Dynamic Alignment of Digital Supply Chain Business Models

Volume 5 Issue 1ISSN 2694-7161

www.ejobsat.com

DYNAMIC ALIGNMENT OFDIGITAL SUPPLY CHAINSBUSINESS MODELSJochen Nürk1,2

1Mendel University in Brno, Czech Republic2SAP SE, Walldorf, Germany

NÜRK, Jochen. 2019. Dynamic Alignment of Digital Supply Chains Business Models. European Journal ofBusiness Science and Technology, 5 (1): 41–82. ISSN 2694-7161,DOI http://dx.doi.org/10.11118/ejobsat.v5i1.161.

ABSTRACT

A model for managing strategic alignment and dynamic capabilities (DC) of Supply ChainManagement (SCM) information systems (IS) has been developed and applied to a traditionalGerman steel company and a highly innovative Austrian steel company. Different concepts ofleading researchers have been combined to get a holistic and detailed view of IS capabilities’impact on strategic fit. The model enables companies to identify ideal levels to strategic fitneeded from SC integration and its antecedents for predefining architectural artefacts as sourcesfor dynamic capabilities. The study contributes to new insights into the IT productivity paradox,where possibilities from IS investments remain unused. Essential concepts for optimising SCperformance by reducing SC complexity and increasing SC agility have been identified andintegrated. The study highlights value enabler and Artificial Intelligence (AI) methods of digitalSC models and how the model’s ontology can be used to increase alignment autonomy. Finally, theapproach supports organisational learning and development of cognitive profiles through collectiveassimilation and sensemaking effects.

KEY WORDS

ambidexterity, digital business models, dynamic capabilities, strategic alignment, supply chainmanagement

JEL CODES

M100, M110, M150, O310, O320

1 INTRODUCTION

The motivation: Businesses demand of aligningSCM IS to their strategy increases globallybased on ongoing changes of industry conditions

and industry consolidations like Mergers andAcquisitions (M&A) as well as from technologi-cal change driven by the digital transformation

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such as the Industry 4.0 initiative. The oppor-tunities of SCM IS alignment are creating dif-ferentiated business value while companies needsimultaneously managing IT costs and focusingon IT investments that are sources of competi-tive advantage. Procedures and tools for SupplyChain Performance Management (SCPM) usingkey performance indicators (KPI) are providedby best practice endeavours such as the 2009SCORE model, which is offered by the USSupply Chain Management Institute and basedon the Balanced-Score Card approach (Kaplanand Norton, 1992, 2001). However, a bestpractice for assessing the strategic fit of SCMIS and the related business and IT architecturesappropriate to derive reasonable actions fromis missing so far. Digital business models andmanufacturing ecosystems such as driven by theIndustry 4.0 initiative focuses on the auton-omy in controlling business processes enabledby components such as Smart Services andCyber-Physical Systems that provide smoothcollaboration of ecosystem members connectedvia the internet at unforeseen events as well.While Artificial Intelligence (AI) methods areused primarily for managing the operationalprocesses such as sensing of conditions, decisionmaking based on business rules steering thephysical systems, the author misses autonomyon the architecture levels for aligning businessmodels and SCM IS onto changing environmen-tal conditions.

The research objective: The main objectiveof this qualitative research was to explore newknowledge and methodology for the strategicalignment of SCM IS. For this reason, a generic,capability-based model being developed thatis grounded in theoretical evidence by keyconstructs of leading researchers in the fieldsthat have been integrated to get a holisticview and a detailed view of strategic alignment.The model should help companies to identifycases of misfit and the context-specific set of IScapabilities for SCM on multi-levels that lead tostrategic fit and superior SC performance. Em-pirical evidence of the model’s transferability tocompanies in the steel industry was searchedby testing it at two companies that havebeen selected as polar types by a traditional

and a highly innovative company (Koulikoff-Souviron and Harrison, 2005, pp. 270–271).There is evidence that firms have successfullyattained higher profitability through IS, en-abling revenue growth (Mithas et al., 2011,pp. 237–243, 248–253) and IS executives areusing IS-based resources to increase competitiveadvantages, by aligning IS plans with businessplans (Kearns and Lederer, 2000, pp. 265–270). However, there is an observation thatmassive IS investments in advanced economieswere not adequately reflected in the resultedbusiness performance since the 1990s (Zukiset al., 2008, p. 5). Brynjolfsson calls this phe-nomenon as the IT Productivity Paradox, whichrefers to unused potentials from IT investments(Bashiri et al., 2010, p. 2; Brynjolfsson, 1993,pp. 67–75; Pinsonneault and Rivard, 1998).The study explores the causes and reasonsof the phenomenon in the sample industryand develops the model useful for identifyingthese and prevent organisations from suchmiss-investments. Finally, this study exploresknowledge and methodology for the strategicalignment of SCM IS utilising possibilities ofemerging digitalisation technology such as ofIndustry 4.0 and Artificial Intelligence (AI).The study aimed to support autonomy instrategic alignment by adopting its ontology toAI-based concepts. For providing the context,the building blocks of digital business modelsand their enabler for dynamic capabilities (DC)for SCM have been explored.

The addressed gap in the literature: A 2016systematic review about business-IT alignmentby Spósito et al. (2016) concludes that there aremany new ideas developed in the field, but withless development forward on existing constructsless and less empirical evidence from adoptionin practice. For closing this gap, a concisestrategic alignment model has been developedby combining key constructs of leading re-searchers; and grounded it in empirical evidenceby applying and testing in at two global, leadingcompanies in the steel industry.

