pues la excesiva ramificación hace el modelo complejo de gestionar y entender, lo que lleva a que su aplicación en la industria no sea extendida (Wang and Halal, 2010).

Por tanto, el segundo de los objetivos de esta tesis es la propuesta y desarrollo de un modelo de valoración de I+D que permita realizar la estimación del retorno de la inversión en I+D para cada uno de los proyectos. Se trata de construir un modelo capaz de reflejar la incertidumbre asociada a estos proyectos y, al mismo tiempo, de fácil aplicación e interpretación, proporcionando y mostrando los resultados de forma que ayuden a la dirección de las organizaciones en la toma de decisiones relativas a dónde focalizar los recursos. El modelo de valoración no trata solo de estimar el retorno final, sino también intenta focalizar, dentro de un mismo proyecto, las áreas de mejora que pueden tener un impacto directo en el retorno

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Introducción

potencial de la inversión. En definitiva, una herramienta que mejore la gestión de I+D y permita, de una forma clara, evaluar el retorno potencial del proyecto.

En tercer lugar, uno de los puntos clave de la inversión en I+D es su financiación, de dónde se obtienen los recursos para una actividad cuyo retorno se espera en un plazo medio o largo y con un alto grado de fracaso (Fertig, 2018). La elección de financiar la I+D de forma endógena o interna, o la búsqueda de financiación externa, constituye una decisión estratégica de la empresa, que tiene implicaciones significativas en las cuentas y ratios de la compañía, sobre todo si las inversiones necesarias son elevadas.

El propósito de estas elevadas inversiones, en una empresa privada, es la obtención de un producto, o la introducción o mejora de un proceso, que permita recuperar la financiación utilizada y obtener una rentabilidad aceptable para los inversores. En consecuencia, el estudio de mercado constituye otro de los puntos clave en el desarrollo tecnológico: debemos no sólo obtener un buen producto, sino además que este sea comercializable. Para una empresa, tener un producto altamente novedoso, pero que no tenga aplicación comercial, puede llegar a ser tanto fracaso como si no hubiera desarrollado la tecnología. La innovación es necesariamente novedad más comercialización (Fernández Sánchez, 2005). Por esta razón, los proyectos de I+D en las empresas deben incluir, además del desarrollo tecnológico, un estudio detallado del mercado y los competidores que permita monitorizar la necesidad de cambios o ajustes en el producto bajo desarrollo.

Conocer y comprender cómo se comercializaría el producto es una cuestión crítica para no encontrarse con proyectos tecnológicamente punteros, pero que finalmente no sean vendibles, entendiendo como vendibles, que pueda comercializarse; y en el caso de proyectos con una inversión de capital muy elevada que sea financiable, es decir, vendible a los inversores. Es preciso tener en cuenta que a los problemas habituales para financiar proyectos que requieren grandes inversiones en capital (CAPEX), los proyectos con tecnología novedosa añaden otras dificultades, relacionadas con su elevado riesgo tecnológico, que hacen más complicada su financiación. Por tanto, en las fases iniciales de desarrollo de los proyectos de I+D se debe tener claro el producto que se va a comercializar y cómo disminuir o acotar el riesgo tecnológico de conlleva (Guarascio et al., 2017).

Las grandes corporaciones están obligadas a pensar en nuevos modelos de negocios para sus productos, no solo para los maduros, sino también para aquellos productos tecnológicamente novedosos, de forma que aseguren una correcta comercialización de los mismos y una recuperación y rentabilidad adecuada de la inversión realizada en los proyectos. Por tanto, una gestión eficiente en I+D requiere que las empresas dediquen a estas actividades equipos multifuncionales y no solo técnicos focalizados en el desarrollo tecnológico (Hoisl et al., 2017; Martinez et al., 2017).

El tercero de los objetivos de la tesis consiste en mostrar y analizar un caso real de gestión de un proyecto de I+D, que supone el estudio del estado del arte de una tecnología en desarrollo. Este estudio permite ilustrar y analizar el enfoque de una compañía respecto hacia dónde y con qué objetivo llevar a cabo los proyectos de I+D. En el caso analizado se muestra el desarrollo de una tecnología con dificultades técnicas, legislativas y de mercado. La tecnología consiste en la producción de

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Introducción

combustibles a partir de residuos agrícolas; concretamente etanol partir de la paja de maíz, que es el llamado etanol de segunda generación.

Estructura de la tesis.

Para abordar los objetivos de esta tesis: (i) analizar un caso real de transformación de una compañía de base industrial en una empresa tecnológica; (ii) evaluar las herramientas de I+D y proponer un modelo de valoración de I+D que ayude en la gestión de los proyectos y la cuantificación del retorno esperado de la inversión; y (iii) ilustrar a través de un ejemplo práctico la gestión completa de un proyecto en I+D que supone focalizar el desarrollo tecnológico en las necesidades del mercado; la tesis se estructura en una serie de capítulos, versiones de algunos de los cuales ya han sido publicados en revistas JCR y uno de ellos se encuentra en proceso de revisión.

Tras esta introducción, en el capítulo 1 se analiza la transformación real de una compañía que implementa una estrategia proactiva y agresiva para convertirse en un referente tecnológico en el sector de las energías renovables y medioambiente. A través del método del caso longitudinal, se ha analizado la evolución de la empresa desde 2004 hasta 2014, que ha supuesto el cambio desde una I+D descentralizada y con recursos gestionados por las unidades de negocios, hasta una estructura de I+D centralizada que permitiera una única visión de la I+D y compartir conocimientos transversales de la compañía. Esta transformación tuvo como antecedente el nombramiento de un CTO en la compañía, que se situó en los niveles más elevados de decisión.

Para el estudio de la evolución, se han analizado las cuatro áreas o dimensiones que determinan la presencia de un cambio estratégico o radical en una compañía (Virany et al, 1992):

(i) Cambio en la estrategia de la compañía. El primer paso para la transformación de una empresa de base industrial en una tecnológica o basada en la innovación, es la decisión de los órganos de gobierno acerca de la nueva estrategia que se va a desarrollar, el fin que persigue y los cambios que conllevará en la organización (Hunger and Wheelen, 2003).

(ii) Cambios en la estructura organizativa. Estos son cambios estratégicamente inducidos, la organización se tiene que adaptar a las nuevas necesidades de la estrategia (Chandler, 1962).

(iii) Cambios en la estructura de poder de la compañía, y en concreto en el sistema de gobernanza. Al igual que la estructura organizativa de la compañía, los órgano de gobiernos deben adaptarse a la nueva realidad, de forma que los cambios estratégicos deben venir acompañados normalmente de cambios en el accionariado de la compañía, en el Consejo de Administración y en el Comité de Dirección, para la incorporación de perfiles que se ajusten mejor a la nueva empresa que se está creando (Baysinger et al., 1991) .

(iv) Cambios en los sistemas de control. En el caso estudiado, este control se ha focalizado en los principales ratios financieros y de solvencia para analizar el impacto que ha tenido el cambio estratégico en ellos. La evolución de estos

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ratios constituye un indicio de las consecuencias del proceso de transformación de la compañía (Simons, 1994).

El análisis del caso permite obtener conclusiones interesantes para otras empresas que estén o se planteen iniciar un proceso de transformación radical hacia un mayor énfasis en la tecnología y la innovación. El estudio señala algunos de los problemas que el cambio estratégico ha supuesto para la empresa, así como algunas decisiones que se deberían haber adoptado y que su omisión ha perjudicado en buena medida a los ratios financieros y de solvencia de la compañía.

Una versión de este capítulo se ha concretado en un artículo, que lleva por título When industrial companies become technology-driven: Abengoa case study, y se encuentra en avanzado estado de elaboración para someterlo a publicación de una revista.

En el capítulo 2 se aborda el estudio de un modelo de evaluación para los proyectos de I+D en el sector medioambiental y de las energías renovables. La I+D requiere, de forma generalizada, inversiones rodeadas de gran incertidumbre y que generan retornos a largo plazo. Además, en algunos sectores, como el energético o el medioambiental, la inversión suele ser bastante elevada. Por esta razón, la organización que acometa este tipo de inversiones debe contar con herramientas de gestión de los proyectos de I+D eficaces, y entre ellas una que sirva para valorar el potencial de retorno de la inversión.

En este capítulo de la tesis se propone y explica una metodología que permite a los gestores de las empresas analizar los proyectos de I+D desde el punto de vista de su impacto en el retorno económico. También indica a los responsables de estos proyectos dónde focalizar los recursos, ya que facilita la estimación de cómo impacta la mejora de cierta variable técnica en el retorno económico del proyecto; pudiendo, por tanto, decidir si merece la pena la inversión para obtener dicha mejora.

Respecto a los anteriores modelos descritos en la literatura, que son brevemente analizados en el texto, el modelo propuesto y desarrollado permite una aplicación y análisis de resultados entendible para los gestores de la compañía. El modelo puede calificarse como de ‘caja translúcida’, pues es transparente respecto al efecto de las variables introducidas sobre los retornos de la inversión, al mismo tiempo que es capaz de recoger la incertidumbre y el largo plazo de los proyectos de I+D. El modelo está desarrollado para proyectos de los sectores de la energía y medioambiente, pero es aplicable para todo aquel sector que necesita de inversiones relevantes en los proyectos de I+D, el denominado CAPEX, y cuyos retornos esperados sean a medio-largo plazo.

Un artículo derivado de este capítulo ha sido publicado en la revista Renewable and Sustainable Energy Reviews, vol 122, 109726 que aparece en el número de Abril de 2020 con el título A research and technology valuation model for decision analysis in the environmental and renewable energy sectors. Esta revista está publicada por Elsevier y tiene un IF (impact factor) de 10,556 estando ubicada en el primer decil de las revistas del ámbito de las Energías.

El tercer capítulo de la tesis recoge, sobre el ejemplo concreto del desarrollo del bioetanol lignocelulósico, lo que supone la gestión integral de los proyectos de I+D.

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Esta gestión holística de la I+D e innovación comprende el análisis de diferentes aspectos y variables (Chen et al., 2018). En primer lugar, la estimación de la demanda global del producto en desarrollo, el llamado etanol de segunda generación, aquel producido a partir de residuos agrícolas. Se estudia la situación de desarrollo del mercado, donde compiten las principales empresas de un producto que aún no está en comercialización estable en ninguna parte del mundo. Como parte del análisis del mercado, se profundiza en la posible gama de precios y en la legislación aplicable al producto. Los aspectos regulatorios son de vital importancia en este caso, ya que la industria reconoce que, sin el apoyo de la Administración en un primer desarrollo de la tecnología, no será posible tener un futuro producto comercial competitivo con el recurso fósil.

A partir de este análisis de la demanda y el mercado, en segundo lugar, se revisa el estado del arte de la tecnología, considerando los cuellos de botellas principales que esta debe afrontar y las estimaciones hasta donde llegará el desarrollo. Esta profunda revisión hace posible un estudio detallado del desglose de costes de una planta tipo y proponer vías de mejora.

Por último, este estudio de caso de un proyecto concreto de I+D muestra el resultado final de disponer de una estrategia de I+D, con unas herramientas que permitan analizar y focalizar la investigación, llevando a cabo un análisis detallado del proyecto, no solo desde el punto de vista de la tecnología, sino del mercado al que se dirige.

Este capítulo ha dado lugar al artículo Biofuels 2020: Biorefineries based on lignocellulosic materials, que fue publicado en 2016 en la revista Microb Biotechnol (Sep; 9(5): 585-94). Esta revista está publicada por Wiley y tiene un IF (impact factor) de 4,86 estando ubicada en el primer cuartil de las revistas del ámbito de Biotecnología y Microbiología aplicada. El articulo desde su aparición ha sido citado en 93 ocasiones según Google Scholar el día 22 de marzo de 2020.

La tesis finaliza con un capítulo de conclusiones, en el que se indican los principales hallazgos y contribuciones del estudio, tanto desde un punto de vista teórico como práctico; señalando igualmente las limitaciones que tiene la investigación y las futuras líneas de estudio.

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Introducción

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Chapter 1: When industrial companies become technology-driven: Abengoa case study.

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Abstract

Companies are forced to review their strategies maintained its competency in the market. There are different paths to follow in order to be reinvented, one of them and most often is bet for R&D and innovation. In this paper it is presented a real case in which an industrial company shift its strategy to become a technology-driven company. It is analyzed the changes made in company strategy, organizational structure, and management and governance. The roles made by the R&D and innovation leaders within the organization, the R&D structure and the financial ratios evolution are also analyzed with the longitudinal analysis of the case study methodology.

The aim of this study is to serve as guidance and inspiration for other companied that are involved or are considering to be involved in this kind of strategy changes.

Keywords

Strategy shift, Longitudinal Case Study, Innovation, Structure Organization.

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1. Introduction

Technological innovation has become critical for companies to be competitive. This trend has forced some industrial companies to augment their focus on research and development into new technologies and approaches with the ultimate aim of enhancing market presence. When this happens, the company’s structure and organization must get adapted. What’s more, the culture of the company must also adjust to this new reality. When a company makes this change all activities from production to development to commercialization and market analysis are affected. Consequently, this transformation involves a strategic change in the company with simultaneous and consecutive changes in different elements and aspects of the organization (Romanelli and Tushman, 1994).

Several companies have been forced to do this shift, however, just a few of them have done a radical transformation, via a firm focus on R&D and technological innovation coming from a traditional industrial base. Although, some studies have been completed regarding these changes (Otley and Beery, 1994; Chiesa et al., 1996; Zhang et al., 2007), currently the process, its consequences, and the effects on the organizations are not deeply understood. This emphasizes the importance of this study as more and more companies will be forced to make disruptive changes enabled by technology and innovation.

The main goal of this paper is to explain the change process that a company with an industrial background suffers through when it becomes a technology intensive company. We will focus on analyzing the impact on different areas of the company, the pro and cons of the decision, and what the main obstacles are during the transition to a new organization.

This study does not have a standard character, which would establish recommendations and lines of action, but instead aims to describe the change experienced by a company so as to serve as guidance and inspiration for other companies that follow. Essentially providing a case-study for those thinking of undergoing similar transformations.

For this objective, we have used Abengoa, which is a company based in Seville (Spain) that by 2014 had become a world leader in thermosolar energy, and one of the top three companies in second-generation biofuels and a leader in the construction of energy transmission and distribution infrastructure. To reach this leadership position and to maintain its competitiveness, Abengoa created an innovation ecosystem through strong R&D investment. The company increased the R&D budget from 23.3M Euros in 2004 (Abengoa, 2005) to 597.7M Euros in 2014 (Abengoa, 2014). The transition from an industrial company, to a technologically focused company, involved a series of key events that will be analyzed in this study: creation of R&D departments within all of its Business Units; a top corporate management position for a Chief Technology Officer; and centralization of all R&D activities by bringing them together under the Abengoa Research brand. Thus, thanks to its clear commitment to R&D, by 2015 the company had a portfolio that included 312 filed patents and close to 100 full-time PhD scientists. This placed Abengoa ahead of its peers—in 2012, the total patents that it had filed outnumbered those of any other private company in Spain. In addition, between 2007 and 2015, it was the sixth Spanish company in terms of funds captured from European programs to match R&D activities.

This study contributes to the current literature on organizational strategic change; with an emphasis on getting deeper knowledge about a particular case. In addition, it examines

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R&D and technology management, pointing out the major decisions that a firm takes in the change process, and their consequences. From a management practice point of view, Abengoa’s transformation constitutes a change model that covers some of the recommendations that have been pointed in the previous literature, allowing us to analyze and the results when they are applied to a real case.

Below, we provide an overview of the theoretical models that exist to describe the R&D management within a company (Section 2), a description of the methodology (Section 3), a detailed case-study of how Abengoa made the transition (Section 4) and the challenges that were faced by Abengoa during this transition (Section 5).

2. Theoretical Background

Current market conditions and trends are continuously changing and are forcing modern companies to adapt their structure and internal organization accordingly to maintain their competitiveness in the market (Halkos and Bousinakis, 2012). This is forcing companies, from all sectors to focus on R&D and innovation. Becoming an innovative company can be done in different intensive stages, i.e., investing a higher amount of resources, allocating specific personnel to perform R&D and innovation tasks, or through a disruptive change transforming the company from an industrial to an innovation and technology-based company (Cohen and Klepper, 1992).

The Punctuated Equilibrium model for organizational change (Tushman and Romanelli, 1985), points out that organizations evolve following two different processes: convergence and reorientation. If the convergence evolution process, it implies incremental changes within the organization, usually during wealthy periods. On the other hand, the reorientation process implies substantial, quick and large-scale changes throughout company activities, including strategy, structure, power distribution and control systems, leading to so-called radical change (Virany et al., 1992) or strategic reorientation (Tushman and Romanelli, 1985; Romanelli and Tushman, 1994). The majority of the literature regarding these changes is based on the assumption that all the defined elements within the organization change simultaneously (Amis et al., 2004), consequently, when the modifications are done sequentially the description is not accurate (Pettigrew, 1992; Van de Ven, 1992; Pettigrew et al., 2001; Van de Ven and Poole, 2005).

Changing from and industrial background to an innovative and technology based company requires a strategic change in that some of the essential elements that describe the company are radically changed (Kodama, 2003), for example, at the corporate level with the appearance of new businesses (Hoskisson and Johnson, 1992), or on the competitive side as new competitive sectors or markets require allocation of resources to be altered (Whittington et al., 2017). These strategic changes are followed by simultaneous or successive changes of basic areas in the company. Therefore, this transformation can be studied as a strategic change process; specifically, as a change that occurs during a period of time which and finally becomes a disruptive change (Amis et al., 2004) Hence, the stages and milestones of the process models can be identified can be used to analyze this kind of evolution (Van de Ven and Poole, 2005).