The research question and objective: Foraddressing the stated research objectives, thefollowing research question and main objectivehave been implemented:

Dynamic Alignment of Digital Supply Chains Business Models 43

• RQ: How, and to what extent does acapability-based model support the degreeof strategic alignment of SCM informationsystems?

• RO: Explore, how, and to what extent doesa capability-based model support the degreestrategic alignment of SCM informationsystems.

The research sub-objectives: For working outthe main research objectives, the following sub-objectives have been implemented:

• RO1: Develop a model for a holistic and de-tailed assessment of the strategic alignmentof SCM IS, and test it at companies in thesteel industry;

• RO2: Explore causes and reasons for thephenomenon of the IT Productivity Paradoxas related to SCM IS;

• RO3: Explore the impact IS capabilities forSCM IS on strategic fit and how to governthese.

2 BACKGROUND AND THEORY DEVELOPMENT

The academic background and used key con-cepts: To compete in today’s dynamic markets,firms must adapt their competitive strategiesfrequently and so need to align their businessmodels and IS to comply with those new rules(Johnson et al., 2008, p. 3; McLaren et al.,2011, p. 909). For this reason, strategic align-ment positively influences IS effectiveness andleads to higher business profitability (Avison etal., 2004, p. 224; Luftman, 2003, p. 9; Porter,1987, p. 7). Previous approaches for measuringstrategic fit or misfit between IS capabilities andcompetitive strategy, adopted by, for instanceMcLaren et al. (2011), Chan et al. (1997),Avison et al. (2004), and Sabherwal and Chan(2001), do not provide measurements on a de-tailed level. For obtain a holistic and a detailedview of IS capabilities’ impact on strategic fitand business performance, different approachesof leading researchers have been combined intoa new model. The model combines a profile-deviation approach (used by McLaren et al.,2011, pp. 918–919; Sabherwal and Chan, 2001,p. 13; Doty et al., 1993, p. 1198) with a cross-domain measurement approach (used by Avisonet al., 2004, p. 230; Henderson et al., 1996)to enable assessing second-order effects of IScapabilities across SCM domains, which arecalled spill-over effects by Tallon (2012). Theconfigurational theory has been adopted to theextent as capabilities can be assessed onto theirfit to each other, which related to Mintzberg’spostulation “for being maximally effective, or-ganisations must design configurations, those

are internally consistent and fit in multiplecontextual dimensions” (Doty et al., 1993,p. 1198; Mintzberg, 1978, pp. 941–943).

A robust model for managing fit of SCintegration antecedents: In referring to thecontingency theory, “there is no universallysuperior strategy or way to manage in a givenenvironment; instead, the context and structuremust fit together if an organisation is to performwell” (Donaldson, 2006, pp. 20–22; Pennings,1975, pp. 394–395). The contingency theorypostulates that alignment between patterns ofrelevant strategic, contextual, and structuralfactors leads to superior company performanceand can prevent misalignment (Oh and Pin-sonneault, 2007, p. 241; Doty et al., 1993,p. 1196). In referring to the systems perspective,Childerhouse and Towill (2011, p. 7445) statethat SC integration achievements result inbetter performance by optimising an entireSC scenario rather than by optimising eachof the sub-systems involved. They argue thatthrough integration, trade-offs and far-reachingdecisions can be carried out, based on sharedinformation and coordination (Childerhouseand Towill, 2011, p. 7445). Various researchersdefine SC integration by the dimensions of or-ganisational relationships, information sharing,coordination and resource sharing (for examplevan Donk and van der Vaart, 2005a, pp. 99–107;Childerhouse and Towill, 2011, p. 7443). VanDonk and van der Vaart (2005a, p. 100) pointout that the increasing practice of integrationefforts in volatile and uncertain demand situa-

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Fig. 1: High-level research model (an author’s view; encases Miles’ and Snow’s strategy types, the SAM dimensions(Henderson et al., 1996), and the MSF model (McLaren et al., 2011)

tions will limit the adverse effects on SC perfor-mance. Many authors emphasise integration asan essential dimension of SCM, but the scopeand context and their views vary considerably,and the objective of SC integration is notuniversally accepted as an ideal state and isnot a question of ‘high integration fits all’(Godsell, 2008, p. 24; Childerhouse and Towill,2011, p. 7445). For this reason, a robust, genericmodel is needed for manage the strategic fit ofSC integration and their antecedent capabilitieson different levels and multiple dimensions; toidentify context-specific information about SCintegration and SCM IS fit in detail.

Building on components of proven concepts:As highlighted in Fig. 1, the theoretical frame-work combines concepts of McLaren et al.(2011) and Sabherwal and Chan (2001), Avisonet al. (2004) and Henderson et al. (1996), whichprovide the foundation on which developmentsof the present study is based.

Competitive strategy types and levels ofsupport to fit: According to Miles and Snow,each of the archetypes Defender, Prospectors,Analysers, and Reactor shows an internallyconsistent pattern of competitive strategy,wherefore Conant et al. (1990) developed an 11dimensional measure involving product-marketbreadth, success posture, surveillance, growth,process goals, competency breadth, adaptabil-ity, administrative focus, planning, organisa-tional structure and control (Miles and Snow,1978; Conant et al., 1990, pp. 367–370; McLaren

et al., 2011, p. 916). These unique patterns ofresponse can help to determine a company’sstrategy type, according to Miles and Snowsarchetypes. Moreover, McLaren et al. (2004,pp. 52–58) found out that Miles’ and Snow’sarchetypes show patterns of ideal levels of sup-port for IS capabilities for SCM that are neededfor strategic fit. They identified that businessescategorised as Prospector or Defender show ahigher performance when key IS capabilities fitthe theoretical ideal for their archetypes, whichhas critically reviewed by the study.

The concept of capabilities: In referringto Grant’s (1996a) theory of the capabilityhierarchy, capabilities have been assessed ondifferent levels of aggregation, and higher-levelcapabilities are integrated by involving lower-level capabilities such as specific knowledge,as illustrated in Fig. 2 (Grant, 1996a, p. 337).The higher the level of the capabilities inthe hierarchy and the more aggregated thecapabilities are, the more far-ranging cross-functional integration is needed; for example,new product development incorporates partic-ularly wide-ranging integration (Grant, 1996a,p. 377). Capabilities at the highest level in thehierarchy directly support the ultimate businessstrategy regarding positioning the organisationin the target market. Hence, Venkatraman’s(1989a) STROBE measures were used at thehighest level of the capability hierarchy forassessing the strategic impact of SCM capabil-ities.


Fig. 2: Strategic fit of different levels (an author’s view, encasing Grant’s capability hierarchy)

Dynamic capabilities: The theory of dynamiccapability, as an extension of Edith Penrose’stheory of the resource-based view (1959) isdescribed as a firm’s ability to integrate, build,and reconfigure internal and external compe-tencies to address rapid change in businessenvironments (Teece et al., 1997, pp. 515–516).DCs are potentials for innovative capacitythat enable firms to implement highly effectivestrategic alignment by reconfiguring their capa-bilities to sustain competitive advantage in fast-changing business environments (Teece et al.,1997, p. 511) through rent creating mechanisms(Makadok, 2001). The present research exploreshow DC related to SCM IS can be managedin an integrated and holistic way for dynamicalignment of SCM IS and in an ambidexterityway for exploration and exploitation (O’Reillyand Tushman, 2007).

Strategic alignment by IS capabilities forSCM: For exploring IS capabilities of SCdomains, strategic alignment has been divided–as an adaptation of Venkatraman’s definitionof strategic alignment–into two components:(1) the concept of ‘strategic fit of SCM’,which represents the measurement of fit of IScapabilities with regard to supporting the firm’scompetitive position, and (2) the concept of‘functional integration of SCM’, which repre-sents factors that foster the alignment withinthe internal IS infrastructure for SCM. Thedegree of strategic fit of SCM IS is expressedby the levels of support IS capabilities for

SCM offing on strategic fit and the levelsof functional integration. For consider SCMprocesses relevant to the steel industry, all SCMdomains have been incorporated to identifythe levels of support of IS capabilities forthe strategic fit of steel firms’ supply chains.Moreover, IS capabilities for SCM have been ex-plored about their ideal levels and their actuallevels of support to strategic fit. Capabilitiesembody a firm’s qualifications for effectivelycombining resources for creating and sustainingcompetitive advantages knowledge integrationfrom multiple sources across the supply chain(Wu et al., 2006, p. 502; Amit and Schoemaker,1993, pp. 33–44; Grant, 1996b, pp. 115–116).

High-order SC capabilities as antecedents ofSC integration: The literature suggests thegrowing significance of SC integration andcollaboration with channel partners throughoutthe supply chain to secure business opportuni-ties, and to focus on operations’ effectivenessand efficiency. Wu et al. (2006) proposedthe high-order SC capabilities information ex-change, coordination, activity integration andresource collaboration for supporting cross-functional and inter-organisational activitieswithin the domains of SCM (Wu et al.,2006, pp. 493–495). Hence, these high-ordercapabilities and further such as responsivenessand agility are uses as antecedents of SCintegration and strategic fit in the presentstudy as they allow aggregated measurements ofoperational SC capabilities across organisations

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Fig. 3: SC integration antecedents (an author’s view, encasing Wu et al.’s 2006 SC capabilities)

(Simatupang et al., 2002, pp. 291–306; Wu etal., 2006, pp. 494–495). Fig. 3 illustrates theSCM capabilities discussed and their relationto SC integration and finally to SC perfor-mance. Fig. 2 shows that these high-order SCcapabilities are seen placed on a high levelin the hierarchy and supported by functionalcapabilities implemented by SCM IS. Businessperformance and strategic alignment can beseen as supported by these high-level SC ca-pabilities, as shown below.

Strategic alignment through the right levelsof integration: SC integration capabilities arekey drivers of business performance in today’sbusiness environment (Wu et al., 2006, p. 495;Shaw et al., 2005, p. 3497; Wang, 2011, pp. 42–43; Liu et al., 2013, p. 1453), but their requiredlevels depend on several situational factors(Godsell, 2008, p. 24). SC integration seemsto refer to the orchestration of other SCMcapabilities, such as information exchange, co-ordination, and resource collaboration, whichleads to the question of the right levels to fitof these antecedent capabilities. As McLaren etal. focus on the generic set of five higher-levelIS capabilities, the necessity to enrich the modelwith capabilities for SC integration is seen, toobtain useful results that support organisationsin strategic alignment in today’s demandingbusiness environment.