A R&D and technology-based firm has several characteristics, the most common is the large amount of resources, particularly financial, that it devotes to R&D. From a management point of view (Gassmann and Zedtwitz, 1998; Argyres and Silverman, 2004; Zhang et al.,

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2007; Arora et al., 2011), there are two basic aspects that are relevant in this kind of company: a top manager devoted to managing technology and R&D, usually termed the Chief Technology Officer (CTO), and how the R&D is organized (centralized or decentralized). Both elements, are key to how a company manages innovation.

Most articles published in this area agree that the effective management of R&D is crucial in order to obtain positive returns from initial investments (Wolff, 2007). Within R&D organizations, the CTO position is vital and plays a pivotal part in impacting the success of R&D efforts and the quality of the products created (Borden and Weig, 2018). Typically, companies with strong CTOs get better returns for their R&D investments (Cetindamar and Pala, 2011; Medcof and Lee, 2017). The CTO’s role and responsibility in determining how R&D is structured and functions within a company has been well-described in a wide range of articles and books. Much of this literature agrees with the idea that CTOs must be part of the highest decision making committees when R&D comprises a key part of the company’s commercial strategy (Smith, 2003; Cetindamar and Pala, 2011; Medcof and Lee, 2017).

To ensure the performance of R&D, Cetindamar and Pala (2011) proposed the following roles for the CTO: 1) the coordination of R&D and technical efforts between business units and corporate research to maximize efficiencies and synergies; 2) to represent technology development at the highest levels of management; 3) to monitor technological advances; 4) to supervise R&D laboratories and technology development units; 5) to assess the technological aspects of major strategic initiatives; 6) to manage relationships between technological actors and external partners (i.e., universities and other companies); and 7) to participate in marketing and media relations. Similar roles are also described by Medcof and Lee (2017).

Regarding the R&D organization, although a wide variety of different R&D structures and methodologies exist (Bagno et al., 2017), in this paper we focus on highlighting the differences between a centralized and decentralized R&D structure. The literature describes different consequences of a centralized versus a decentralized R&D organization. Arora et al. (2011) suggested that centralized R&D leads to a more scientific approach for companies with a low level of business diversification and in those that normally focus on organic growth with complex technologies. On the other hand, a decentralized R&D is found in companies that, although having greater market value, invest less in R&D and consequently generate less patents than centralized R&D organizations. Centralized R&D attracts higher caliber academic scientists; however, this can also represent a risk if these scientists are more focused on their own research interests rather than on corporate objectives. Another problem associated with centralized R&D is the challenge of gaining the trust of managers from other units within the company (Nobel and Birkinshaw, 1998). This is because R&D is out of their direct control and future business competitiveness depends on research deliverables, yet to be developed by the scientists.

The choice between centralized and decentralized R&D depends on what the company is looking for. A decentralized R&D program is often found in companies that are more focused on products that have near term impact, whereas centralized R&D programs exist within organizations that are focused on long-term projects that require more basic research, with larger return rates (Gassmann and Zedtwitz, 1999). Shifting from a decentralized to a centralized R&D organization, or vice versa, requires broad and major changes to the structure of the organization. This can greatly impact how employees view

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the organization and may push them out of their ‘comfort zone’ (Bagno et al., 2017). Employees’ emotions are affected during reorganizational procedures due to the uncertainty generated, so it is crucial to inform them about the process in order to reduce anxiety, which will help to increase their efficiency during the process (Halkos and Bousinakis, 2012). The management of this restructuring is particularly important because, if effective, it will help to retain the most talented people (Chiesa et al., 1996).

Transforming an industrial company into an intensive technology and innovation firm requires a strategic shift with significant changes in the overall company strategy, the organizational structure, its governance and control system (Virany et al., 1992). Although, these changes are made simultaneously, they usually follow some relevant milestones (Van de Ven and Poole, 2005). In this paper, we analyze the change process followed by a company, with special focus on the changes in strategy, organizational structure, and management and governance required when the company decided to become an intensive technology and innovation firm.

3. Methodology

We have chosen to use the case study methodology, as it is the best way to describe and analyze the changes made in the organization. Two of the authors of this study were actively involved in the organizational change at Abengoa, which provides the opportunity to provide an insider’s view of the complexities of the changes and challenges faced by the company. This in-depth view—one that is typically not accessible to academic researchers—adds a wealth of qualitative data to complement quantitative analyses and also aids in assembling a complete description of the events (Yin, 1994; Zainal, 2007). Possible none neutral interpretation that the participant-authors may have, has been overcome with support of the information obtained from the company, both primary and secondary, and by revisions from the other two authors that do not have any previous relationship with the organization under study.

The case study methodology that has been chosen aims to explore the effects of real decisions on the organization, while taking advantage of a detailed analysis of how conditions and variables are related (Zainal, 2007). In this way, the methodology enables the authors to better explore the “how” and “why” of each event and to better comprehend the social context in which the decisions are made (Cooper and Morgan, 2008). This type of study is therefore highly suited to research into phenomena that cannot easily be measured from a quantitative point of view (Yin, 1993).

Because strategic decisions are not made in isolation, they need to be placed in the proper context. Strategic decisions are influenced by historic, economic, social and organizational factors—factors that must be carefully considered in the analysis. By enabling the complexity of real-life situations to be considered, the case study methodology adds significant value (Zainal, 2007; Baxter and Jack, 2008; Cooper and Morgan, 2008; Meer-Kooistra and Scapens, 2008). The case study methodology has been widely used in literature to analyze and describe the consequences of real-life decisions. Although a number of other articles in the literature have described similar industry shifts towards technological innovation (Appelbaum et al., 2017; Holtbrügge, D., Schuster, T., & Wilbs, 2017) and the impact that R&D has on a company (Marafon et al., 2015; Salimi and Rezaei, 2018), the current study

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brings a new and more detailed focus on the underlying R&D organizational changes that are necessary to drive these changes.

We have chosen to conduct a longitudinal analysis of the case study, because this approach provides a more complete view of outcomes and trends over time. As described by (Zainal, 2007), longitudinal analysis “…provides a systematic way of observing the events, collecting data, analyzing information, and reporting the results over a long period of time”. For our study, the longitudinal case is focused on understanding the change process that a company has suffered during its transformation from an industrial firm to a technology intensive one.

The literature usually uses two different definitions for change: (i) measurable differences over time in a company or corporation in selected dimensions; and (ii) a description of the sequence of events on how developments and changes unfold (Van de Ven and Poole, 2005). The second definition is frequently associated with an explanation of the temporal order and sequence, in which change events occur (Pettigrew, 1990; 1997; Poole et al., 2000; Van de Ven, 1992). It is well known that the latter approach is best to describe how firms develop and change over time (Pettigrew et al., 2001). From this point of view, and within an input-process-output model, events represent changes in the variables. These in turn constitute stages in the process. Thus as a process unfolds, its sequence of events, inherent causes and consequences can be analyzed and the previous ‘black box’ is disclosed between the antecedents and the results of the change (Van de Ven and Huber, 1990). This analysis calls for longitudinal research in which files, documents, and reports are used to illustrate the company’s objectives, as well as the visible results of the changes implemented.

The data we present below were compiled from company reports, financial statements, strategy plans and interviews with key people, including the corporate CTO, General Manager of R&D and a number of R&D area leaders. The interviews enabled the authors to consider multiple points of view, and provided an unprecedented insight into the various responsibilities, decisions and underlying drivers for change.

4. Abengoa’s transition from an industrial to a technology-driven company

This section describes the different steps taken by Abengoa to become a technology-driven company. Here we describe in detail the different steps and phases that the company has passed through to be convert into a technological company.

The company was formed in 1941, in Seville, a city in the South of Spain. The company was founded to manufacture a five-ampere mono-phase meter, which interestingly was never done. After this first product attempt, the company became focused on undertaking electrical assembly projects. During its first stage, the company expanded at the national level, and it was during the 1960’s when the company started its first projects in Latin America and its expansion continued to other locations worldwide. Abengoa started the 1990s with a total revenue of 314 million euros which almost tripled in a decade, reaching 865 million.

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The dramatic increase made in R&D investments between 2005 and 2014 are shown in Figure 1. Funding rates jumped from 23.3M Euros in R&D in 2004 to nearly 600M Euros in 2014.

Figure 1 Abengoa R&D investment from 2005 to 20141 Adapted from Abengoa Annual Report 2014.

This increase in R&D is based on a strategy shift that is shown in Figure 2 and explained in detail in this section.

Figure 2 Diagram representing the main period of Abengoa R&D transformation

4.1 Changes process. The beginnings of R&D: 2001-2003

Abengoa started the new century with solid revenues, coming, mainly, from assembly and installation in the industrial, energy and telecommunications sectors, where the added value is limited. As such, it was decided to shift to sectors where Abengoa could add more value to the market. To reach such a position R&D investment is required. In 2001, Abengoa business was divided into four sectors: bioethanol, environmental services, systems and

1 Figure 1: This amount includes the investment in flagship projects: 2005; Solucar Platform, 2006; Solucar Platform, 2013; Hugoton and Khi, 2014; Hugoton, Khi and Atacama.

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networks, and industrial engineering and construction. The 2001 Annual Report highlights Abengoa as a technological company which defined itself as “It is now an industrial and technological company…” Emphasizing the technology as a main driver of its description (Abengoa, 2001). During this period, R&D and technology increased its importance in the company strategy. It started getting higher visibility as it was the first time that R&D investment was reported separately as a financial statement, by this time Abengoa investment in R&D was 17.5 million Euros. Furthermore, at this time, Abengoa was able to obtain a subsidy from the Department of Energy in the USA (DoE) for a total amount of 32.4 million USD for R&D in second generation ethanol (Abengoa, 2003). These were the firsts stages of the gamble that the company was doing on R&D to become more competitive in technology markets.

4.2 Abengoa takes a bet on increased R&D investments: 2004-2009

In 2004, Abengoa was divided into five different units: Solar, Bioenergy, Environmental Services, Information Technologies, and Industrial Engineering and Construction. At that time, the latter covered most of Abengoa’s activities—and made up almost half of the company’s revenues. As mentioned, the construction industry is a conservative sector that mainly relies on mature technologies. Thus, innovation was very limited and mainly comprised the application of lessons learned from one project to another. In Abengoa’s 2005 Annual Report (Abengoa, 2005) a strategy was announced to diversify the business by intensifying its focus on tech-heavy sectors such as solar energy and biofuels (see Figure 3)

Figure 3. Abengoa Business Units in 2005 (Abengoa, 2005). Figure adapted from Abengoa Annual Report.

Within each of the above-mentioned Business Units (Figure 2) were individual R&D departments. The R&D departments sought out financing and oversaw project progress. The director within each Business Unit defined the R&D strategy. Exceptions to this were in the solar and bioenergy units, which both created an independent R&D company inside their units—an organizational change that reflected the greater need for innovation in these areas.

At the corporate level however, R&D lacked representation and activity was simply presented to corporate leaders through financial and administrative summary reports, such as a follow-up activity report that was updated every six months. Thus, there were no top-level management positions to guide research innovation and development strategies.

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In addition, the R&D managers at this time were most often personnel that lacked science backgrounds or previous R&D experience outside of the company. Instead, they typically had experience in engineering and construction. In 2009, the number of PhDs working for Abengoa was less than 15. Despite having a relatively limited number of academic staff, Abengoa took major strides forward by beginning construction of a large thermoelectric solar energy tower with a power capacity of 11 MW in 2005, and a demo facility to produce ethanol from biomass in 2006.

Although most of Abengoa’s strategy was based on the natural growth of the company, agreements were established with third-party companies to buy technology or acquire “know-how”. An example of this strategy was the agreement reached with Dyadic International to develop an in-house Abengoa enzyme cocktail—a technology that is essential for many critical steps in the development of second-generation biofuel technologies (Valdivia et al., 2016).

While Abengoa had much R&D success over this period, some organizational and strategic decisions were made that were not in line with the company’s policies. For example, while R&D activities were well developed in the solar and bioenergy units, the remaining units were lagging behind. As well, each Business Unit had developed its own independent management procedure for project selection and development, and these procedures were at times not fully aligned with corporate strategies. Another weakness was that certain departments and leadership saw R&D departments as more of a cost to the company than an opportunity to create new products and gain market dominance. The culture of the company was standing in the way of progress, and while there was a lot of work to do on this front, it would soon change.

4.3 R&D gains representation at top management levels 2009-2010

A key readout of whether a company places importance on technology and R&D is whether the activity is represented by top-level management boards (Cetindamar and Pala, 2011). Abengoa made a strong commitment in this realm when it created the position of CTO in 2009. The CTO held a seat on the Board of Directors and the company’s strategic planning team. At this time, R&D investments were around 60M Euros and involved over 400 personnel devoted to innovation, technology and R&D.

The CTO received clear instructions from the Board of Directors: i) to ensure that the technological strategy was in line with the business strategy; ii) to harmonize the management structure among the different units, and iii) to assure that the market saw Abengoa as a technological company, participating in international research and innovation forums. These responsibilities are well-aligned with the roles that are expected from the position as described in the literature (Adler and Ferdows, 1990); Smith, 2003; Cetindamar and Pala, 2011).

Under the leadership of the CTO, the different R&D and innovation departments were reorganized. An R&D and innovation department was established in all Business Units—even in those in the more conservative sectors with mature technologies. The CTO was also responsible for converting the R&D units within the most innovative business departments

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(i.e., solar and bioenergy) into independent companies that eventually built a strong presence in Spain and the United States.

The new departments and companies had independent finances, and these were reported directly to the Executive Chairman of Abengoa. At this point, hydrogen and emission reduction companies were created. These Business Units were born with a clear objective: to consolidate themselves as economically viable businesses within a period of 4-5 years.

To further promote innovation in the organization, the Board of Directors established a set of overarching R&D objectives. As stated by Lerner and Wulf (2007) and Honoré et al. (2015), this approach is an effective way to advance R&D in organizations.

Deeper organizational measures were taken by top management to ensure that there was a clear strategy for innovation. The following three key management tools were implemented within all departments:

- Stage-gate methodology: This project management tool, based on the methodology published by Cooper et al. (1990), was integrated into Abengoa’s ‘Internal Mandatory Rules’ (abbreviated NOC in Spanish) such that every company project was required to follow it.

- R&D Key Performance Indicators (KPIs) were defined to measure progress. These included:

o Personnel devoted to R&D&I (research, development and innovation) o PhDs working for the company o Filed Patents o R&D intensity (i.e., investment in R&D divided by total of sales)

The KPIs were presented by the CTO to the Board of Directors each six months. By the end of 2010 the values for these KPIs were (Abengoa, 2010):

o Personnel devoted to R&D&I: 441 o PhDs working for the company: 22 o Filed Patents:113 o R&D Intensity: 1.7%

- R&D valuation: a techno-economic analysis tool was developed in-house to assess the potential return of every R&D project. This tool was implemented to ensure that all projects had a clear market target. Furthermore, it measured whether the resources allocated to each project were being used effectively to reach objectives.

Thanks to the company’s investment in innovation, it began to gain a technological edge. For example, Abengoa’s second commercial solar tower power plant, which was completed in 2010, was the biggest tower in the world with a production capacity of 20 MW. This tower was built next to Abengoa’s 10 MW solar tower—combined; these towers comprised the largest solar complex in the world.

Also, in 2010 Abengoa began its first international project: the construction of a second-generation biofuel plant located in Hugoton, Kansas (United States). Although this project was built on experience gained from the construction of an existing 5 ML facility in Salamanca (Spain), the technological challenge was enormous, particularly because it required scaling up to almost 95 ML. The success of this project was another clear

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demonstration of the competitive advantage gained by investing in innovation and technology.

By the end of this period, investments in R&D were 92.6M Euros—almost double the amount made in 2005. Financially it was a big challenge for Abengoa to support this level of investment, although around one quarter of these investments were paid for through European R&D grant and project funds.

4.4 The creation of Abengoa Research 2011-2012

An important milestone in the transformation of the company was the creation of Abengoa Research (AR). AR was born to lead and manage basic research and complex technical problems. Since its inception, AR was led by a renowned university professor, who was in charge of establishing the organizational structure and strategic goals of AR, which had two main objectives:

- Identify breakthroughs and new technologies with market potential that may provide competitive advantage.

- Solve complex technical problems that require deeper scientific investigation.

AR was organized into various scientific knowledge areas and each area was led by one senior scientist with a strong background in academic science. With the creation of AR, the company took one step further into the realm of basic research. The number of PhDs in the company reflected this shift; before the creation of AR there were 22 PhDs and by the end of 2013 the number had increased to 67.

Another aim of AR was to consolidate and improve the company’s patent portfolio. For this, a Patent Office was created, which took over existing patent resources from the Business Units. Up until this time, the Business Units had done an excellent job of capturing intellectual property (IP), having applied for over 100 patents. The mission of the new Patent Office was to optimize the IP strategy for all of Abengoa’s technologies and to improve the quality of the patents, rather than increasing the total number.

AR, as a new entity, had to define its relationship with the other Business Units. AR was created to become a sort of technological research center within the company—one that could provide key technological know-how. The collaboration framework between AR and other Business Units is illustrated in Figure 4:

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Figure 4. Abengoa R&D and Innovation. This structural scheme of Abengoa Research and its relationship with the Business units is adapted from the 2013 Abengoa Annual Report.

AR focused on foundational research, while the Business Units focused on the final development steps. For this arrangement to work, both needed to have tightly aligned aims and well-defined mechanisms for knowledge transfer.

One hurdle was that AR staff—who had backgrounds in academia—brought with them a culture that was very different from that of other Abengoa R&D employees. Although AR had a budget to spend on developing future technologies and breakthroughs that would benefit Abengoa in the medium-long term (~10 years), all other AR projects were financed by the Business Units. This scheme was established to ensure that AR activities were tightly aligned with company needs. Despite the expected friction between AR and the Business Units, Abengoa’s top management had a strong vision and a plan for AR, which ensured that its integration was supported, and this helped to ensure that the initiative moved forward.

During this time, Abengoa continued to use its established management tools for all R&D projects and continued to create new Business Units in sectors where technology played a critical role. This time was also marked by Abengoa’s establishment of two new initiatives: i) Seapower, to develop marine and tidal energy, and ii) Energy Crops, to develop sustainable energy from agriculture. Furthermore, Abengoa was also concentrating its business in two main sectors, energy and environment, and reducing investment in those that were no longer considered core business, such as information technology and valorization of residues—businesses that were sold.

During this time of expansion, Abengoa initiated a number of projects, such as the construction of solar power plants in Abu Dhabi l; the Waste to Biofuel project, which aimed to convert municipal solid wastes into biofuel; and a project that used molten salts to store solar energy.

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4.5 The centralization of R&D 2013-2015

In 2013, the company started to shift its strategy and overall organizational structure. Abengoa switched from a model in which the different Business Units acted as independent organizations with common corporate management, to one in which all units were integrated into one unified organization. Within this new structure, products progressed from R&D to the final product through different Abengoa branches within the company.

AR was made a big part of this reorganization. The Abengoa Board of Directors and Chairman made AR’s responsibility to develop technologies from early development stages all the way to a market-ready product. Thus, between 2013 and 2014, all personnel involved in R&D were moved under the AR umbrella.

To support this development, AR changed its organization by specializing into teams that were aligned with specific Abengoa business areas: bioenergy, concentrated solar energy, photovoltaics, power systems, chemical processes and complementary areas of engineering services. These areas were each led by a Technology Area Director, who had a degree of independence from the AR general manager but worked with him to define targets. A key issue at this point was that AR failed to seek out new leaders with the expertise required to fill these new positions; rather, the existing scientific and business personnel were folded into this new structure. This saw AR, which was based at Abengoa headquarters in Seville, grow from 66 personnel in 2013 to more than 400 by the end of 2014.

In this new role, AR was in charge of maintaining and reinforcing technology under development and defining a plan for the next ten years of Abengoa R&D. In order to achieve these aims, AR faced a number of sizable risks and challenges:

- Collaboration: the need for Business Units to recognize and work with AR as a key player in technology development

- Commercialization: the need to maintain the commercial focus of the research, while at the same time identifying disruptive ideas

- Work Culture: To consolidate and foster cohesion between the scientific and Business Unit personnel so that they share the same objective

In order to face these challenges, steering meetings were held between AR leaders and Business Unit leaders at different levels. At the upper most level was the Technological Strategy Meeting, a monthly meeting between top level AR and Business Unit personnel. These personnel included the AR General Manager, the Technology and Area Directors, the Business Unit General Manager and the Technology Development leaders. The objectives of this meeting were to i) define the core R&D strategy; ii) review and assess the value of technologies; and iii) approve new R&D projects and cancel existing projects that were deemed unnecessary.

In addition, weekly Technology Management Meetings were held between AR and Business Units to monitor project progress. These meetings were attended by the Technology Area Director, the Project leads and managers in AR and the Business Units. The general objectives of the meetings were as follows:

- To assess project progress based on Stage-Gate methodology (budget and schedule)

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- To solve issues relating to technologies and research

- To define the market potential of the projects and related technologies

- To analyze technological and market risks

- To identify alternative strategies

- To define and propose new projects

Further to this organizational model, management tools were also updated. As part of this, the Technology and R&D Competitiveness Value Assessment (TechValue) tool was developed and launched. TechValue served to measure the direct impact of the R&D on costs. Complementary to R&D valuation, TechValue measured the cost reductions on commercial products that directly resulted from R&D. The main goal of this tool was not to evaluate the viability of technology or its market value, but to measure the competitiveness of the technology in the market. It also enabled the company to determine whether the competitiveness of a product could be improved through R&D or innovation, or whether it was already a mature technology. TechValue results enabled Abengoa to decide if a product had matured enough for R&D activities to be halted so that design and engineering improvements could be made. The R&D value, KPI’s and Stage-Gate methodology also continued to be part of the management tool-kit.

Regarding the R&D strategy and project management, Abengoa was organized according to the Horizons defined by McKinsey&Company (Baghai, M., Coley, S., & White, 2000). In the case of AR, these horizons were adapted to its activity: H1 were very advanced projects with first commercial actions already performed; H2 represented mature projects that required further research and/or development before stepping up to H1; H3 were exploratory projects that were aligned with Abengoa’s general interests.

As previously mentioned, AR projects were financed by the Business Units in full, except for the subsidies obtained by AR. Therefore, AR needed to work closely with Business Units to define project scope and budget. Thanks to this requirement, continuous communication between AR and Business Units was in place. Regarding IP, because the Business Units financed technology development and led commercialization, they also filed patents.

As the importance of technology activities grew, in October 2014 the Board of Directors created a Technology and Strategy Commission, formed by 2 independent directors and 1 director that represented the original bunch of investors, with some, but not a vast, experience in technology and R&D. This commission had the following objectives: i) to analyze and evaluate the potential development of marketable technology; ii) to oversee R&D strategy; and iii) to supervise the main R&D activities, such as the patent portfolio and the implementation of AR within the company.

Under these leadership and management conditions, Abengoa was forced to make difficult decisions; for example, its Seapower technology projects were frozen due to unfavourable market conditions. This type of measure was understood and supported as part of the company’s dual focus on technology development and sound business decisions. R&D figures by the end of 2014 demonstrated that Abengoa’s R&D success continued: it had filed over 312 patents, employed 92 full time PhDs, and its investment in R&D reached nearly $600M USD.

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4.6 Changes in Governance and Control System

Above we have described the most significant events and stages during Abengoa’s transformation from an industrial company to a technology and innovation company. The description has been mainly focused on the strategy and organizational changes of the firm. Nevertheless, the changes should have also been in other aspects of the organization, such as the governance, and the control systems (Virany et al., 1992; Romanelli and Tushman, 1994). Regarding the governance, we briefly analyze the Board of Directors, its composition and the capital structure during the period of change. For the control system, our analysis will focus on the financial ratios used to measure the company performance and its solvency.

Power Distribution/Governance

Capital structure

The Company became public in 1996, but since then the family owners have always retained more than 50% of the shares, in fact by 2003, they retained more than 60% of the total shares and until 2014 they were never below 54% of the political rights of the company. Hence, there was not a significant change in the company shareholders, with the political control remaining under the traditional shareholders.

Board of Directors and Management

In 2001, Abengoa Board of Directors (BoD) was formed by 4 persons, all of them belonging to the original founders’ family, it meant that none of them were independent. It was in 2003 when the number BoD members increased to 7, to include three independent members on the BoD, with relevant expertise in management and economics. The representation of independent directors increased in the years 2005 and 2006 to reach 56%. However, this percentage decreased in 2007, when the number of BoD members increased to 13, with only one of the new additions being an independent member; meaning only 31% were now independent members. The new directors were all external and represented the different original share-holder families in the BoD. In 2008 the number of BoD increased again to with the addition of one more executive and one more independent. However, throughout this period none of the Independent External Directors had a background in the field of science and technology or innovation.

The next relevant change in the BoD and the Executive Management of the Company, was the appointment of a Chief Executive Officer (CEO) who was different to the Executive Chairman, this appointment occurred on 2010. It is significant as it was the first time in the history of the company that the Chairman put part of his responsibilities on the CEO. The BoD structure was stably maintained until 2014, which is the end of the analysis period of this study.

To increase the international presence of the company and to have better visibility of the worldwide political, economic and technology situation, Abengoa created in June 2010 an

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International Advisory Board formed by international prestigious professionals to advise the company on its international strategy. The profiles of the different members of this Advisory Board were from different fields, economists, managers, and also scientists. It should be pointed out that the role of this board was just advisory and they had no legal responsibility.

On the topic of Executive Management, Abengoa had a Steering Committee formed by the top managers of the company, which were: Chairman, CEO, head of corporate departments and head of the Business Units. This committee managed the daily organization of the company and any instruction made by the BoD, in short, it was the first operative committee completely formed by executives.

Control System-Financial ratios

In addition to the above description of the BoD evolution, the control system of the company, should have shown the strategic change of the company. Here we analyze the control system through profitability and solvency ratios.

Beforehand, it must be clarified that data used to calculate the ratios are based on the company’s annual accounts. Also, it has to be noted that the accounting regulation rules modification (2008), from which all European public companies shall follow the International Financial Reporting Standards (IFRS); this implies that the accounts previous to 2008 were done following different rules. In addition, the group of firms, that formed Abengoa Group, under study have a complex consolidation that also changes each year of our time horizon.

In Figure 5 below we show (i) Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA), which measure the profitability of the company without considering financial and tax aspects; (ii) Return on Assets (ROA), which measure the capacity of the company assets to generate incomes; and (iii) Return on Equity (ROE), which measures the ability of the company to give returns to its shareholders.

Figure 5 Evolution in time of different Financial ratios. Source Abengoa Annual Reports

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EBITDA (M€) ROE ROA

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EBITDA

The company’s EBITDA consistently increased every year at high ratios during the 2004 to 2014 period. It should be noted an increase of around 10% with respect to the EBDITA of the previous year, except for 2014 in which the increase was just 3%. In the period 2004 to 2014 EBITDA grew at a compound annual rate of 22.8%. This increase demonstrates the company growth based on the new technology business generated thanks the new strategy.

The EBITDA increase comes from the growth of assets, both tangibles and intangibles, which includes the asset called “investment in projects”. Under the view of the R&D and innovation strategy, it can be said that part of the EBITDA increase came from those intangibles assets that are achieved thanks to the investment in R&D, mainly the income coming from patents and technology fees. It must be noted, that the accounting of these projects is subject to IAS 12, which allows the activation of expenses and the counterpart as incomes; the application of this accounting standard allowed the company to obtain certain incomes.

Return on Assets (ROA) and Return on Equity (ROE)

Both profitability ratios have had complete divergent performance. In the case of the ROA, which measure the assets profitability through the ratio EBIT (earnings before interests and taxes) and the total assets, the evolution of this ratio does not show the impact of the new strategy. As is shown in Figure 5 EBITDA increase was not translated into a sustained increase in the ROA, probably due to the long return period of the executed projects.

Regarding ROE, in the period 2004 to 2010, the ROE was maintained in the range of 20% to 30% depending on the year; these are high values compared with its peers. From 2010 to 2014 the ROE suffers a large deterioration, decreasing more than 70% in three years. The progress in the transformation towards a technology-intensive company was made with a loss of profitability for shareholders, motivated by the decrease in the net income.

In addition to the performance financial ratios, it is critical to focus on the solvency ratios, to analyze if the strategy shift influenced the company’s solvency.

Table 1 Historical evolution of financial ratios 2004-2014

In Table 1, some common solvency ratios are calculated. Short-term solvency was measured with the traditional ratio CA/CL which relates current assets to current liabilities. It shows the company’s capacity to cover its immediate liabilities. It’s a short-term solvency measure.

Long-term solvency was measured with two indicators, the relationship between total assets and total liabilities (TA/TL), which indicates the company's ability to be able to pay its debts, basically the tendency that the company may incur bankruptcy. The other ratio is the relationship between cash flow (profits + amortization) and the total liabilities (CF/TL) which indicates the ability to pay its debts with the generated funds.

Ratios 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

CA/CL 1.03 0.93 0.94 1.2 1.0 0.83 1 1 0.83 0.84 0.88

TA/TL 1.2 1.2 1.1 0.96 1.13 1.1 1.1 1.09 1.09 1.09 1.1

CF/TL 0.036 0.04 0.05 0.03 0.039 0.046 0.034 0.07 0.07 0.069 0.065

TL/NL 6.44 6.66 11.8 9.13 13.4 9.56 9.41 9.86 10.21 10.17 8.5

EBIT/FE 2.07 1.85 0.7 1.73 1.04 2.33 1.49 1.19 1.42 1.44 1.33

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The indebtedness was measured by the TL/NL ratio, which measures the relationship between indebtedness and net worth, it has also been calculated by the EBIT/FE ratio which relates the profit before interest and taxes and financial expenses, that is what remains once the financial expenses are covered.

By analyzing the solvency ratios, we can see that both long and short-term ratios are maintained during the period. Also notable are the fairly low levels and deterioration in the last few financial years, and the high dependence on external funding that the company had.

The company had significantly high debt levels compared to its net worth. These high debt levels mean high solvency risks. The financial leverage is so high, that the company presents an insolvency risk for the shareholder. This situation led to the coverage of the interest payments being at excessively low levels, since with its EBIT, it could barely cover its annual interest debt —all of which jeopardizes the profitability of the company.

5. Discussion and conclusions

In the previous section, we analyzed the case study of Abengoa, whose commercial strategy was based on differential technology-based products and new in-house developments. With the aim of placing technology at the heart of its commercial policy, the company decided to rearrange its decentralized R&D departments in different business units to a centralized R&D structure. Under this centralized structure all the necessary resources and projects were placed under the leadership of a single General Manager. The reorganization required the reshuffling of around 400 personnel positions, which coincided with the creation of Abengoa Research. This new scheme also involved the recruitment of scientists from different disciplines to the company, some of which were hired as Directors of the new research areas, and others as junior or senior scientists. This new organizational structure was focused on existing employees and new hires, although leadership concentrated their energies on the new comers. In this section we will analyze the pros, cons, difficulties and opportunities that arose during the reorganization.

The company become one of the main private Spanish companies in the generation of patents filed per year, this was thanks to the incorporation of experienced researchers, coming from academia, which gave the company a deeper scientific and technical knowledge.

The company’s transformation stemmed from a strategic decision made by the Chairman, CEO, BoD and Shareholders, and led to deep changes in different aspects of the firm, mainly in the organizational field, which culminated in radical strategic change (Romanelli and Tushman, 1994; Kodama, 2003). The change took several years, during which old and new elements of the company were running simultaneously, with the obvious difficulties and challenges that this situation implied. If the company had been a young company or already an intensive technology firm approaching new sectors or markets, the process described would have been different. During the change process it 3 issues must be highlighted which are relate to the defined strategic change (Virany et al., 1992): (1) resistance to change and

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cultural change; (2) the financing of technology intensive activities; and (3) corporate governance.

Firstly, the change involved at least five different Business Units, all with different degrees of involvement in R&D and with very different areas of focus. This somewhat slowed down the assembly of AR as a unit and led to a number of inevitable operational consequences, with strong resistance to cultural and organizational changes, which were observed both at the management and the employee level.

Resistance from the General Managers was one of the main hurdles to be overcome. Although, the reorganization process began with clear instruction from the Chairman, on behalf of the Board of Directors, under the new organizational structure, the General Managers (GMs) of the Business Units felt that they were losing control over a critical part of the business, including future strategies and decision making. As a protective reaction, Business Units attempted to minimize the personnel that were moved to AR, leaving personnel in the Business Units that were tasked with close supervision of research developments at AR. This ultimately led to conflicts because there were unnecessary overlaps and a lack of clarity regarding duties and roles.

On the other hand, for the GMs, the new responsibility in research was given to new leaders which were new comers to the company, in fact, most of them moved from universities and research centers. The different backgrounds between initial company employees and new researchers caused a culture shock between how the projects and the organization needed to be managed. Unfortunately, the original employees were not acquainted with most of the new research directors—their views, working strategies and targets—and as such, there was some initial turmoil in AR. In fact, this situation required the rotation of a number of researches at the launch of AR because they did not adapt to the company requirements. Hence, uncertainty was created among employees, mainly due to the division between the corporate management and the Business Units and contradictory messages, mainly informal, that were received from different sources. This uncertainty generated doubts and resistance, although this was partially overcome by training plans and career path initiatives as strong incentives for many employees, who appreciated the opportunities available from the transformation that was taking place.

How AR projects were financed was another con that the new strategy had. In addition to the loss of control, the GMs felt that the new organizational structure, which provided AR with more control over technological developments, represented a financial risk because it could allow R&D costs to outstrip potential benefits to the business.

AR was financed by each Business Unit. This meant that projects were selected, and resources were allocated by consensus between the Business Units and General Director of AR. Under this scheme, approximately 90% of budgets were devoted to collaborative ideas under H1, H2, while H3 projects, the remaining 10% of funds were committed to new, breakthrough ideas. It was only in H3 projects, that AR acted autonomously in deciding the line of investigation. For the rest of the projects, Business Units and AR had to meet a consensuses approach, for the scope, budget and objectives.

This financing scheme had several consequences. On the one hand, Business Units focused on funding research that was expected to provide market returns. Because Business Units were in charge of selling the new products and technologies, they were very favorably positioned to define market needs. This meant that AR’s independence was fundamentally

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tied to finding the most suitable approaches to fulfill the market’s needs in the shortest period of time. At the end of the day, this scheme was devised to align the company’s R&D efforts towards commercialization, without compromising the link between exploration and exploitation (Raisch et al., 2009). This framework was not free from interference; heated discussion between the management of AR and the Business Units was common and resolution often required the direct action of the CTO or the CEO. The focus of the Business Units on the most immediate R&D products and market prospects could result in the company missing important breakthroughs and alternative opportunities, while the vision of AR on basic research could end up having an extensive long-term project portfolio that would be highly costly and risky for the company.