Measuring SCM IS’ strategic fit using aprofile deviation approach: Because both com-petitive strategy and IS capabilities are multi-dimensional constructs, to operationalise theirstrategic fit requires investigation of a consider-able number of contingency relationships unlessa configurational approach is used (McLaren

et al., 2011, pp. 915–916; Sabherwal and Chan,2001, p. 13). Therefore, a profile deviationapproach has been used for integrating mea-surements of IS capabilities, on different levels,on their impact on the dimensions of strategicfit. For this reason, capabilities that are or-ganisationally and functionally related to theprocesses of an SC domain were measured usingprofiles. Fig. 4 shows how the profile deviationapproach has been adopted for assessing thestrategic fit of SCM IS. Profiles of IS capabilitieswith theoretical ideal levels – derived from thebusiness strategy – are compared with profilesof IS capabilities with actually-implementedlevels. While ideal levels are linked to an organ-isation’s business strategy, actual levels of sup-port are typically a result of the transformationof the IT strategy. As stated by Prieto and deCarvalho (2011, p. 1409), to possess capabilitieswith levels of support to strategic fit andnot exhaust these for competitive advantageor for generating growth means they will beautomatically wasted. Therefore, capabilities’actual levels of support that are higher thanthe needed levels are seen to have a negativeimpact on the overall level of strategic fit. TheEuclidean distance method has been used forcalculating vectors of capabilities’ degree of fitas it was validated by previous research such asof McLaren et al. (2011). With this approach,a measure as an indication of the strategicfit of the whole SCM IS, and measurementsfor the SCM domains and SCM processes canbe calculated. By this means, measurementsof strategic misfit have been worked out usingidentified levels of misfit identified for detailedIS capability.


Fig. 4: A configurational theory-based concept for measure SCM IS’s strategic fit (an author’s view, adapted fromSabherwal and Chan, 2001)

Differences and similarities of the modelto previous models: McLaren et al. inspiredthe present research with their 2011 MSFmodel. However, they give no description of themeasurement model’s content that is needed toidentify the relevant IS capabilities, and theystated neither full IS capabilities for neitherSCM nor anything about which domains theycome. Moreover, the measurement instrumentfor analysis needs to be developed in the fieldstudy for each case individually. McLaren et al.used a questionnaire originated by Conant etal. (1990) to determine the firm’s competitivestrategy type and a justification table for iden-tifying ideal levels that need the be provided bythe generic capabilities. A reasonable measuringinstrument needs to be developed for eachstudy individually by identifying IS capabili-ties relevant to the case. The IS capabilitiesidentified need refer to the generic capabilitiesgiven by McLaren et al., which is a prerequisitefor integrating the case-specific measurementinstrument in the MSF model. Thus, fromthe present study’s point of view, McLaren etal. delivered a useful framework for buildingan applicable measurement model. For obtaina holistic assessment of SCM IS impact onstrategic fit, the measurement model has beenenhanced using concepts of leading researchers.

Proving McLaren et al.’s classification sys-tem: For proving the classification-system ofpredefined levels of support to fit by five genericSC capabilities pronounced by McLaren et al.,their level of support for strategic alignmentneeds to be compared with the ideal levels ofsupport identified by the present study. In theirresearch, McLaren et al. (2011, p. 914) empha-sise IS capabilities for SCM that are relevantfor a firm’s strategic fit as follows: (i) opera-tional efficiency; (ii) operational flexibility; (iii)planning; (iv) internal analysis; and (v) externalanalysis. Furthermore, they disclose ideal levelsof these generic IS capabilities, related to thefirms’ competitive strategy archetypes for sup-porting strategic fit. A cross-domain approachhas been used in the present research to reflectthe structures of the affected supply chainareas and their dependencies concerning bothstrategic integration and functional integration(Avison et al., 2004, p. 230; Henderson andVenkatraman, 1990, pp. 6–28). The IS capabil-ities identified must be mapped to the genericcapabilities stated. For this reason, the genericcapabilities propounded by McLaren et al. havebeen analysed by the present study related tothe following SCM processes identified for thesteel industry: (i) New Product Development,(ii) SC planning, (iii) SC operations, (iv) Rela-tionship Management, and (v) SC PerformanceManagement.

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3 RESEARCH METHODOLOGY AND CASE STUDIES

As the nature of this research is to develop anew methodology and proof it in specific busi-ness contexts, a qualitative research methodol-ogy has been select. Qualitative research allowscombining quantitative and qualitative meth-ods such as case studies where the researcher ispart of the sample to be explored. Finally, it isopen to considering facts that were not expectedat starting the research, and, therefore, iswell suited for developing a new theory bylinking data and theory iteratively (Eisenhardt,1989, p. 533; Bryman and Bell, 2003, pp. 424–516; Silva and Hirschheim, 2007, pp. 333–334;Kaplan and Duchon, 1988, pp. 574–583; Yin,2009, pp. 130–134).

Essential theory development based on sec-ondary data: An exhaustive literature reviewhas been conducted to identify key concepts fordeveloping the research question and objectivesand for grounding the research in theoreticalevidence (Yin, 2009, p. 130). Regarding themultidisciplinary nature of the study, a widerange of literature from a variety of fields hasbeen reviewed to select useful ideas. Hence, thefollowing fields were systematically reviewed byfocusing on key researcher:

1. strategic alignment and measurement models– as the main subject; concepts of businessstrategy and IS strategy as central compo-nents of strategic alignment;

2. capabilities and IS capabilities – as acapability-oriented approach has been usedfor assessing strategic fit and maintain DCfor SCM IS;

3. Supply Chain Management – as a methodol-ogy for managing the degree of strategic fitof SCM IS have been developed and tested;

4. Artefacts of IS for SCM – as an objectiveof the research was to develop the modelfor maintaining strategic fit using EAMpractice.