Although this method of financing AR projects, gave control of R&D projects to the Business Units, it also obliged them to look for funds to finance the projects, which required them to look for external finance, increasing the total company debt. One of the measures taken to assist in the financing was to have AR look for EU and national and regional subsidies through competitive projects, the vision of the Research Area Directors led Abengoa to be one of the most successful Spanish companies in respect to receiving competitive grants from the EU. This included some H2020 basic research projects and LED development projects, such as NER-300 aimed at building the first plant to make biofuels from urban wastes. To understand the full extent of AR’s commitment to obtain grants, each Technology Area Director had, as part of their objectives, to request a quarter of their budget in subsidies and to be successful in at least 50% of the requests. Hence, the projects were mainly financed with external debt and subsidies, while for highly risky projects, financing with a company’s own resources is the most recommended (Hall and Lerner, 2010).

Another limitation for AR was that this way to fund the project obliged that the intellectual property remained in the hands of the Business Units. This was because of the fiscal argument that patents, and industrial secrets derived from work by AR were carried out as contracted research. In other words, the value of the investments made by the Business Units remained with them. In the medium term, this was detrimental for AR because it was unable to create its own assets as it did not own the technology to be sold to third parties. AR activities were, as such, completely dependent on in-house business, as it did not have an external market (Johnson et al., 2008).

While AR could also have been strengthened by gaining control of the generated IP, this solution, which was discussed at the AR Board of Directors, did not have the support of the mother company because it was considered that it may enable AR activities to deviate from Abengoa´s strategic interests. This decision, made by the governing bodies appeared to be in contradiction to the transformation process. Intellectual property (IP) is a crucial element of success for a technology and R&D company. During the transformation, Abengoa had remarkable results on this front, increasing not only the number of patents, but also their quality, however, and in spite of numerous attempts, IP management did not have a clear plan and adjust to the overall company strategy.

Another con was that the power distribution and corporate governance did not begin changing at the same time and with the same alignment that the strategy and reorganization did. Capital control was maintained by the traditional shareholders, without addition of new wealthy and long-term investors. The BoD, did experienced changes in its composition, with the addition of independent directors, but the directors representing the

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shareholders were always the majority. This resulted in two main problems, the first one was that to maintain the same capital structure, the company was forced to greatly increase its debt. The financing of new projects and the increased investment in R&D, through external financing with its corresponding financial costs, created increased debt that affected the financial ratios, as shown in ratios analyzed, mainly in the ROE and solvency. The second problem was that the BoD was not aligned and adapted to the strategic shift that the company was undergoing. The BoD composition and member profiles remained similar throughout the transformation. Although there were independent directors with proven track records and experience, the expertise in innovation and technology was limited. In addition, the BoD still maintained several inside directors with a clear bias toward the company’s traditional business. A more usual tack would have been to adapt the BoD to the strategic and organizational shift that the company had embarked upon—adding new BoD members with technological and innovation profiles.

In conclusion, the transformation from an industrial company to a technology and innovation intensive firm requires radical change which significantly affects the company’s aspect. The strategic decision to develop new activities with breakthrough technology should be accompanied by changes in the organization, including decisions and changes in the R&D structure that the company will use—centralized vs decentralized—and the top-management. The implementation of organizational and strategic change also involves numerous alterations in broad aspects of the company, most important of which are: personnel, financial resources and organizational change (i.e., management system, operational structure and company culture).

In Abengoa’s case, although some hurdles and problems needed to be addressed, the conversion of Abengoa Research into a centralized R&D company enabled it to attract valuable research talent, which under other circumstances would have been impossible. In a very short time, Abengoa Research became a key player in the field of environmental and energy research at the national and international level. Abengoa Research became an R&D hub for renewable energy and environmental sciences—one that had no peer in Spain and few equals among other European companies. However, the coexistence of traditional and new activities, with the new personnel who had completely different backgrounds and working cultures, generated problems that needed to be resolved. In a growth period, with high expectation, the potential resistance can be easy to overcome, but the problem can run much deeper and be far more difficult to resolve when the company’s results worsen.

Governance and financing are important aspects that are closely related to one another, and decisions on these aspects made during the transformation process can greatly affect the final outcome. The transformation from an industrial company to a technology intensive company requires a long period of low profitability and reduced cashflow, this necessitates a proper financial structure, which is in turn related to correct governance (Baysinger et al., 1991). The Abengoa’ case is a model that we believe can be replicated by other companies who wish to bet on technology as their competitive advantage. However, it is necessary to remember that radical strategic change requires modifications in diverse aspects of a company and that high-level management consider all the stakeholders that may contribute to the success of the change process.

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Hedge Performance via Simulation. The Journal of Risk Finance, 3, 6–34.

















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Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors.2

2 Valdivia, M., Galan, J. L., Laffarga, J., & Ramos, J. L. (2020). A research and technology

valuation model for decision analysis in the environmental and renewable energy

sectors. Renewable and Sustainable Energy Reviews, 122, 109726.

Impact factor JCR: 10,556

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Chapter 2: A research and technology valuation model for decision analysis in the environmental and renewable energy sectors

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Abstract

Annually, companies invest vast amounts of resources into research and development (R&D) in order to remain competitive in today’s markets. This investment intensive effort needs to be carefully managed and monitored in order to make the most of these investments. Making informed decisions regarding resource allocation is vital for maximizing return on investment (ROI). One important way to achieve this is use of a valuation model that is capable of estimating the potential return of R&D investment by assessing among other parameters, the level of uncertainty and risk associated with this kind of project. This paper presents a valuation decision model that has been applied to the renewable and environmental sector. Here we present a flexible, user-friendly and understandable model that reflects the intrinsic risks associated with R&D projects. The model can be customized for use across a variety of capital intensive and medium-long term R&D projects in the private and public realms. A real-life case study is used to illustrate the utility of the model within the biofuel sector; specifically, for a project focused on the production of biofuel from municipal solid waste. Use of valuation models, such as the one described here, when used from the very initial phases of the project till project completion can serve a vital role in R&D management—one that enables decision-makers to define realistic and productive targets throughout the full R&D project period.

Key Words

R&D Management, R&D valuation, Real Options, NPV Valuation, R&D uncertainty, R&D risk, Project Management.

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1. Introduction

Investment in research, development and innovation is critical to for companies to stay competitive in medium and long term. This is particularly true for those in technology-driven sectors, such as pharmaceutical, environmental or renewable energy industries. For these sectors, the risks associated with expending resources to develop new and successful products are significant. These risks are linked to the requirement for extensive time and resources necessary to convert a good idea into an innovative product (Moehrle and Walter, 2008). Furthermore, the resources that companies manage, both in human and economic terms, are dwarfed by the extensive resources that could be expended on potential R&D projects (Hassanzadeh et al., 2012). Thus, it is critical that project managers and decision-makers in industry have the appropriate tools required to decide where to devote these limited resources (Raynor and Leroux, 2004). Decision analysis and valuation methods represent important tools that help managers to maximize financial returns on investments and guide the selection of critical variables during the different development phases of R&D projects.

R&D projects within a company can be monitored through different qualitative and quantitative methods as described by Ruegg (Ruegg, 2007) and Souder (Souder, 1972). While R&D management through valuation models are well described in a number of journals, techno-economic valuation approaches for R&D are applied mostly in large companies and not so often in small enterprises or large academic projects. Most models, regardless of the underlying methodology used, are focused on measuring the economic return on investments. However, as stated by Davis and Owens (Davis and Owens, 2003), these models are most useful for providing insight into a given process, rather than by providing a final overall value for returns.

Currently, the three most widely used models are: 1) discounted cash flows (DCF), which enables the measurement of a net present value of the investment (NPV); 2) the tree decision model; and 3) real options valuations. There are other methodologies, such as system dynamics or data envelopment analyses, that are no longer widely used in industry.

Companies within the pharmaceutical sector have used the above-named valuation methodologies extensively, particularly due to the historical importance of R&D in this sector. Other sectors, such as infrastructure or energy, where R&D has not been historically so important, are now developing appropriate tools to guide sustainable business models. Not surprisingly, these sectors are adopting models developed originally by the pharmaceutical sector. Nonetheless, these methodologies are not yet, in general, used on a regular basis due to their lack of transparency or suitability. Hartamann and Hasaan (Hartmann and Hassan, 2006) stated that a number of companies rely on the traditional deterministic NPV model despite its lack of flexibility, because managers feel more comfortable with the logic behind the model.

To improve the evaluation of R&D projects within the energy and environmental sectors, the ARDV model (applicable R&D valuation model) a flexible valuation methodology based on decision analysis, NPV analysis, Decision Tree Analysis and Monte Carlo simulation is presented here. This novel evaluation approach is intended for use by project managers and

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decision makers to simulate the stepwise progress of R&D projects and to provide a transparent model, that is free of ‘black boxes’, and that can be used and understood by most stakeholders. The approach that has been developed takes into account technological and commercial risks associated with launching innovative or new products into the market.

Although considerable research and theory into the valuation of commercial projects exists in the renewable energy sector (Menegaki, 2008; Jeon and Shin, 2014; Fernandes et al., 2016; Schachter and Mancarella, 2016), the literature is limited to the analysis and valuation of a few specific R&D projects. The approach presented in this article was developed, validated and used for several years in a company within the renewable energy sector. Although it was used within this sector, the model may also serve as a useful decision analysis tool for other capital-intensive sectors, such as infrastructure or conventional energy for medium-long term development projects (ie, projects that span from 5 to 10 years in duration).

The model presented here helps to advance current valuation models, and therefore R&D management, through a methodology that was developed to address the weaknesses of traditional models. For R&D project managers, the model provides a simple and user-friendly interface that can be adapted to a myriad of project milestones—thus, providing a clear advantage over DCF. Furthermore, the new model serves to limit risks, and enables analysis typically done with Real Options models, while providing implementation methodology and quicker analysis of results. With respect to Decision Tree Analysis, the new model is easier to implement, while covering the same number of scenarios. The model allows one to measure the impact of changes to on the overall final result—making it useful for defining project targets. Therefore, this new model represents a useful decision-making aid for managers—one that can be used over the entire lifetime of a variety of R&D projects.

The article is divided as follows: in Section 2 article, a literature review is presented that describes the current state-of-the-art in R&D valuation, and the reasons why a new model has been developed. In Section 3, a detailed description of the model methodology is presented. Section 4 presents a case study and justification, and Section 5 includes a discussion and conclusion, which summarizes the new model and describes future research trends in the area of R&D project valuation.

2. Literature review

R&D management is a relevant field in business development, and one of the main topics within R&D management is valuation. R&D valuation is an essential part of any management system and is widely used by corporations that invest in innovative projects (Bjorn and William, 1997). However, the financial value of technology development is quite difficult to estimate, especially during the earliest development stages (Davis and Owens, 2003). Given this uncertainty, it is not surprising that there exist inherent risks for the use of any valuation models, as they may provide misleading readouts and/or lead to incorrect conclusions (Hunt et al., 2003). The fundamental purpose of a valuation model is to provide accurate data to decision-makers and to decrease uncertainties and risks. In order to achieve goal, better models are needed (Celiktas, 2016; Schachter and Mancarella, 2016). Furthermore, models must consider that the majority of the value derived from an R&D project is, typically,

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generated not after initial investment, but rather via subsequent opportunities that arise thanks to this investment (Liu, 2011).

R&D valuation can be used over a wide range of scenarios and project types. Therefore, one must differentiate between valuation of a specific and independent project and valuation of a larger project portfolio. The latter is used to evaluate a set of independent projects that compete for the same resources within the same organization. Furthermore, decisions regarding R&D in a corporation are influenced by different factors; in addition to established valuation protocols; public policies and macro trends are also important players. Although similar methodologies can be used to analyze independent projects and a portfolio of projects, the approach taken to analyze the results should differ (Wouters et al., 2011).

While other models focus only on one endpoint—the commercialization of the technology under development (Boer, 1998; Hunt et al., 2003; Vega-Gonzalez and Rivera-Velasco, 2016)—the literature generally agrees that the best way to value and manage R&D projects is to divide projects into different stages (Luo et al., 2008). This approach helps to reduce risks and hedge uncertainty. The model that has been developed here is focused on project-based valuation—an approach that is also compatible with portfolio analysis. As presented by Raynor and Leroux (Raynor and Leroux, 2004), the flexibility provided by portfolio valuation, while representing a best practice for R&D projects, also enables project selection to be better aligned with the strategic goals of the organization.

As stated above, NPV, Decision Tree Analysis (DTA) and Real Options are the methodologies most often used in R&D valuation models. Because each of these have pros and cons, current research is focused on finding ways to robustly integrate them to leverage individual strengths and overcome limitations. As specified by Managi (Managi et al., 2016), the paths taken by R&D projects are highly variable; thus, models need to be flexible enough to consider various scenarios, while remaining reliable, understandable and efficient. Another important characteristic of an R&D valuation model is that, in addition to technology risks, it must also consider market risks (Vega-Gonzalez and Rivera-Velasco, 2016). Use of these models by managers must leverage the ambiguity typically linked to long-term R&D projects (Lint and Pennings, 1998), and managers should carefully set strict thresholds so as to not artificially increase the project value (Luo et al., 2008).

The key limitations of the deterministic NPV model consist of the lack of flexibility and inability to adapt to the intrinsic uncertainty of R&D projects (Lint and Pennings, 1998; Perlitz et al., 1999). The NPV model relies on a series of deterministic cash flows, which are modified by a risk rate provided by a discount rate. These values are used to calculate the net present value of the investment. It has been demonstrated by several authors (Siddiqui et al., 2007; Farrukh et al., 2009; Lambert et al., 2015) that the use of a classic or deterministic NPV model, without flexibility, fails does to capture all of the potential value that a R&D project may create. Additionally, Lee (Lee, 2011) affirms that in the face of market uncertainties, policy changes or market disruptions, the NPV method fails to adequately describe reality.

Lint and Pennings (Lint and Pennings, 1998) do not recommend use of NPV for long term R&D projects. Uncertainty in R&D must be seen as an opportunity and accounted for in the model (Hunt et al., 2003). To adequately manage uncertainty using the NPV model, several authors recommend the use of Monte-Carlo simulation (Crama et al., 2007; Jeon and Shin, 2014; Oreskovich and Gittins, 2016).

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Another commonly used methodology is Real Options (RO). It was first introduced by Myers (Myers, 1984) to evaluate R&D projects, and this approach has superior flexibility than previous valuation models. An added benefit provided by RO is the ability to consider future opportunities that arise from R&D project investments. It is based on financial options methodologies, defining an option as “right, but not the obligation”(Hunt et al., 2003). Many authors purport that RO best reflects flexibility (Mascareñas, 2007), and the reality of an R&D project (Oriani and Sobrero, 2008); accordingly, it is the most cited model in the literature (Perlitz et al., 1999). As such, RO is seen to be superior to traditional NPV in that it is better equipped to describe and account for volatility, long timelines and high-risk projects; that said, it should be noted that some RO valuation models are actually based on NPV approaches (Bednyagin and Gnansounou, 2011). Limitations of RO have also been described (Luo et al., 2008), including the tendency to overvalue the projects due to subjective forecasts.

However, and as stated by Luo et al. (Luo et al., 2008), although RO is considered a more effective model than DCF and DT for R&D project evaluation, it is not widely used by the industry (Perlitz et al., 1999; Willigers and Hansen, 2008). Other authors have stated that use of RO alone is not sufficient for R&D projects (Schachter and Mancarella, 2016). This may stem from serious usability issues, which have been raised by Dater et al. (Datar and Mathews, 2004). In particular, the volatility parameter, which strongly influences the final result, is the least understood and is often miscalculated (Loch and Bode-Greuel, 2001)—effectively becoming a “black-box” for users.

Other RO limitations include the fact that the quality of RO analysis may be compromised by faulty interpretation of results and that the model has a tendency to overvalue future benefits (Tompkins, 2002; Blanchet-Scalliet et al., 2005; Liu, 2011). Because of these limitations, some experts recommend combining NPV with RO when evaluating R&D projects (Managi et al., 2016).

A third model used to evaluate R&D projects is DTA. This model presents a number of future scenarios that are associated with certain probabilities (Wang and Halal, 2010). A key limitation of the DTA is that it can quickly become too complicated when the number of future possibilities increases (Trigeorgis, 1997; Reyck et al., 2008; Ben-David, 2012; Kahneman and Tversky, 2013).

In order to select an appropriate model, certain experts suggest that the choice should depend on the intended final users. For example, some authors affirm that DCF is preferred by practitioners, while RO is more useful for early stage projects (Vega-Gonzalez and Rivera-Velasco, 2016).

As summary of the advantages and limitations for the different valuation methodologies are shown in Table 1.

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Table 2 Advantages and limitations of existing valuation methodologies.

Of all the different industrial sectors, the pharmaceutical sector is the most developed in terms of R&D valuation (Hartmann and Hassan, 2006; Oreskovich and Gittins, 2016). Within the renewable energy sector, although there are intensive R&D investments, there are a paucity of studies that explore sector-specific case studies or scenarios (Menegaki, 2008). Thus, it is clear that there is a need for more research into the area of renewable energy valuation. While the models have been developed via macro comparisons with fossil fuels (Detert and Kotani, 2013), and studies have defined market potential (Kjaerland, 2007) and utility of the inclusive willing to pay methods (Menegaki, 2008), existing sector-specific models have serious limitations (Siddiqui et al., 2007).