Methodical evidence implied in the theoreticalframework: In referring to Klein et al. (2006b,pp. 88–91) framing/data theory for concept ofmental models a design research approach has

been used for developing the theoretical frame-work and deriving the measurement model andmethods. The critical constructs introducedabove have been integrated so as to feature thefavoured characteristics by the measurementmodel holistically, but, arriving it as simpleas possible. Moreover, the methodology had toenable participatory design by experts of thesubject and members of the target practice(Blomberg et al., 1993, pp. 123–150). The-ory triangulation, as described by Yin (2009,p. 116) has been added to the research theoryby investigating different complementing andrevival concepts of strategic fit and throughsynthesising useful concepts of different keyresearchers in strategic alignment (Yin, 2009,pp. 130–134). The synthesis brings the findingstogether and leads to the development of theresearch question and the research objective.In referring to Yin’s (2009, pp. 130–162) logicmodels for increasing case study evidence, themain building blocks of the present researchmethodology are the literature review andsynthesis – in order ‘to rely on proven theoreticalpropositions’ – form the basis for the devel-opment of the research question and objectiveand the development of the research theory andmethodological framework – in order to developlogic models.

The rationale for case study research: Whilecase studies in SC integration make it hard togeneralise findings, specifically if there is noclear theoretical framework supporting these,surveys incorporated only limited aspects ofintegration and fail to consider what actuallyhappens in SC relationships and to addressthe context or business conditions (van Donkand van der Vaart, 2005b, p. 32). They suggestthe use of a multi-case study for research inintegrative practice to bridge the gap betweensingle case studies and surveys, and to developknowledge in the field in its prevailing stage(van Donk and van der Vaart, 2005b, p. 33).Hence, three case studies were used as primarysources in order ‘to use multiple sources ofdata’ for developing the final research theoryand for testing the strategic fit measurement


model. Therefore, the developed model has beenapplied to companies in the steel industry, andsemi-structured interviews have been used forcollecting data from case study participants tothe research objectives.

Exploratory field study at SAP instead ofa pilot case study: A pilot case study istypically used for exploration-based research,which helps to test and refine the basic researchtheory and data collection plans (Yin, 2009,p. 92). Hence, the scope of a pilot case studycan cover both substantive and methodologicalissues and helps to refine relevant questions andconceptual clarification of the research design.For reviewing the research theory and collectingqualitative data, a case study at the author’semployer SAP SE has been used instead of apilot study as SAP is a leading vendor for SCMsolutions for the steel industry and takes care ofthe sectors’ challenges and needs. Moreover, theauthor has access to SAP’s field organisationson a global basis. Hence, an exhaustive casestudy within the SAP organisation has providedrich information about the industry relatedto the research objectives rather than using asingle, industrial sample of a pilot case study.As a core objective of the case study at SAP,the entire concept of the model had beenreviewed. Moreover, the model’s content hasbeen developed by identifying the right set ofIS capabilities for SCM processes relevant to thesteel business and prepared these as an industrytemplate. For this reason, IS capabilities haveidentified by expert discussions on an individuallevel required to point out the industry andorganisation specific vital differentiators thatare relevant for a strategic fit. The case study atSAP has been conducted by session sequenceswith each of 12 experts. Comprehensive worksessions were conducted with each of the par-ticipants, including semi-structured interviews,and the validation of the case study outcome.Leading experts who developed SCM solutionsfor all manufacturing industries were involved,and, therefore, were able to assess the researchtheory regarding its generality and adaptabilityto other industries. More than 20 additionalexperts have been consulted for in-depth discus-sions of topics identified during the case study.

Five reports (each between 12,500 and 18,000words in length) have created as the outcomeand signed by the experts. These reports haveserved as raw data for the findings arrived.

The rationale for sample selection and par-ticipants of industrial case studies: The sampleorganisations are both global players based inAustria and Germany, with subsidiaries andinternational involvement around the globe.Moreover, both steel companies are engagedin both high-end product segments and low-end ones. The Austrian steel company is wellknown for highly innovative involvement inboth collaborative product development con-cerning high-end products, and in driving ISinnovations. In referring to Kuolikoff-Souvironand Harrison (2005, pp. 270–271), the presentstudy goes for polar types in sampling thehighly innovative Austrian steel company (A),that offers high-end products to the automotiveindustry on the one hand, and in sampling thetraditional German steel company (B) with astable product portfolio for the packaging in-dustry on the other hand. Hence, the polar typerationale is seen in the products the companiesoffer and the resulting different characteristicsin their SCM processes. Both organisations arelarge steel producers and have to plan andsynchronise their supply chains on a globalscale. The industrial case studies have beenconducted by session streams as well, wheresix participants including the companies CIOsbelonged to the core teams, and additionalparticipants from the companies’ business andIT teams contributed to the assessment of theSC domains they are managing and consulting.Finally, the companies’ senior management con-tributed to the identification of the companies’competitive strategy type.

Qualitative data analysis using content anal-ysis: For testing the model by validatingmeasurements against the qualitative data col-lected, the directed content analysis approach,according to Mayring (2014) has been used. Therecorded interview data have been transcribedcarefully and fitted into a frame (measurementmodel) of predefined categories of capabilities soas to triangulate these against the quantitativecalculated levels of strategic fit. The rationale

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Fig. 5: The case study data collection and analysis process (an author’s view)

for the directed content analysis approachesdue to Hsieh and Shannon (2005, pp. 1281–1283) is based on the complexity of SCM data,as complex SCM processes and their relation-ship to the research objective was analysed,and terms of SCM are often used differentlyin individual organisations. Both categorisingapproaches, according to Mayring (2014), havebeen adopted. The inductive category formationhas been used for identifying and weighingnew categories and content for developing in-depth findings to the research objectives. Thedeductive category application was used bymanually fitting interview data meaningful topredefined categories for the reason to provethe measurement model on construct validityand triangulating the qualitative data with themeasurements as mentioned above.