To solve these hurdles, this article presents a flexible, user-friendly and understandable model that reflects the intrinsic risks associated with R&D projects, while highlighting the main technological developments needed to achieve economic and commercial viability, the ARDV Model. The ARDV model, in addition to prioritizing projects based on economic returns, can be used to rank projects by the difficulty of achieving technical targets.

3. Model explanation

R&D project valuation, particularly in the renewable energy sector, must consider the following issues: i) uncertainty inherent in the development of the technical aspects of the project; ii) long-term market conditions and risks associated with market acceptance; and iii) maturity of R&D project. The model presented here covers these three issues.

In order to take into account inherent uncertainty, the ARDV model considers variables that serve to faithfully describe key technical characteristics of the R&D project. From our experience, it is recommended that no more than five technical variables be analyzed per project. This number sufficiently serves to describe the evolution of the project and allows good correlation between variables, while enabling the effective interpretation of results, such as, the price of the raw material vs. the final product cost. Selection of these variables and the values introduced for each must be agreed upon by the project technologists and other project participants (Lint and Pennings, 1998).

Market drivers are as important as technology variables. Selling prices, market regulations, the numbers of potential business opportunities are included in the model. Furthermore,

Advantages Limitations References

Easy to understand DeterministicWidely use and accepted Lack of flexibility

Financial Sensibility analysis

Flexibility Not understandableUncertainty Inefficient

FinancialComplicated data implementation

Result interpretation

Project overvalue

Graphical representation Difficult implementationSimple analysis for few

scenariosSensibility analysis

Complexity for significant number of scenarios

Discounted Cash Flow Model

Real Options

Decision Tree Analysis

Lint and Pennings 1998Pertlizt et al .1999

Lambert 2015Lee 2011

Wang & Halal 2010Trigeorgis 1997

Vega-Gonzalez & Rivera-Velasco 2016

Hunt et al. 2003Oriani Sobrero 2008

Bednyagin & Gnansounou 2011Luo et al. 2008

Schachter & Mancarella 2016Datar & Mathews 2004

Diesel et al. 2009Tompkins 2002

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the influence of competitors on the selling prices and market share are modeled. As with the technical variables, correlations between these variables can be modeled. Moreover, correlation between technical variables and market variables can comprise part of the model.

Because uncertainties in an R&D project are significant, the selected variables are modeled using Monte Carlo simulation with different probabilistic curves. To define the curves for each variable, the user can draw on the range defined for each and set the probability that the value of a variable is at the minimum, maximum, as well as the mode and/or the average in a previously defined range. The dispersion of the inputs introduced in the model is a function of the maturity of each of the variables. This data is used to create the most likely curve for the variable; possible curves can be either continuous (beta, normal, triangular, etc.) or discrete (binomial, discrete, etc.). This methodology then enables the future behavior of the project, and consequently the inherent risk, to be defined and projected for the R&D project.

The period considered for market commercialization of a technology is also important. This period depends on the technology and market maturity, and it is defined as the obsolescence period, which is the duration of time over which new technologies remain competitive in the market. This period varies depending on the technology and according to market conditions; namely, the number of competitors and entry barriers. In our model, the duration of market commercialization spans between 7 and 20 years.

One of the main challenges to creating a reliable model is the need for realistic inputs for the variables. Market variables are taken from reports by prestigious agencies that publish future trends in the renewable energy sector, such as the International Energy Agency or the Energy Information Administration. Technical variables are defined based on the current state-of-the-art and theoretical values and technological objectives for each of the projects.

Once the inputs and associated uncertainties are introduced, the model is ready to evaluate the viability of the technology. However, additional information is required to take into account potential market opportunities and the number of facilities to be executed thanks to the R&D development.

The energy and environmental markets offer limited opportunities for new technologies. To help identify these, the product pipeline under evaluation is introduced into the model and is based on forecasts provided by international agencies and internal companies’ predictions. For some projects, a probability of occurrence of the market opportunity is included. As it is often the case, a number of additional opportunities, which have not been identified originally in the pipeline, may ultimately arise.

In order to make the value calculation, the model simulates a project for each of the opportunities that may appear in the pipeline and it calculates the cash flow for each. After satisfying the cash flow and the investment criteria, which in our case is set as a minimum internal return rate (IRR), if the project fulfils the requirements defined by the firm, then the project would be executed and consequently will generate value for the company.

But the model is not just a go/no-go decision model, it also enables analyses of the main variables required to reach a viable product within the technical and economical realms. The model can also enable users to discern critical variables and identify where more resources should be invested based on their ability to influence economic returns. Furthermore, the

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model can be used to define a path between various technical targets for each phase, and to review these paths and how they influence other variables and projected economic returns. This is summarized in the scheme presented in Figure 1.

For any economic valuation, the accuracy and quality of financial parameters are critical. As explained above, while ARDV model is a techno-economic model, it is also focused on studying the viability of R&D projects, so the discount rate cannot be calculated only based on financial parameters, as some authors affirm (Loch and Bode-Greuel, 2001). To evaluate R&D projects, companies generally use the weighted average cost of capital (WACC), which has been traditionally used for commercial projects—an approach that is likely not appropriate (Crama et al., 2007). Taking this into account, for R&D projects, the discount rate should depend on factors, such as project sector, the maturity of the project and the target market. For the current model, project maturity is the most weighted factor.

The company where the model was validated is managed using the 3H methodology introduced by McKinsey in their publication “The Alchemy of Growth”(Baghai, M., Coley, S., & White, 2000). Using this methodology, a project is divided into three categories or ‘horizons’ depending on their role in the organization. Briefly, these are: H1 – the cash generators; H2 – growth options; H3 – future options. To ensure a balanced R&D project portfolio, there should exist a spread of these three horizons to ensure that new and innovative products are developed, while maintaining cash generation. Obviously, each horizon is associated with a different level of risk, both technological and commercial, and this must be considered in the discount rate. Typically, the discount rate range used in our model operates within the following ranges: H1 (7 – 9%); H2 (10 – 12%); and H3 (12 – 15%). The exact numbers chosen within these ranges depend on the specific project characteristics and those of the sector and market maturity, etc. The methodology of the current model is presented in the next section using a specific example.

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Figure 1 Valuation decision model. This scheme shows the interrelationships between the different types of variables and the intrinsic characteristics of the project

4. A case study and justification

Below is described a detailed example in which the ARDV model was used to valuate an R&D project. The model has been validated through its use in a multinational company that is focused on the engineering and construction of renewable energy projects with proprietary technology. The company, which has its headquarters in the South of Spain, had (in 2015) a presence in more than 70 countries and more than 20,000 employees worldwide. The company has a strong innovative culture and is a pioneer in commercializing new technologies in biofuels, solar or hydrogen energies. It owned the first commercial solar tower in Africa and the biggest thermal solar complex in Europe. The company understood the food versus fuel dilemma and promoted research in the area of bioethanol from agricultural residues and municipal solid wastes.

The company invested nearly €100 million in R&D in 2014. This R&D was organized according to the different sectors in which the company had a presence: bioenergy, solar, water, hydrogen and power electronics. Each of these sectors run their own projects under a project director and a number of project managers that handle the allocated funds.

The case presented in this paper was set in the bioenergy area, although the project includes a combination of energy and environmental activities and involved second generation (2G) biofuel production. One of the key goals of 2G technology is to produce biofuels from agricultural residues, which is currently in early stages of commercialization (Valdivia et al., 2016). In this case, the raw material that is used to produce fuel is municipal solid waste

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(MSW). The impetus for this project was based on the fact that almost all of the ethanol produced for blending with gasoline is derived from cereal grains or sugarcane.

The project addresses an emerging issue regarding MSW—the sheer volume of it is becoming a problem for municipalities worldwide and one that is expected to worsen as the world population increases (IEA International Energy Agency, 2014; Scarlat et al., 2015). In summary, the R&D project under evaluation in this example involves the production of bioethanol from MSW in order to aid the treatment of MSW and to generate a biofuel. Figure 2, below, provides a high-level overview of the process of producing ethanol using MSW as raw starting material.

Figure 2. High-level process configuration for ethanol production using Municipal Solid Waste (MSW) as raw material. The figure shows the main steps in the conversion of MSW into ethanol. The first step is pre-sorting the MSW; in the second step, the organic fraction is sent for pre-treatment and enzymatic hydrolysis, during which the enzyme cocktail is added. The sugar produced during hydrolysis is fermented to produce the beer that is distilled to obtain ethanol.

The obvious first step required to build the model is data collection. This step can be divided into three main areas: technical inputs, market inputs and financial hypothesis. The second step is to select the main variables that influence the technical development of the R&D project. The critical technical variables are selected by the project director and should include all variables that impact commercial viability, should not be more than five in order to ensure that analysis of the data is feasible. Namely, for this project, these variables are; i) ethanol yield per ton of raw material, ii) natural gas consumption, iii) enzyme dose, iv) and capital expenditure (Capex).

Ethanol yield per ton of raw material serves as readout of improvements made to the pretreatment of the starting material; it measures the amount of product (ethanol) that can be obtained per unit of raw material (ton of MSW). The natural gas consumption is the main expense in the operation of the plant, or the main variable cost; thus, reducing this value will directly benefit the profitability of the process. The performance of 2G ethanol technology depends heavily on enzyme performance (Valdivia et al., 2016), which is the enabling additive that transforms the raw material into a viable product. The Capex, which measures the funds needed to be financed per project, is a critical determinant of financial viability.

As described in the previous section, each of the variables are defined by a range and a trend that models how they change as the project progresses. These values are defined by considering acceptable parameters for current state-of-the-art processes and the technological objectives of the project.

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Process improvements arise from two main factors: the R&D activities, and the learning curve, which defines the incremental improvements made as projects are successfully commercialized and scaled up. However, both sources of improvement are uncertain; thus, in order to include this in the model for each of the selected variables, a value range is defined. Table 2 shows, on the one hand, how the variable ranges change based on the number of plants built, termed the learning curve (see the various columns in Table 2). On the other hand, Table 2 also shows the ranges of each variable, which depend on success of the R&D project. The project director is the person in charge of defining these ranges, in accordance with the project targets.

Table 3 Technical variables to be included in the model. The first column indicates the model’s technical variable. The rest of the columns are variable ranges and probabilities depending on the number of plants that are built.

For each of the above variables, the most accurate probabilistic curve is defined, thanks to a software model known as @Risk. As an example, a curve for natural gas consumption in the first facility, as shown in Table 2 above.

On the market side, the methodology is similar. In this case, three variables that are most likely to influence commercial viability were selected. These variables are selected by the project director, who has most in-depth knowledge of the project, and by the corporate strategy department of the company, which is responsible for providing market know-how and defining prices (i.e., a market analysis expert).

The variables that were chosen were: i) the price of ethanol; ii) the fee that the company would charge municipalities per ton of MSW treated; and iii) number of industrial plants that will ultimately use the commercialized technology. Regarding ethanol price, four scenarios are defined in the model. These scenarios are based on projected legislation changes, the company’s past experiences and the existence of subsidies for advanced biofuels. The scenarios shown in Figure 3 correspond to the future price ranges provided to the model.

Number of Facilities ConstructedCritical Technical Variables Probability Value Probability Value Probability Value Probability Value

15% 18 25% 18 25% 15 40% 1560% 21 60% 21 60% 18 50% 1825% 24 15% 24 15% 21 10% 2120% 65 15% 65 10% 65 10% 6560% 75 70% 75 80% 75 80% 7520% 85 15% 85 10% 85 10% 8510% 12 10% 10 5% 9 15% 930% 15 40% 12 15% 10 60% 1060% 18 50% 15 80% 12 25% 1210% 260 20% 260 50% 260 80% 26055% 311 60% 311 40% 311 15% 31135% 350 20% 330 10% 330 5% 330

Enzyme Dose (mg/g)

CapEx (M€)

1st Facility 2nd and 3rd Facility 4th to 15th Facility Facility >15

Natural Gas Use (MW)

EtOH per raw material (gal/Ton MSW)

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Figure 3 Ethanol prices. All scenarios are internal predictions made by the company. Scenario 1: High oil prices and fiscal credit until 2022 (blue lines); Scenario 2: Internal company strategic plan (orange lines); Scenario 3: high oil prices and fiscal credit extended beyond 2022 (grey lines); Scenario 4: No fiscal credit since 2014 (yellow lines)

The second variable, which is critical to the success of the project, is the fee that the company would charge municipalities per ton of solid waste treated. This is a competitive market where companies develop new technologies to fulfill the prerequisites set by municipal administration, in terms of prices and environmental requirements. It is expected that the fee paid by the municipalities will decrease in coming years, mainly because incoming technologies will further increase the competitiveness of the market. As with the other variables, probabilistic curves were inputted into the model based on internal company projections. Table 3 shows the MSW fee range; also, note that as the number of facilities increase, competitiveness is expected to increase , and the fee paid by the municipality thus decreases. See for instance the probabilistic scenario for the first facility and the probability scenarios when the technology is fully mature with more than 15 plants built.

Table 4 MSW fee. The table shows the evolution of the MSW fee variable. The columns show the ranges of the variables and probabilities depending on the number of plants that are built.

A third key variable is the number of industrial plants that will ultimately use the technology. The potential market readout slowly grows from the start of the commercialization process up until the end of the 20 years period (ie, the obsolescence period) (Figure 4). The project pipeline is based on internal company projections. Each opportunity is defined for the

Number of Facilities ConstructedCritical Marlet Variables Probability Value Probability Value Probability Value Probability Value

0% 30 0% 30 2% 30 35% 300% 40 8% 40 17% 40 29% 4012% 50 11% 50 21% 50 22% 5020% 60 18% 60 23% 60 9% 6050% 70 45% 70 24% 70 5% 7018% 80 18% 80 13% 80 0% 80

2nd and 3rd Facility 4th to 15th Facility Facility >15

MSW fee (€/ton)

1st Facility

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municipalities worldwide that have shown interest in building a facility with these characteristics. The projection is that with a 90% of the total probability the number of plants will be in the range between 14 and 23 facilities with a minimum of 9 and a maximum of 29 plants being built.

Figure 4 Number of commercial opportunities in the next 20 years. The diagram shows the probabilities of plants of being built in the valuation period. Each project in the pipeline has a particular probability of occurrence. The sum up of each number of plants corresponds to 100% probability for the analysed variable

To the above project variables, we shall ad the financial parameters to measure the project, which will be influenced by the maturity of the project. As explained in the previous section, the maturity of the technology is the factor that most influences the financial parameters used. The maturity of this project is considered to be ‘H2’ (as per McKinsey Methodology (Baghai, M., Coley, S., & White, 2000)) in terms of a R&D project, due to the fact that the technology has already been demonstrated at bench scale, but not at the commercial plant scale. The return rate required (IRR) for this kind of project ranges between 8% and 10%. For this specific project, a value of 8% has been chosen for the first two plants and a value of 10% has been chosen for subsequent plants. This was done to take into account the greater risks associated with the first plants to be developed, while ensuring that investor margins were not overvalued at early stages and to enable the technology to become commercial though requesting lower returns.

Once all the variables were inputted and defined, the model provided a cash flow model per year from 2016 to 2029 for each project, including the option to consolidate all of the

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projects. This last readout is critical, as it enables the user to identify the total amount of resources required and the cash recovery.

Results

The first set of results presented below is for the evaluation of a stand-alone plant. Subsequently, the evaluation of all R&D programs is shown, including the calculation of overall development cost.

Construction of the first stand-alone facility

Figure 5: IRR for the initial facility. The area covered by the figure corresponds to 100% probability for the analysed variable. IRR distribution for the first commercial plant built. With a probability of 43.3% the IRR of the plant will be above 8% and only in 4.7% of cases the IRR will be above 10%.

The first analysis reveals that in approximately 45% of the simulated cases the IRR will be greater than 8%, which provides the go ahead for construction of the plant. While the model is useful for calculating the probability of positive returns, it can also be used to inform the project team about how to influence these forecasted results. To gain this insight, the user must identify which variables exert the greatest influence on the final IRR of the project. This can be achieved by carrying out correlation analysis, which reveals the relationship between IRR and key influencers.

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Figure 6: Sensibility analysis. The analysis of variable shows that the variable that influence the model the most is the MSW fee and CAPEX; whereas enzyme dosage, the ethanol prices and the raw materials have much lower effect.

The first variable is a market input—specifically, it is the fee that is charged per ton of MSW treated. While the leadership overseeing R&D has limited ability to influence this variable, the risks associated with it must be carefully considered and be used to determine which markets are targeted. The second variable is CaPex (Figure 6). Typically, every R&D project that enters into the last phase of development must concentrate its efforts on improving process design to reduce the required investments since this variable has a clear negative effect (Figure 6)

Yet another market variable is ethanol price. This must be carefully considered as it will greatly impact whether the project achieves commercial success. The fourth variable is the ‘potential to ethanol’, which is related to two factors: the quality of the starting raw materials, which varies according to geography (note: discuss this factor it is not further discussed because it does not influence model outcomes); and how the raw material is pretreated (a variable that the model shows directly affects economic returns).

The last variable that was considered is enzyme dose. The model shows that this variable has a similar impact as the ‘potential to ethanol’ and also that reductions in the dose will positively impact outcomes (Figure 6).