3.1 Strategic Fit Assessment andInterim Result

Competitive strategy type assessments: As a firststep, the scope of the strategic business areasand the relevant SCM processes to be assessedwere determined by joint sessions. Based onthat scope, the strategic fit measurement wascalibrated using the prepared template. As asecond step, the questionnaire of Conant etal. (1990) has been filled out by the seniormanagement for identifying the strategy typeaccording to Miles and Snow. The identifiedstrategy type has been used to determineideal levels of higher-order SCM capabilities,

according to McLaren et al. (2011). The resultsof the strategy type assessments do not showunique strategy types for all rated businessdimensions. It shows the business of company A(Fig. 6) categorised by the leading strategytype Defender and by the second strategytype Protector, which reflect the company’scharacteristic on a high level.

A hybrid strategy with strong defender focusand strengths in innovation: The questionnaireresponse was also reflected by the result ofthe semi-structured interviews, which leads tothe following view: the sample organisationshows a predominantly defender strategy typein a stable market position and steady marketgrowth with a strong focus on high-end prod-ucts, and, therefore with significant strengthsin engineering. This prospector position withengineering competency breadth is reflected bythe activities for searching business opportuni-ties as an innovator in the market. Accordingto the organisation’s engineering strengths,new market opportunities result mostly frominnovation offered from a stable customer base.At the same time, along with this prospec-tor/innovator position, the sample organisationshows strong defender strategy characteristicsfor securing margins and a stable financial posi-tion through operational excellence. Because ofthe stable defender strategic position and theprospector position in engineering that allowsscreening the market for new opportunities, theproduct-market focus shows an analyser strat-egy type. Based on the preparatory steps, the


Tab. 1: Response to strategy type questionnaire of company A (Conant et al., 1990)

Businessdomain Dimension Rated characteristics Strategy

typeEntrepreneurial Product–market

focusIn comparison to competitors, our products are stable incertain markets while innovative in other markets.

Analyser

Marketleadership

In contrast to competitors, we have an image in themarketplace by offering selective products of high quality.

Defender

Marketsurveillance

We are continuously monitoring the marketplace. Prospector

Market growth In comparison to competitors, the increase or losses indemand are possible from concentrating more fully ondeveloping our present markets.

Defender

Engineering Process goals One of our most important goals is our dedication andcommitment to keep costs under control.

Defender

Competencybreadth

Our managers have broad entrepreneurial skills, beingflexible, and enable change to be created.

Prospector

Infrastructureadaptability

The one thing that protects us from competitive failure isthat we can do a limited number of things exceptionallywell.

Defender

Administrative Administrativefocus

Our management tends to concentrate on a secure financialposition through cost and quality control measures.

Defender

Planning Our organisation prepares for the future by focusing onproblems that, if solved, will maintain and improve ourcurrent offerings and market position.

Defender

Org. structure In comparison to our competitors, the structure of ourorganisation is product-market oriented.

Prospector

Control Unlike many of our competitors, we use decentralisedprocedures to evaluate our performance, and manymembers are involved.

Prospector

strategic fit measurement has been conducted.Initial sessions have shown that the assessmentdelivered fruitful quantitative results for therating of capabilities’ levels to fit.

Strategic fit assessment: Each relationshipbetween the IS capability provided by theSCM IS and the high-order SC capabilityneeded to support fit has been rated with thefollowing levels: ‘high’, ‘medium’, ‘low’, and‘not relevant’. Besides, support of IS capabilitiesto fit through SC capabilities of other SCMdomains have been considered for assessingsecond-order effects from such spill-over effects.Original assessment sheets of SC domains usingthe Euclidean distance method for calculatingstrategic fit are shown in the Appendix. Besides,rich qualitative information has collected aboutthe contribution of SCM IS capabilities to fit forthe different domains and causes and reasons forthe IT productivity paradox. Finally, the model

itself has been rated using a five-point Likertscale on the appropriateness for measures thedegree of strategic fit of a steel firm’s SCM ISwhen considering their competitive strategy.

Strategic fit measurement and analysis of themodel: For calculating the strategic fit ratiousing the Euclidean distance method, the ratedlevels have been replaced using the followingnumeric values: 0 for ‘not relevant’, 1 for ‘low’, 2for ‘medium’; and 3 for ‘high’ levels of supportof fit. The means of ideal levels and actuallevels of support of fit have been calculatedfor each combination of IS capability and high-order SCM capability as derived from theassessment sheet and figured out in Tab. 2. Theaggregated ideal levels show the significanceof the S&OP domain’s capabilities to strategicfit. The same procedure has been used forcalculating aggregated levels of support by IScapabilities to strategic dimensions according to

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Tab. 2: IS capabilities for S&OP support of fit (case study A)IScapability

Ideallevel

Actuallevel Fit Quotation or

paraphrased quotation Paraphrase Paraphrasecategory B Related KPIs

Demandplanning

2.3 2.0 1.7 “One of the most significantspill-over effects of demand predictionis the indirect, but the clear impact onresources balancing for productions ofdifferent segments. High levels ofvisibility in activity integration arethe result contributing to smoothoperations, SC synchronisation andcoordination.” (PA1)

Demand predictionhas an impact onwell-utilisedresources andindirectly on howwell the activitiesare integrated forfulfilling theexpected demand.

Enablement:The reachedaccuracy is aresult of highSC modellingandconfigurationefforts in DPand S&OP.

1. Forecastaccuracy

2. Profitability3. OEE and plant

utilisation4. Delivery

adherence5. Material &

resourceavailability

6. Transportationsadherence

Demandreview

2.2 1.9 1.7

Demandalignmentwithoperations

1.9 1.7 2.0

Real-timevisibilityof demandchangesacross SC

2.6 2.1 2.2 “Increased visibility of demandchanges’ impact on material flow hasimproved customer-order due-dateadherence, reliability, and improvedoperational excellence as well.” (PA1)

Increased SCvisibility improvesoperationalexcellence anddelivery adherence.

SCmodelling

2.4 1.7 2.6 “Simulation capabilities supportstrategic decisions, pro-activeness,risk mgmt. also, increase SC agilityby better decisions.” (PA1)

Capabilities for modelling andsimulating different businesssituations and contradictingobjectives are vital drivers.Plan

simulation2.4 2.2 1.4

Venkatraman’s (1989a) STROBE measures, asshown in Fig. 2. Finally, the strategic fit indi-cators that are provided by the IS capabilitiesof the S&OP domain have been calculated asan aggregated measure of each SC capability.Strategic fit indicator values between 0 and 1indicate a high level of fitness to the strategyof the measurement capability combination.Levels between 1 and 3 indicate the extent ofa misfit. By this approach, the strategic fit hasbeen identified at an overall level and individuallevels of IS capabilities’ impact onto fit throughhigher-order SC capabilities.

As shown in Tab. 3, the average ideal level of3.0 and the actual level of 2.95 for ‘business pre-dictability’ underpin the strategic significanceof this high-order capability. Most capabilitieswith an aggregated value for the ideal levelabove ‘2’ show significance to strategic fit aswell. Moreover, the levels show that the coreobjective of predicting the business for the long-term has fulfilled to a high degree by the imple-mented IS capabilities. High levels of support ofIS capabilities for ‘exchanging information’ and‘coordinating related activities’ with customersand internal parties are needed to predict theforecast reliably. Potentials for improvementsto fit were identified for demand managementregarding ‘SC responsiveness’ and ‘SC agility’.

However, the main objective of the S&OPprocesses is to develop a plan based on knownand expected demand, while considering ex-pected constraints in supply, such as downtimes

for resource maintenance, so that expecteddemand can be fulfilled. This main objectiveis rated with high levels for the capability‘coordination’ through IS capabilities of de-mand planning, which is indirectly fulfilledto a high degree through spill-over effects onthe operations processes of the upstream anddownstream domains. These secondary levelsare also represented for the SCM capability‘coordination’ by IS capabilities for demandalignment that also have a positive indirecteffect on ‘operational efficiency’. Tab. 2 showsthe aggregated result of the strategic fit mea-sures from the perspective of the IS capabilitiesfor S&OP which has been derived from thecolumns ‘fit ratio’, ‘average degree of ideallevels’ and ‘average degree of actual levels’of the assessment sheet figured out in theAppendix.

Identified potentials for improvement to fit(misfit): The most significant deviation betweenideal and actual levels has identified for ‘ac-tivity integration’ for aligning forecast withproduction management. Hence, the analysisshows significant potentials for improvement in‘activity integration’ between involved parties.Demand alignment with production manage-ment, procurement and logistics represent acore function of central planning. High levelswere rated for demand alignment capabilitiesfor providing the required levels for ‘SC respon-siveness’ and ‘SC agility’. These requirementswere fulfilled to a medium level by the actual


Tab. 3: IS capabilities’ contribution to fit from S&OP (case study A)SCcapability

Ideallevel

Actuallevel Fit Quotation or

paraphrased quotationParaphrase(category A)

Paraphrase tocategory B KPIs

Businesspre-dictability

3.0 2.9 1.0 Business predictability is fulfilled tohigh-level for the long-term.Predictability on characteristics levelsis essential. A significant spill-overeffect is the indirect balancing ofcritical resources, which reducesefforts in coordination.

Prediction oncharacteristics levelsis of high relevancewith indirect impacton balancing criticalresources among thesupply chain.

S&OP capabilitiesprovide businesspredictabilitycapabilities. Theaccuracy is reachedby high efforts of SCmodelling in S&OP.

1. Forecastaccuracy

2. Revenue3. Profitability4. OEE & plant

utilisation5. Delivery

adherence6. Material and

labouravailability

7. Transportationsadherence

Coordination 2.9 2.8 1.7 High levels of ‘information exchange’and ‘coordination’ are needed fordemand collection & supply alignment.

Contribution to fitfrom S&OP relatesto a high degree onstakeholders’collaboration foraligning demand andsupply.

Collaborationcapabilities fordemand alignmentenabled by controland SC modellingfor alignmentworkflows.

Informationexchange

2.8 2.7 1.7

Activityintegra-tion

2.7 2.3 3.0 Misfit in ‘activity integration’between demand management andproduction planning.

Operationalefficiency

1.8 1.7 1.0 The indirect impact of DP capabilitieson the coordination of manufacturinghas a positive effect on ‘operationalefficiency’.

S&OP capabilitieshave a positiveimpact onoperationalefficiency.

The second-ordereffects of S&OPenable high levels ofoperationalefficiency.

SCrespon-siveness

2.2 2.0 2.2 Medium levels of support to fit by ‘SCresponsiveness’ and ‘SC agility’underpin the misfit in activityintegration between S&OP &production.

High levels of integration between demandmanagement and production planningprovide high levels of SC responsiveness(coverage and enablement by modellingthe right levels).SC agility 2.2 2.0 2.2

SC riskmgmt.

2.2 2.1 1.4 Plan simulation helps to analyse options against expected risks by improved visibility.

capabilities. The potential for improvementsin ‘SC responsiveness’ and ‘SC agility’ fromdemand alignment underpin the misfit in ‘ac-tivity integration’ capabilities between demandmanagement and production planning. Theidentified levels of the misfit in ‘activity integra-tion’, ‘SC agility’ indicate that improvements inIS capabilities for optimising, simulating, andvisualising plans can help to increase levels of fitbetween demand management and productionmanagement. Fig. 6 shows the impact fromlevels of a misfit onto SC planning by mod-elling and simulations capabilities and internalalignment by activity integration, SC agilityand SC responsiveness. Moreover, it showsalso high levels of a misfit in the visibilityof supply chain end-to-end processes. ModernIntegrated Business Planning (IBP) solutionscan be evaluated for improving the strategicfit of SC visibility and alignment. Such IBPsolutions emphasise on the collaboration ofstakeholders involved in the overall planningconsensus process.