After this initial analyses, and by considering how the variables influence each other and the outcomes, the developers can then decide on which variables they want to focus.

Construction of subsequent facilities

After analyzing how R&D improvements differentially influence the IRR for the first facility, further analysis was conducted for the construction of subsequent facilities. In Figure 7, the change in IRR over time is shown; the graphic shows that the R&D and construction of new

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plants improves the returns (i.e., from 7.8% to 8.35%) , until the number of facilities is above 15 when the return drops to 7% (Figure 7) and drops even further if the number of plants is above 15.

it

Figure 7: The evolution of IRR as the project matures. Red graphic: 1st plant to be built, with an expected mean return of 7.8%; Blue graphic: 2nd and 3rd plants with expected returns around 8.3%. Green graphic: 4th to 15th plants with an expected return of 7% and Purple line plants > 15. The area covered by the figures for each of the scenarios (1st plant, 2nd and 3rd plant, 4th to 15th plants and plants>15) corresponds to 100% probability for the analysed variable.

While this seems counterintuitive, it is hypothesized that as the number of plants increase, the market becomes saturated, and consequently the fee per ton of MSW will decrease enough to offset any gains made due to technical improvements.

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Figure 8: Hypothesized effects of changes to MSW fees. Red graphic: 1st plant built; Blue graphic: 2nd and 3rd plants. Green graphic: 4th to 15th plants and Purple line plants>15. The area covered by the figures for each of the scenarios (1st plant, 2nd and 3rd plant, 4th to 15th plants and plants>15) corresponds to 100% probability for the analysed variable.

Bearing this data in mind, it is key to determine the minimum fee per ton of MSW that the project requires to be viable, i.e., to establish the number of plants to be built. The model can quickly determine this by enabling one to run a simulation while varying the value of the MSW fee. In this particular case the minimum fee required for an IRR above 8% is 70.3€/ton. This is much higher than the €41.7 /ton that the model gives for the point at which more than 15 plants are constructed. Figure 8 clearly shows that for an expected IRR of 8% the MSW fee decreases as the number of plants increases. Thus, the model can identify key variables and threshold values that can help to reveal a clear path towards the development of competitive products. By accounting for how the technologies evolve over time, the model also enables companies to forecast key market values to can could inform the drafting of future agreements and strategies to adapt to competitive pressures.

Program valuation

The next step in the analysis is to determine the total program return, or the total value that will be created by the R&D investment. The model helps to weigh product pipelines and project developments against the total resources needed for product commercialization—key factors that can help managers proactively adjust resource allocations as the project advances.

The model considers the fulfillment of the investment criteria for each commercial opportunity, and also the total program value creation. This latter value is determined by

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calculating of NPV ranges to be created for investors, while considering the discount rate. As previously mentioned, this is a techno-economic valuation; thus, it is not purely a financial analysis and, along with profitability of the R&D project, the model also aims to identify project viability from a technical perspective. As such, the discount rate is calculated based on the maturity of the project and the number of resources allocated to R&D, similarly to how the methodology is used to calculate IRR. As an H2 project, the discount rate is 10%. For other stages, such as H1, the range is less, due to lower risks (i.e., 8-10%), while values increase for higher risk H3 projects (i.e., 12-15%).

The timeline that the model considers is 15 years, which corresponds to the lifespan of the technology—a value that is determined by considering commercialization readiness, obsolescence period and the time span required to fully capture the full potential value created by the technology. The R&D investment period is set to 6 years from the date of valuation, and the intensity of the R&D investment, as expected, decreases from about 24 million € for the first year to about 3 million € for the third year and even lower for the sixth year (Figure 9).

Figure 9: R&D Investment for the MSW program.

Considering the characteristics chosen above, the value generated by the total program is shown in the Figure 10. The left-hand side of the graphic shows that the project has a 32% chance of causing net economic loss for the company, with potential losses reaching a maximum of €34M. However, the right-hand s of the figure shows that the project has a 25% chance of generating net positive economic returns valued greater than €100M (Figure 10). The spread of the results shown in Figure 10 is a consequence of the uncertainties and the risks involved in the project.

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Figure 10: Program NPV. The area cover by the figure corresponds to 100% probability for the analysed variable

When considering risk management, managers should be constantly searching for ways to reduce the risk of losses. As described in the literature (Boer, 2003; Raynor and Leroux, 2004; Wouters et al., 2011), R&D investments are made in phases. Thus, evaluating and optimizing risks present in each phase serves to reduce global risk—a strategy that can be followed using our model. For the current project under evaluation, our model shows that when the project is in the early stages of commercialization, an important consideration is the timing of the construction of the plant. If management sets a limit—for example, that the project be aborted if the first plant is not built within 2 years of project launch—the model shows that maximum potential losses fall to €25M; however, this policy would greatly reduce the possibility of achieving positive returns.

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Figure 11: Program NPV limiting the R&D investment. On one hand, the NPV is affected positively due to the decrease in R&D investment; while on the other hand, the project suffers due to lack of technical competitiveness. The area cover by the figure corresponds to 100% probability for the analysed variable.

This is likely because, as shown in Figure10, much of the foundational R&D investments have already been made, and as such, aborting the project reduces the possibility of recovering these investments. After analyzing the stress scenarios, it becomes clear that the proper decision is to continue the R&D project, because while the potential losses grow when the project is continued, the possibilities of receiving a positive return increase to a greater extent.

5. Discussion

There are numerous valuation models for R&D projects; unfortunately, many of them have considerable limitations (Vega-Gonzalez and Rivera-Velasco, 2016). This paper presents a comprehensive and robust ARDV model that displays easily interpreted graphic results. These can be used by managers and project leaders to make decisions based on data and a clear understanding of the projects’ implications. The tool presented in this study takes as inputs, data that project managers have obtained during the execution of the project or information that is gleaned from available data, which is more transparent when compared with Real Options models, where data implementation may become a “black-box” (Linton et al., 2002; Mascareñas, 2007). Another advantage of this new model is that the results are presented as graphical outputs that are easy to interpret and analyze. This makes the

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model particularly attractive for decision makers because the conclusions drawn can be understood and easily explained to the organization. For example, this is an improvement over the RO and DTA models, which provide values as readouts that can be difficult to interpret and compare to other scenarios (Benninga and Tolkowsky, 2002; Martín-Barrera et al., 2016)(Perdue et al., 1999). Another benefit of our model is that it includes programs that enable managers to carry out sensibility analysis, as shown in Figure 6. Combined, these advantages enable managers and project managers to use this new model to define clear and measurable targets and gauge the impact of decision making on project progress—overcoming one of the main limitations of the classical NPV model (Jeon and Shin, 2014; Vega-Gonzalez and Rivera-Velasco, 2016).

Table 5 Decision model analysis presented in this article

Although the case study described in this paper is for a project that is at an advanced stage of development, the model can be used at any R&D project stage. This flexibility represents an advantage over models that exclusively focus on early project stages (Stawasz and Stos, 2016) or mature projects (Luo et al., 2008; Vega-Gonzalez and Rivera-Velasco, 2016). Moreover, the valuation model can be updated as different project phases progress, so that the model evolves with the project, providing managers with an up-to-date view of the project and the ability to trace and track key decisions made during project execution. Use of the model in this way enables managers to define clear and measurable targets throughout the entire project. Thus, by ‘connecting the dots’ over the project lifespan, the model can be used by managers to define funds the required and expected returns based on improvements in previous phases—a characteristic that is complementary to other analyses that are focused on portfolio valuation (Grimaldi et al., 2017).

Although this article deals with a particular example within the bioenergy sector, the model can be used in both the environmental and renewable energy sectors. It is particularly relevant for projects with medium/long term development periods (i.e., about 5 years); and those with intensive capital expenditure, both at the R&D stage and at the first commercial facility (or product) stage.

It should be noted that the methodology presented in this article can be used to valuate alternatives of the final product or different projects—enabling analysis across a full product portfolio. This flexible and homogenous valuation methodology provides comparable data and results that can be used to make decisions across a product portfolio, as can be carried out using DTA (Ben-David, 2012), but with the advantage of being able to do it as part of a single analysis.

Easy to understandManager acceptance

Flexibility Graphical representation

Sensibility analysisDirect result interpretation

Decision Analysis Model

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Regarding portfolio analysis in a same simulation, although the ARDV model can be used for portfolio analysis, at present the model would need to be run separately, it means in different simulations, for each of the projects, which would impy This limitation need to be overcome in future. However, the analysis and comparison between two (or more) projects is feasible and provides reliable results.

6. Conclusions

This article presents a R&D valuation model that is flexible, reliable and reflects the intrinsic uncertainty associated with intensive capital expenditure projects. The benefits of the presented model are the following:

• The model focuses on decision making analysis, which provides a better understanding of the potential returns, and the ability to gauge the effect of key variables on project development.

• The model overcomes the following existing bottlenecks:

o It can be implemented at different project stages, from early R&D phases, when they have a lot of uncertainty, to pre-commercial stages, when the confidence increases.

o It provides graphical outputs that are easy to understand by managers and project leaders.

o It can be used to simulate multiple scenarios, while doing so in a way that is simple and easy to understand.

• The model can be applied to any renewable energy, environmental or conventional energy project.

Some of the current limitations of the model presented here include the following:

• Technical variables are defined by project directors; thus, if the data is not defined in a rigorous manner, the analysis could become impartial.

To overcome this limitation, a Steering Committee could be created and tasked with deciding on the inputs; alternately, forecasting methodologies, such as the Delphi methodology, could be used.

In conclusion, the valuation model presented here has the potential to become an important tool to support R&D management—one that provides complementary knowledge and confidence for making decisions under uncertain circumstances. The methodology is particularly relevant for use with capital intensive programs that require several years of development and that are executed within highly competitive technological niches and markets. Given the flexibility of this model, it can be easily adapted for valuation in other sectors or for different project types—a characteristic that overcomes the limitations of other more traditional models.

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Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials.3

3 Valdivia, M., Galan, J. L., Laffarga, J., & Ramos, J. L. (2016). Biofuels 2020: biorefineries based on lignocellulosic materials. Microbial biotechnology, 9(5), 585-594.

Impact factor JCR: 4,86

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Chapter 3: Biofuels 2020: Biorefineries based on lignocellulosic materials

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Abstract

The production of liquid biofuels to blend with gasoline is of worldwide importance to secure the energy supply while reducing the use of fossil fuels, supporting the development of rural technology with knowledge-based jobs and mitigating greenhouse gas emissions. Today, engineering for plant construction is accessible and new processes using agricultural residues and municipal solid wastes have reached a good degree of maturity and high conversion yields (almost 90% of polysaccharides are converted into monosaccharides ready for fermentation). For the complete success of the second generation (2G) technology it is still necessary to overcome a number of limitations that prevent a first-of-a-kind plant from operating at nominal capacity. We also claim that the triumph of 2G technology requires the development of favorable logistics to guarantee biomass supply and make all actors (farmers, investors, industrial entrepreneurs, government, others) aware that success relies on agreement advances. The growth of ethanol production for 2020 seems to be secured with a number of 2G plants, but public/private investments are still necessary to enable 2G technology to move on ahead from its very early stages to a more mature consolidated technology.

Key Words

R&D Management, 2G Biofuels, Market

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1. Introduction

Biofuels produced from crops have been the driving force in renewable energies. In the first decade of the 21st century, there was a major focus on the debate of food vs. fuel. Reports made by national and international agencies, such as OCDE (OCDE, 2008), FAO, EU, and others concluded that the food commodity prices were being impacted by consumption for the production of biofuels. Other slightly later reports (Mohr & Raman, 2013) studied the sustainability of 1G ethanol production and the implications of alternative feedstocks. Influenced by the global debate, policies were implemented to promote the production of liquid biofuels from feedstocks not used for human consumption, and give rise to what is called, second generation biofuels (2G). Lignocellulosic material, from herbaceous crops, hardwood and softwood, are the main feedstocks used for the production of liquid biofuels, particularly ethanol.

The main drivers behind a push toward both 1G and 2G biofuel production, are (i) energy supply security and reduction in dependency on oil imports, (ii) support for rural areas through technology deployment and creation of knowledge-based jobs and (iii) mitigation of the greenhouse gas emission (GHG), and the reduction in emissions of particulate matter that are toxic for the environment, animals and humans —promoting a low-carbon and sustainable economy.

Over the last decade, research in the 2G market has been searching for a significant breakthrough that will lead to it being cost-competitive with first generation biofuels. Unfortunately, the development of the lignocellulosic ethanol market has been slower than expected due to a perception of high technological risk, intensive capital costs and the low oil prices that result in poor economics for the biorefineries (Stephnen et al., 2012). An intensive growth period (2014-2020) has been forecast and production capacity is expected to reach 2220 Million litres by 2020, from a capacity of 750 Million liters in 2014 (United Nations, 2016). Other organizations, are not as optimistic but also estimate growth, for example, the OECD-FAO (Food and Agriculture Organization of the United Nations) estimate a total capacity of 1703 Million liters for second generation ethanol by 2024, mainly in USA and Europe (UNCTAD, 2015).

In spite of discrepancies in the forecast figures for 2G ethanol, the general feeling is clear, there will be very significant growth over the next decade. Some studies have estimated the value that can be generated by the lignocellulosic industry, for example, (Hertel et al., 2015) give a value to second generation industry of $64 billion under baseline conditions. Apart from the direct evolution of the lignocellulosic industry, there are other external factors that have a clear impact on the future of 2G production, in particular oil prices; for lignocellulosic biofuels to be cost-competitive an oil price in the range of $70-85/barrel is required (Sims et al.,2009).

A focused strategy to battle climate change through regulation will strengthen the stance of alternative technologies and secure the second generation biofuel industry. In conjunction with climate change mitigation strategies, new technologies also require a boost to allow the development of new products. Some steps have been made globally in this regard and the USA is currently the most advanced market owing to a stable legal framework. The EU is a step behind due to social concerns and the lack of a common

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strategy in EU-28. Low carbon fuel legislations in France, Italy and the UK include ethanol tariffs and anti-dumping penalties as barriers to biofuel production.

Analysis of the policies in the USA revealed several drivers that favor 2G ethanol. These policies were developed under the Energy Policy Act of 2005 and were published as the Renewable Fuels Standard (RFS), which was later updated by the Energy Independence and Security Act of 2007. The RFS assures that the transportation fuel sold in the USA contains a minimum volume of renewable fuel. The RFS objective (Figure 1) is to increase the biofuel blende up to 36 billion gallons (Bgal) by 2022 from 9 Bgal in 2008.

Figure 6 Volumes target for renewable fuel for the USA. Source: US Environmental Protection Agency (EPA)4.

To assure these targets are on track, each year the US Environmental Protection Agency (EPA) publishes the amount of biofuels that the blenders have to include in their gasoline, the Renewable Volume Obligation (RVO), is controlled by assigning identification numbers, that is, renewable identification number (RIN). The last update was published by the EPA on Dec.15, 2015 and the requirements are shown in Table 1. Of note, the volumes required were significantly reduced from those published in the clean air act of 2007, this is due to a delay in the deployment of the first commercial lignocellulosic bioethanol plants.

Table 6 Update of biofuels volume requirements for 2014 to 2017 according to EPA5

4 Source: https://www.epa.gov/renewable-fuel-standard-program/program-overview-renewable-fuel-standard-program

5 Source: EPA. https://www.epa.gov/renewable-fuel-standard-program/proposed-renewable-fuel-standards-2017-and-biomass-based-diesel

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The RINs are assigned to the production facilities and are traded in a public market. The blender buys RINs at the same time as the ethanol is purchased or it goes to the market to buy the RINs and reach the requirement set by the administration. To differentiate between corn-starch ethanol, cellulosic ethanol and other renewable fuels, several categories are defined to give an amount of blending for each type of biofuel that is, D3 for cellulosic biofuel, ethanol and biogas. This classification is under the legislation of the RFS2 which defines different categories depending on the feedstock and the GHG reduction6: Renewable fuel: 20%; Advanced biofuel: 50%; Biomass-based 50%; diesel: 50% and Cellulosic biofuel: 60%. Since the start of the RFS 354 million metric tonnes of CO2 has been avoided (Boland & Unnasch, 2015).

In addition to the previously described blending legislations, the US includes a number of value generating aspects at the federal level, for example the cellulosic waiver credit (CWC), which is a tax exemption that inversely correlates with gasoline prices. The EPA calculates the CWC each year, it price is the greater value of $0.25 or $3.00 minus the wholesale price of gasoline. For 2015 the waiver was $0.64/gal while in 2016 the value may be as high as $1.33/gal and in 2017 it is likely that the waiver will be higher again due to lower gasoline prices.

It is also necessary to take the impact of the automobile industry into account. It is important that they promote the 2G ethanol industry, not only because car/truck emissions will be reduced, but also because there are concerns among consumers and policy makers on the so called “blend wall”, that is, the amount of biofuel that can be blended per unit of final fuel. Legal blending in a number of countries is around 10%, this has been under discussion for several years now, and it has been proposed to increase the legal requirement up to 15%. No agreement has been reached between oil refiners, vehicle producers and the biofuel industy, and as such this move is proceeding very slowly. Experience shows that current motor vehicles could operate with E15 without any major changes.

In countries where the bioethanol industry is well developed, such as Brazil, most of the vehicles are flexi-fuel, allowing the consumer to choose between using regular fuels or biofuel depending on prices. The Brazilian experience supports that biofuels can be used in appropriate vehicle fleets.

2. What is the market situation today?

Tremendous advances have been made by the lignocellulosic industry in the last decade. In fact, at least four commercial plants have been inaugurated in the last few years: Project Liberty by the joint venture Poet-DSM; Dupont 2G ethanol facility in Iowa; Abengoa

n/a: not available

6 Compared to a 2005 petroleum baseline

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Bioenergy Hybrid Kansas, by Abengoa; Crescentino by BetaRenewables, (the only one in Europe)

All of these are at different levels of operation as they are in their current start-up phase. In all cases, a number of issues have been encountered that have prevented full operation, this can be expected for the use of first-of-its-kind technology. The good news is that the owners expect them to be in regular operation by 2017; success in this set of facilities is crucial for the further commercial deployment of the industry.

In the US, government support for the 2G technology has been significant, but is probably still not sufficient. To date, the government has allocated large amounts of funding for R&D projects, in addition, some companies, such as Abengoa, POET-DSM and IneosBIo have received funding for commercial facility construction. There is no doubt that this support helped to advance the state of the art for this technology, however, further support is needed in order to bring the technology to maturity. This support could be provided through direct funding for commercial projects, and through support using tax credit exemptions, premiums for lignocellulosic biofuels or by increasing the legal blending limit.

Investors are another necessary arm that can assist in bringing the 2G biofuel industry to a mature level; there are a set of private investors that are willing to allocate their resources to green, sustainable and economically viable technology. In order to have access it will first be necessary to deploy the aforementioned commercial facilities. This will decrease the perceived technological risk for investors, and increase the number of entities, banks and private funds interested in this market.

3. The lignocellulosic biofuel value chain

One of the main peculiarities of the lignocellulosic biofuels is its value chain, which starts at feedstock harvesting. The availability of enough cost-effective biomass is one of the main challenges for the industry. It should be noted that previously many agro-wastes were left on the ground. New machinery is needed to harvest, process, transport, and store the large amounts of material that are needed to make 2G biofuels. All of these factors influence the price of feedstock and unfortunately the logistics for handling and supplying feedstock are not well developed.

Different raw materials can be used as feedstock for lignocellulosic biofuels, these include agricultural residues (corn stover, wheat straw, sugarcane straw, bagasse, etc.), forestry residues (woody biomass), municipal solid waste or energy crops planted in non-productive areas.

The challenge is not the global amount of feedstock that is available; in fact, a number of studies estimated that in the US alone, there is more than 450 Mdryton/year could be available by 2030 (U.S. Department of Energy, 2011), this amount would have the potential to produce 67 Ggal ethanol/year. The US Department of Energy (DoE) suggested that there are between 600-1000 million tons of terrestrial biomass that should be available at price of $60 /ton at origin (the farm gate).

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So the problem is not the amount of biomass but the logistics of procurement. As a consequence of the lack of a well-defined logistical model, biomass supply represents the main cost in lignocellulosic biofuel production. It should be noted that municipal solid waste is an exception here and has a different scenario. Another major issue is the cost associated with getting the biomass to destination. Today’s commercial plants have transport costs up to $75 US /ton, this makes the economics of the technology non-viable. Efforts to optimize the supply of biomass are needed. For this two lines should be stressed, on the one hand, the biomass at origin, and on the other hand the logistical model. For the former, farmers need to be made aware of the profit that could be derived from the sale of biomass for added value processes. Industry experience, both in the USA and Europe, demonstrated that farmers need to be educated in the benefits that they will obtain from the deployment of this industry. The industry must provide the relevant key information to farmers in the regions where a 2G facility is going to be constructed to create the proper atmosphere. The extra income for rural areas will increase the profitability of traditional farming.

The possibility of utilizing marginal land for the growth of biomass to be used as feedstock for biofuels is a major advantage of the lignocellulosic biofuel industry. A large amount of work has been aimed at the development of viable energy crops; these kind crops are easily adapted to the environment and ground conditions. Moreover, these viable energy crops increase two critical factors; the energy density per hectare (GJ/ha), this is the amount of energy that can grow per hectare, and the potential biofuel yield (Gal/ton), this is the maximum theoretical amount of biofuel that can be obtained per unit of biomass (Somerville et al., 2010).

One of the main challenges that any commercial plant faces in obtaining financial backing is the assurance of long term feedstock supply. To have a bankable project, it is necessary to have sufficient feedstock to assure the economic return of the project. This kind of contract is new for farmers and to biomass suppliers who usually work on an annual basis. Creation of agricultural associations will be an important step on the path to reaching these agreements. As mentioned above, the use of energy crops with reliable long-term base production will also decrease the risks in supply.

Signing long-term supply agreements is quite a challenge, particularly in Europe, where the number of participants is multiplied. For example, to assure a 300 kton per year supply of corn stover it is necessary to reach an agreement with more than 20,000 farmers, while in the USA, for the same amount, it could be achieved with just 150.

Supply is not only a cost problem; it is also a location issue. Nowadays, facility location is determined by feedstock availability over a very limited distance, not more than 200 miles. To have available feedstock in a short radius, the facilities are placed in relatively remote areas, this in turn increase the rest of the production costs, such as, utilities, personnel and final product transport logistics.

One solution could be to create centralized markets or biomass reference markets that allow homogeneous supply routes, e.g. biomass pellets or chipped material in the case of woody biomass. An example could be, as described by (Lamers et al, 2015), an intermediate storage where pre-processing of the biomass is carried out. This would decrease logistic costs and provide higher versatility to all facilities. Standardization of biomass from different feedstocks, defining specifications regarding polysaccharide content that could be reached by more than one agriculture residue or energy crop will help to advance the 2G industry.

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Another challenge is to have multi-feedstock lignocellulosic biorefineries, that is, second generation facilities that work with heterogeneous lignocellulosic materials. If future facilities are able to process a mix of feedstock, the industry will reach the flexibility required to provide more freedom for the location of the facilities, and the number of potential facilities per region will probably increase. As such, an area to develop is the optimization of productive processes for lignocellulosic biofuels so that they operate simultaneously with different raw materials; this will make the technology less dependent on local feedstock from a given location.

4. Lignocellulosic facility process description

Once the biomass is harvested, collected and transported to the facility location, the next step is to process and convert it to a liquid biofuel.

The technology to process lignocellulosic materials is not yet fully established, with the first commercial plants finalizing commissioning and beginning start-up in the last few years. Although many advances have been made, second generation biorefineries have great challenges to overcome in the next decade in order to become a mature and competitive technology. The procedure is mainly divided into 4 processes (Figure 2): 1) pretreatment, where the cellulose and hemicellulose of the biomass is made accessible; 2) enzymatic hydrolysis, where the biomass is converted into sugars thanks to the addition of the proper enzymes; 3) fermentation, where the alcohol is produced from C5 and C6 monomers and finally, 4) distillation to produce a purified liquid fuel. In all of these areas, there is still room for technology improvement.

Figure 7 Lignocellulosic process converting the biomass into biofuels and co-products. Process step for conversion of agricultural residues in ethanol. Source: Abengoa

The aim of the storage and biomass handling areas is to receive and store harvested leftover agro-wastes. For a 25 Mgal facility, almost 1,000 tons of biomass per day is required. The storage area dimension depends on the logistic model, but it is expected to have enough capacity to maintain for at least 6 month of operation. The stored biomass must meet at least two key specifications: moisture content and ash content. The entrance to the processing area is through the biomass handling section, where the biomass, normally

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stored in bales, is de-stacked, de-stringed and size-reduced to reach the designed particle size distribution (PSD); finally, ground material is screened to remove fine particulate, which have high ash content. The output from the biomass handling goes to the biofuel process conversion area and if the facility has an integrated biomass boiler, part of the biomass will be allocated to be burned.

Biofuel conversion starts with pretreatment, which consists of an acid or alkaline soak system that saturates the feedstock with dilute strong acid or base. The soaking system also removes a significant amount of sand, which can cause severe erosive damage to the equipment. The soaked biomass is pretreated in a continuous steam explosion reactor. The pretreatment process solubilizes the hemicellulosic sugars, primarily xylose and arabinose, and greatly improves the cellulose digestibility through disintegration from lignin.

The pretreatment process is followed by a conditioning step to adjust pH, temperature and total solids content. Afterwards, the enzyme is added to the conditioned pretreated biomass slurry to reduce the viscosity in a continuous liquefaction tower to the point at which the slurry can be easily pumped to saccharification tanks (Alvarez et al. 2015). Sufficient residence time in the saccharification tanks is maintained for the conversion of the cellulose into monomeric glucose and hemicellulose to glucose, xylose and other sugars of lower concentration.

The saccharification is followed by simultaneous fermentation of xylose and glucose to ethanol using a genetically modified strain of brewer’s yeast (Saccharomyces cerevisiae). Other organisms such as Clostridium and Pseudomonas can be used to produce alternative biochemicals (Rasgauskas et al. 2014, Tolonen et al. 2015, Ramos et al. 2016, Sanford et al 2016).

Another option exists in recovering the lignocellulosic sugars to be sold as raw material for biochemical processes in plants located in a radius of about 500 km. In this case, it is necessary to include solid-liquid separation steps that permit the sugar to be purified to a level that can be used in further processes.

In a standard 2G facility, ethanol is distilled and dehydrated using conventional distillation and molecular sieve steps. The semi-solid residue from the distillation process, called whole stillage, is separated into a liquid stream referred to as thin stillage and a soil cake using a filter press; the cake is a fraction enriched in lignin. The thin stillage is concentrated to produce ~50% solid syrup through seven stages of multi-effect evaporation. The cake and syrup have different options to extract their value, currently the most common one is to burn it on-site, or in a closely located biomass burner, or to sell it as raw material to be used in soil amendment. The raw materials can be used in soil amendment because composition allow to increase organic C content and provide porosity to substrates that facilitate seed germination and plant root development.

We have so far described the “core areas” required for lignocellulosic biofuel production. However, depending on the location or on the type of project, other areas, such as a biomass boiler or wastewater treatment plant, have to be included within the facility to comply with environmental legislation or to generate the vapor needed for plant operation.

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The facility cost should take into account both, the capital cost needed per gallon (Capex) and the cost to operate the plant (Opex). Both are influenced by the kind of project. There are three main types of projects:

- Greenfield/ standalone: this is a plant by itself. It needs a complete value chain management. Its location influences the Capex and also the logistics of biomass supply and ethanol sales. In terms of Capex, it needs the construction of the core process, biomass handling, pretreatment, enzymatic hydrolysis, fermentation and distillation; and also auxiliary operation units such as cogeneration and a waste water treatment plant; these usually significantly increase the overall cost.

- Co-location/ Bolt-on: in this concept some existing infrastructures and operations can be shared thanks to the proximity of other industries. This model requires at a minimum the construction of the process area. The value chain for feedstock procurement and logistic must be completely developed. The construction of auxiliary operations units is limited.

- Hybrid /integrated: The whole value chain is completely integrated within a 1G facility, taking advantage of the synergies in feedstock supply and product logistics. The construction of auxiliary operation units is not necessary.

The aim of the two last configurations is to decrease the cost of the auxiliary or complementary operation units and focus the efforts on reducing the cost of the technological core areas.

For example, for a green field project the cogeneration area may represent up to 30-35% of the total equipment costs, as such choosing a location where the energy instead of being produced on site, could be bought at regular prices from close industries or facilities represents a significant advantage. Some other issues, such as cake and syrup destination need, however, to be solved.

After feedstock availability, reducing the investment cost through the reduction of the auxiliary equipment is the second step toward making a commercially viable project. The challenge, to the industry, is to be as flexible as possible to increase the potential locations where to place viable technical and economical projects.

There are a range of alternatives, outside improving the logistic model, where the industry is working to reduce operational capital costs. These include enzyme cost reduction by improving activity, valorization of the lignin contained in the raw material, increasing the pretreatment efficiency or improving the yeast production organism.

Cost structure

The location of a plant will definitively influence the overall facility layout. Investment costs will differ significantly depending on the configuration required. For a green field stand-alone project, a non-core area such as cogeneration increases the initial investment needed by more than 30% (Figure 4). Absence of synergies with external utilities concomitantly results in an increase in storage equipment and logistical costs.

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Figure 8 Equipment cost area distribution of a general lignocellulosic greenfield facility. Pretreatment and Boiler are the most cost intensive areas. Source: Abengoa

A co-located plant may require less than 50% in capital investment than a stand-alone plant. Within this strategy, Poet-DSM has co-located a 2G facility within a 1G plant. Our estimates are that investment cost ranges between $10 - 14/gal, for a plant with a nominal plate of 25 Mgal according to its location.

5. Biomass handling & Pretreatment

As described above, the first step in a lignocellulosic facility is to prepare the biomass for processing. This is critical because it influences all of the downstream processing. We can distinguish two main steps, one where biomass is just pre-processed to obtain the designed PSD and, in the subsequent step, the biomass is pretreated in order to make hemicellulose and cellulose accessible for cellulases.

For the biomass handling area, the main objective is an appropriate delivery of milled feedstock to the pretreatment system with a consistent quality that meets the required specifications in terms of particle size and total ash content. As mentioned in the feedstock section, the logistics model is critical to obtain a homogenous stream at the beginning of the process. No special innovations have been made in this area, but some experts are starting to develop different methodologies that will improve the facility performance and also the integrated logistic model.

Once the biomass has gone through the handling system, the next step is the pre-treatment that will release the oligomers to be transformed into sugars. This is a critical step in lignocellulosic biofuel production. Each market player has developed their own technology

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as the feedstock defines the pretreatment technology and conditions. One of the main hurdles that the industry faces is that the pretreatment needs to be optimized for each raw material, limiting the flexibility of the plant to process different feedstocks. That is why more versatile pre-treatments are required so as the process becomes less raw-material dependent.

Pretreatment is the “core” area that is most capital intensive, requiring an investment that may represent between 30% and 50% of the total equipment cost. One of the main challenges is the intrinsic recalcitrance of the lignocellulosic biomass which results in lower biomass to sugar yields and therefore in the higher pretreatment costs (Stephen et al., 2012).

A number of technologies are available today for the pretreatment of lignocellulose, including, chemical, physical and biological processes. Some of these technologies have already been commercialized and are well known, while others are still at lab scale.

The most relevant commercial technologies are given in Table 2:

Table 7 Pretreatment technologies.

The 2G commercial technologies are protected by a number of patents that guard the technology while the economic viability of the projects are improved. This is why different pretreatment have been considered. The different strategies result in a series of advantages/disadvantages that are enumerated in Table 2.

Steam explosion is a well-known advanced technology which consists of heating the biomass in water under pressure followed by a sudden decompression of the reaction vessel. As a result of the violent decompression, the structure of lignocellulose is disrupted and the fibers are opened up, leaving sugar polymers more accessible to the subsequent enzymatic hydrolysis (Stelte, 2013). As no chemicals (other than water) are used, equipment corrosion is minimal and requirements for the reactor metallurgy are less demanding. Also, the level of release of chemicals that may act as inhibitors in the saccharification or fermentation steps is very low. The main drawbacks of steam explosion are related to the mildness of the process which limits the effectiveness of the pretreatment and demands the use of very high

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enzyme loads in the saccharification step. Also, steam explosion is not effective on high lignin content softwood samples, and the effectiveness with hardwood is also limited (Brownell et al, 1986, Yang & Wyman., 2008).

Another option is the use of dilute acid in the pretreatment, this involves the use of dilute aqueous solutions of inorganic acid (HCl, H2SO4) combined with temperature. This pretreatment results in good depolymerization and release of hemicellulose and cellulose. Compared to steam explosion, dilute acid pretreatment is more efficient for woody samples. As acidic conditions allow partial depolymerization of hemicellulose and cellulose, the enzyme loading required is lower compared to simple steam explosion. However, this kind of pre-treatment requires high capital investment due to the special reactor metallurgy; operational costs are also higher. It is well known that aqueous ammonia treatment allows biomass delignification without a significant degradation of sugars. However, the effectiveness of this pretreatment with some feedstock, such as woody biomass residues is rather limited (Yang & Wyman., 2008).

Commercial pretreatments for corn stover, wheat straw and sugar cane straw are currently being optimized, however, one of the main challenges that the sector must overcome in the coming years is to enhance process versatility to be able to deal with more than one raw material at a time. In terms of operational cost, pretreatment consumes chemicals and steam, this can represent up to 20-25% of the total operational costs.

Lignin removal is another key step in the development of the biofuel industry; in the current methodology, lignin is maintained until the distillation phase. However, there are several pretreatment technologies in development that try to separate the components of the biomass in different streams, one of the most promising is the use of Ionic Liquids (ILs) which are able to dissolve lignocellulose under mild conditions, resulting in more accessible cellulose and recovery of lignin in the raw material. Nevertheless, there are still challenges to the industrial deployment of this technology, including high cost and regeneration of ILs (Tadesse & Luque, 2011).

Searching for other alternatives may reduce costs and increase the possibility of using lignin in new ways that are not currently used to add value. Further discussion on the lignin issue follows below.

In the first commercial plants, one of the main bottlenecks in the commissioning and start-up phase was achieving suitable performance in the pretreatment area. Problems such as blockage due to non-defined particle size distribution, not achieving the pretreated biomass specifications, and not being able to reach high enough downstream have all been major hiccups.

The main challenges in pretreatment are: development of a versatile technology that can reach appropriated levels of pretreated biomass material independently of the raw material used; generation of combination plants that can more easily process under milder conditions to allow optimization both economically and environmentally; to optimize pre-treatment processes using less corrosive chemicals making construction materials cheaper and, as consequence reduce the initial investment (in particular, steel alloys resistant to acid or base).

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6. Enzymatic hydrolysis

After the material is conditioned in the pretreatment area, the next step, the enzymatic hydrolysis or saccharification is one of the most critical factors in lignocellulosic biofuel production, and represents one of the main technology development areas. Enzymatic hydrolysis represents the second main operational cost, after the biomass production; in 2G it is ~25-30% of the operational costs while in 1G it is below 3%.

Why this difference? The answer is the enzymatic cocktail needed for 2G. These cocktails have a combination of a wide range of activities and properties, such as cellulases, hemicellulases and ß-glucosidases that convert the polysaccharides into C6 and C5 sugars (Alvarez et al. 2015). Most of the enzymes used in the conversion to sugars are of microbial origin, but have been ‘hitched’ to other than their native host, that is, they area engineered. Today only about three companies have commercialized this kind of cocktail: Novozymes, Dupont and Abengoa.

In the market there is a consensus that the final enzyme cost contribution should be stabilized around $0.4/gal. Nowadays, this cost contribution is achieved at least at a demo scale by the enzymatic cocktail developed by Abengoa, although the performance on a regular basis at commercial scale is still a pending issue. Reported commercial data indicate that the three commercial cocktails may operate within the same range. Reducing the enzymatic cocktail cost contribution is critical for the viability of the 2G technology because the cost of enzymes can be up to 30%.

How can these cocktails be improved? There are several possibilities, (i) reduce the enzyme loading by improving the enzyme activity through genetic engineering using high performance mutagenesis strategies coupled with massive tracking systems, (ii) Reduce the cost of protein through better production methodologies, (iii) Increase the overall hydrolysis yield by adapting the enzyme cocktail performance to the process conditions. For the last one the exploration of extreme environments and the use of metagenomic techniques that allow the identification of new enzymes that are more efficient is a critical focus (Elleuche et al, 2014). The most relevant aspect is the use of thermophilic enzymes because the increase of temperature prevents hydrolysates from being contaminated and, therefore, the loss of raw material; this increases the overall performance of the process.

Another factor is that the total solid ratios at which the technology can work is relatively low compared with 1G, this is due to the lower yield per tonne of raw material introduced and the longer residence times needed. In 2G the typical solid ratio is 20% versus 33% for 1G. Although the enzymes would work even better at lower solid concentration, this is the minimum ratio at which ethanol concentrations are reached. One of the main improvements that can be made is to increase the performance of the enzyme cocktail at higher total solid, or at least reaching optimum performance at current levels.

As noted above, the 2G enzyme cocktail seems to be a niche market with very few players. A significant increase in the volume of 2G enzyme market is required in order to provide producers with the equity to optimize their cost structure. Enzyme producers need to be involved in the industrial development process if they want 2G facilities to achieve their nominal capacity. If the market is converted into an oligopoly with few competitors gaining most of the value from the 2G technology, the development of the industry globally will

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become difficult. Enzyme developers need to be involved in the industry growth, participating in different ways with, not only the industrial developers, but also with other stakeholders.

7. Fermentation

After the enzymatic hydrolysis, a sugar stream is obtained that differs from the first generation sugars in that the amount of C5 sugars represents almost 30% of total sugars, C5 sugars are released from the hemicellulose with xylose. The current fermentation technology produces ethanol, although other alternative alcohols or bioproducts can be synthesized e.g., alkanes or long chain alcohols, butanol, jet-fuels, etc.

The fermentation of glucose and xylose in the same reactor can be considered a well developed technology, with conversion yields of more than 95%. Very efficient yeasts have been designed and optimized to ferment xylose and glucose simultaneously. While this is true with herbaceous lignocellulosic material, when the raw material comes from wood, a series of inhibitors are generated in the pre-treatment that make the fermentation processes less efficient (Heer & Saner 2008, Tomás-Pejó and Olsson, 2015). Another very important aspect is to shorten the fermentation time; this could lead to a reduction in the number of fermenters per plant with a consequent saving in the initial investment. In addition, it should be possible to work at different pHs or temperatures, to improve the propagation phase.

In this area, unlike at the enzyme market, there is no risk of an oligopoly which could cap the market and there is still room for improvement in terms of operational conditions to decrease the overall costs.

The processing of C5 is a must. It is not economically viable to process lignocellulosic biomass without getting any value from the hemicellulose or the lignin contained in the raw material. Several production platforms, using bacteria or yeast, are under investigation but none of them have as yet reached high enough productivity.

Some experts predict there will one day be a lignocellulosic sugar market, but today’s oil prices make it non-competitive, except for in some niche cases where the upstream product is produced outside of the sugar production facility. This is the reason why so many joint ventures and co-ops have arisen in this market in recent years.

The industry must look for win - win agreements. This will include on one hand, the 2G technologists or the companies that have the necessary technology to reach affordable lignocellulosic sugars and on the other hand, the final product companies (chemical companies) that can benefit from green sugars and alcohols.

Clearly the final market needs will accelerate the product development based on the specification requirements and continuous collaboration between technologists and final clients.

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8. Distillation, Solid&Liquid Separation and Evaporation

The final step in reaching the product is the distillation of the stream, which comes out from the fermenters. The distillation of ethanol, if the stream is produced on specification, is an area that does not present major issues. It represents between 15-20% of total equipment costs. A critical factor in this step is the use of the stillage produced during the distillation. To have an economically feasible technology, the valorization of this stream product is mandatory. The stillage has a high concentration of lignin, 20-30% depending on raw material source. At present, in commercial plants, the stillage, following separation of the water content, is sent to the cogeneration area to be burned to provide power vapor to the 2G process. This waste is currently burned in cogeneration plants where the value of the generated energy is $5-10 US / ton. To increase the profitability of the plants of lignocellulosic material it is necessary to add value to the stillage. In the configurations where there is not a cogeneration area within the plant or nearby, the lignin stream is used for irrigation in areas close to the facility. The main problem is the large volume produced. For example, a 25 Mgal plant, produces around 300,000 ton/day of stillage. The other option is to send it to a wastewater treatment plant, if the configuration has it, or send it to a disposal agency; both of which represent a significant extra cost for the plant (Ramos et al., 2016).

So, to increase the value obtained for this co-product, one of the main challenges of this industry is to valorize the stillage as was done with fiber in the first generation industry. In the process of ethanol generation from corn or other cereal grains, the economy of the process rests not only in the sale of alcohol but also in the grain residue that is left, which is known as DGG and is used as animal feed (Patrik R. Lennartsson, 2014).

As has been stated, the stillage contains up to 30% lignin within its composition coming from the lignocellulosic biomass. Lignin is a natural polymer with excellent properties that can be used in the chemical industry. A number of reviews on lignin valorization are available (Rasgauskas et al. 2014), but there are few success cases that have been implemented. A difference that 2G lignin has compared to commercial preparations, principally those which come from the pulp and paper industry, is the absence of sulphur; this is a great advantage for its use in some chemical applications such as resins or composites. Most 2G technologists are struggling with themselves on how to increase lignin value. The current proposal is to extract lignin from this waste to be used in the synthesis of new resins to replace those based on chemical compounds derived from the petrochemical industry.

Once the technology has been fully developed and the main challenges resolved the question is: Would the market be ready for this new product?

9. Concluding remarks

Production of liquid biofuels from grains in the so-called 1G generation is a mature technology that seems to have plateaus in technology and production capacity. An increase in the production of ethanol is foreseen to be the result of the conversion of agricultural wastes and municipal solid residues into ethanol using the so-called 2G technology. A number of 2G plants have been built, but their operability at full nominal capacity has not

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been attained so far due to mechanical hurdles. The identification of these limitations, their potential solutions and combined public/private investment will be crucial to enable 2G technology to move on ahead from its very early stages to a more mature consolidated technology. International expert agencies believe that an estimate of 15 2G plants will be built by 2024. In our view, we also foresee that ethanol will not be the only product to be derived from polysaccharides originated from agricultural residues and wastes, but also another more potent liquid biofuel, such as butanol, is making symbiotic biofuels part of this new market.

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Conclusiones

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El propósito de esta Tesis Doctoral ha sido profundizar en el conocimiento de la utilización que hacen las empresas de la I+D para acceder a nuevos mercados y productos, convirtiendo las inversiones y la gestión de la I+D y la innovación en palancas de crecimiento y fuentes de ventaja competitiva. Para abordar este objetivo general se ha llevado a cabo el estudio de un caso real y se han planteado varios objetivos específicos:

(i) Comprender y documentar la transformación de una compañía de base industrial a una empresa tecnológica, señalando las fases del proceso y las principales ventajas e inconvenientes observados.

(ii) Proponer e implementar herramientas de I+D industrial y, en concreto, desarrollar un modelo de valoración de I+D que ayude en la gestión del proyecto y en la cuantificación del retorno esperado de la inversión

(iii) Estudiar de forma completa un proyecto de I+D, focalizando el desarrollo tecnológico en las necesidades del mercado.

El análisis longitudinal del caso muestra que la transformación de una empresa industrial a una empresa tecnológica supone un cambio estratégico, con cambios en la estrategia, la estructura, el poder y los sistemas de control de la compañía. Esta transformación requiere la implicación de toda la organización y que las guías del cambio vengan dictadas desde el más alto nivel y con una figura de relevancia al frente del proceso.

En este proceso de transformación, la decisión del cambio estratégico es el primer paso, que se adoptará en el nivel más elevado de la jerarquía, pero debe venir acompañada de cambios en los niveles organizativos y una adaptación de la estructura a la estrategia fijada. Desde la perspectiva del gobierno corporativo, la compañía debe plantearse si debe continuar con los mismos líderes y si ha de introducir cambios en los órganos de gobierno. Respecto a los objetivos y a los sistemas de control, la transformación afectará a la estructura financiera y a la rentabilidad de la compañía.

El estudio del caso ha profundizado en la reorganización de la I+D de una compañía multinacional para llegar a ser una empresa de innovación, pasando de una I+D descentralizada a una centralizada. Se puede considerar que esta reorganización ha tenido éxito, en función del número de patentes y de otros indicadores de innovación, pero se han identificado algunos puntos críticos que interferían en el proceso. Entre ellos se puede mencionar que la transformación genera tensiones dentro de la organización, las cuales deben ser superadas con agilidad por el órgano de gobierno de la compañía, pues es conveniente culminar la reorganización en el menor tiempo posible.

Otro punto crítico tiene que ver con la financiación de la transformación. El diseño de la estructura de financiación es fundamental para el éxito económico del proyecto, sobre todo al tratarse de una organización con la I+D centralizada, lo que implica menos independencia de las unidades de negocio al gestionar y rentabilizar la I+D. La unidad o división de I+D necesitará financiación de la empresa, interna o externa, para desarrollar sus proyectos de innovación. Además, en los sectores donde se centra el estudio esta tesis, energía y medioambiente, los proyectos de I+D requieren elevadas inversiones a medio-largo plazo, de 5 a 10 años, pues suelen ser industrias muy intensivas en capital. La ausencia de una adecuada estructura financiera y de fuentes de financiación bien definidas, o en su defecto, muy dependientes de otras unidades de negocio, limita la capacidad de I+D de la propia organización. Por otro lado, desligar la financiación de la I+D de las propias unidades de

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negocio, puede provocar que la unidad de I+D no tenga una clara orientación hacia el mercado de la compañía.

El éxito del cambio estratégico requiere cambios simultáneos o sucesivos, pero coherentes y coordinados, en los distintos aspectos que caracterizan ese tipo de transformación radical. Si una de las dimensiones no se adapta a la tendencia marcada por ese cambio, puede provocar tensiones en el proceso y, con bastante probabilidad, dificultar el éxito de la transformación. En el caso estudiado se observa que el ámbito de poder y de gobierno corporativo se mantuvo sin grandes modificaciones, lo que ha podido perjudicar la culminación del proceso y la creación de valor en la empresa.

A pesar de estos inconvenientes y dificultades, es preciso resaltar, como se demuestra a lo largo de la tesis, que la inversión en I+D e innovación y la transformación de la estructura de I+D, ha aumentado la generación de conocimiento en la compañía. Este conocimiento se ha traducido en un incremento notable del número de patentes, que han permitido a la empresa acceder a nuevos mercados y productos que no hubieran estado a su alcance si no fuera por esta apuesta tecnológica.

Una vez definido el proceso de transformación que ha seguido la compañía, la tesis profundiza en la gestión de los proyectos de I+D y, en concreto, se ha propuesto y desarrollado un modelo de valoración, como parte del conjunto de herramientas a usar en la gestión. Los proyectos de I+D deben ser gestionados como cualquier otro proyecto, con unos objetivos medibles y reales, tanto desde el punto de vista tecnológico como de mercado, pero entrañan ciertas peculiaridades (incertidumbre tecnológica y de mercado, largo plazo, intensidad de las inversiones) que exigen modelos específicos.

Los entornos en los que se mueven las empresas son cada vez más competitivos y dinámicos y, por tanto, los proyectos de I+D corren el riesgo de quedarse obsoletos, incluso antes de llegar al mercado. Estos entornos hipercompetitivos exigen realizar simultáneamente un correcto seguimiento del mercado y un análisis hacia donde enfocar la tecnología. La tesis describe y profundiza en un caso real de un proyecto de I+D, en cuya gestión se tienen en consideración todas las pautas tecnológicas, de mercado y legislativas para alcanzar el éxito. Incluso con este análisis comprensivo, el proyecto conlleva riesgos inherentes a la propia investigación y comercialización del producto.

Las empresas necesitan reducir al máximo las incertidumbres de sus proyectos, y este empeño es especialmente relevante en proyectos de I+D, para los que se ha considerado a menudo que es difícil estimar el retorno que va a suponer la inversión. La tesis propone y aplica un modelo que permite estimar la rentabilidad de la inversión de I+D y que supera algunas de las limitaciones que se han observado y señalado de los modelos clásicos (Valor Actualizado Neto, Opciones Reales y Árboles de Decisión). Este modelo, además, ofrece la posibilidad de focalizar la investigación y las decisiones en aquellas variables que hacen posible maximizar el retorno global del proyecto, así como concentrar los recursos en aquellos proyectos con mayor potencial, no sólo tecnológico, sino también de mercado.

El modelo desarrollado, específicamente diseñado para el caso objeto de estudio, pretende ser una herramienta útil para otras corporaciones y proyectos de I+D con similares características, ya que es de fácil aplicación, proporciona resultados entendibles y tiene un carácter dinámico y flexible que permite implementar distintos cambios y considerar diferentes escenarios. Este modelo es de aplicación en cualquier sector industrial, siempre que los proyectos tengan similitudes con el considerado en la tesis. Esta aplicación en otros

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contextos conllevará la adaptación y mejora del propio modelo que redundará en una mayor usabilidad y generalización.

Los modelos tradicionales plantean diversos problemas cuando se aplican a proyectos de I+D. El modelo propuesto puede significar una contribución a la gestión de este tipo de proyectos, ofreciendo una herramienta de evaluación completa en cuanto a las variables manejadas, versátil y flexible en su utilización, y de fácil comprensión tanto en su funcionamiento como en sus resultados.

Al tratarse de una tesis industrial, el estudio ha intentado dar respuesta a una serie de problemas planteados por la empresa, relacionados con la gestión de los proyectos de I+D. Se trataba de mejorar la gestión de este tipo de proyectos, mediante el desarrollo de modelos de gestión y evaluación aplicados a casos concretos implantados en la empresa. Junto a esos problemas más específicos, la dirección también sugirió la conveniencia de analizar el proceso de transformación que había experimentado la compañía, para identificar posibles problemas y puntos críticos.

En consecuencia, las conclusiones del estudio se refieren a la problemática concreta de esta empresa, pero pueden ser generalizadas a otras empresas y sectores, como puede deducirse de las publicaciones de los capítulos en revistas de impacto, que ponen de manifiesto el interés general por los resultados de la investigación.

Como toda actividad humana, el presente estudio tiene diversas limitaciones que animan a futuras investigaciones sobre el tema. En primer lugar, aunque se han utilizado diferentes métodos de obtención de la información, combinando la observación participante con fuentes de información secundaria, entrevistas con directivos y el análisis crítico de los directores de la tesis, es posible que existan sesgos en algunas interpretaciones. La réplica de esta investigación en otras empresas y contextos indicará la naturaleza de esos sesgos y su importancia en la validez de los resultados obtenidos. En todo caso, las conclusiones no difieren de las obtenidas por estudios similares, proporcionando cierta confianza en la consistencia de los resultados.

En segundo lugar, el proyecto de I+D analizado es sumamente específico y pueden existir dudas sobre su posible aplicabilidad a otro tipo de proyectos. Ciertamente, se trata de una tecnología de vanguardia y un producto en fase de desarrollo y comercialización, con una elevada incertidumbre de mercado, tecnológica y legislativa. No obstante, hay numerosos proyectos de I+D con estas mismas características y consideramos que los resultados y conclusiones obtenidas pueden ser de utilidad para los responsables de esos proyectos. Como se indicaba en el párrafo anterior, los futuros estudios pondrán de manifiesto el grado de generalidad de los resultados.

Finalmente, aunque estamos convencidos que el modelo de evaluación propuesto y desarrollado constituye una mejora respecto a los modelos tradicionales, tiene limitaciones como se indicaron en el capítulo correspondiente. No obstante, el modelo constituye un paso más en un proceso de mejora que, sin duda, servirá de base para que otros investigadores y prácticos desarrollen nuevos modelos que permitan superar esas debilidades.

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Documents

R&D Management in industrial companies: Tools and