The high levels of a misfit in activity integra-tion, SC responsiveness and SC agility indicateneeded improvements in collaboration betweenstakeholders and transparency of the planningprocess. On the other side, the level ‘1’ of fitin operational efficiency shows that the S&OP

processes have a very positive impact on theefficient utilisation of the production assets byspill-over effects. The level ‘2’ for coordinationand information exchange and the vast extentof misfit of activity integration with the value‘3’ indicates the need for improving SC vis-ibility and stakeholder collaboration as well.Alignment of changes in customer demand withthe available production capacity in the mostprofitable way is a highly demanding task forall involved parties for optimising contributionmargins. Hence, there are remaining potentialsfor improvements in profit-oriented plan optimi-sation. Moreover, there were improvement po-tentials seen for increasing SC transparency byvisualising the impact of demand dynamics onproduction, supply, and Financials, throughoutand in time. The assessment shows that the IScapabilities support the sample organisation’sbusiness strategy to a high degree throughthe SC capabilities. The direct impact of IScapabilities to the strategic dimension due toVenkatraman’s STROBE measures is visiblewith significance for ‘riskiness’ and ‘defensive-ness’.

Strategy type of organisation B: There wasa collective sense among the case study par-ticipants and the company’s managing boardabout the view of the company being in a

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Fig. 6: Levels of SC misfit from IS capabilities for S&OP at company A

strong Defender strategy position with char-acteristics and behaviour of an Analyser inobserving the market for new opportunities forsecuring the position in a long-term perspec-tive. The response to the questionnaire alsoreflects this strategic positioning. According tothe discussions, the sample organisation hasa robust product portfolio but is very activein searching new customers in new geographicmarkets to sustain the competitive position,which underpins the Analyser characteristics inmarket surveillance.

The strategic relevance of company B’s SCMprocess and their fit: The processes of theupstream and downstream domain, the S&OPdomain, and the Relationship Managementdomain, have been identified as strategicallyrelevant for securing the company’s Defenderstrategy position through high levels of effi-ciency and agility. These processes have toprovide the necessary conditions for ‘SC re-sponsiveness’ and ‘SC agility’ by alignmentactivities and active information exchange withcustomers. Backlog management has been iden-tified as the backbone process for securingeffective asset utilisation and for satisfyingstrategic customers’ demand reliably and withthe required profitability at the same time. Thebacklog management process bases primarilyon collaboration between sales and productionmanagement and requires underpinning inputsfrom demand management and relationshipmanagement processes for preparing the nec-

essary conditions for effective demand align-ment by information exchange with customers.Hence, backlog management’s effectiveness is,based on high levels of ‘activity integration’ be-tween demand management, relationship man-agement, and operations. It helps the companyto fill customer orders highly effectively andefficiently by its use of supplying organisa-tional parties. Moreover, these strengths sup-port highly responsive changes in the orderbacklog such as through very high levels ofintegration of collaborative activities betweeninvolved parties. For that reason, its strengthin this core SCM process helps the companyto secure the Defender strategy position byproviding operational excellence in efficiencyand agility. Also, the flexibility described inthe planning and operations processes allowsthe company to act in new markets by takingchances on new opportunities based on the ex-isting product portfolio. Based on the maturityof the company’s processes, high-levels of fitof IS capabilities for SCM were identified ingeneral. Nevertheless, significant opportunitiesfor improvements were identified for SCM coreprocesses as summarised below.

Improvement potentials from identified levelsof a misfit at company B: High potentials ofimprovement were identified for the SCM ac-tivity integration within the company’s parties,using IS capabilities of demand managementand production/inventory management. Thesame levels of improvement were identified


for activity integration with external channelpartners, such as the parent company formanaging raw material supply. Moreover, fur-ther opportunities have been identified aboutoperational efficiency and flexibility of inter-nal and external transportation managementactivities. Because active and efficient backlogmanagement with high levels of integrationamong the parties involved’ has been identifiedas core strength, improvements were estimated

instead from predecessor processes such asdemand management, since demand is man-aged manually. For this reason, support byIntegrated Business Planning (IBP) capabilitiespromises to support the operational backlogmanagement process without losing flexibility.Moreover, the assessment shows that increasedtransparency of SC optimisation potentials cansupport SC integration, SC risk managementand operational excellence.

4 FINDINGS AND DISCUSSION

4.1 Commonalities and Contrasts ofthe Assessed Business Models

Context-specific profiles of IS capabilities andlevels of support needed to strategic fit: Forboth companies, the actual implemented andideal levels of SCM IS capabilities to supportstrategic fit of their SC business models wereidentified considering context-specific implica-tions. Moreover, levels of a misfit from SCintegration and antecedent capabilities havebeen calculated that expresses the impact ontobusiness strategy. A holistic picture of SCMIS strategic fit from IS capabilities’ effect onprocess levels and second-order effects withinand across SC domains has been yielded fromtriangulating the calculated figures with thequalitative extracted information. The strategicfit assessments provide the companies with asolid basis for defining qualified and focusedaction plans.

High visibility of contrasting key prioritiesin SC capabilities for SCM: Due to the polartype sample selection, valuable contrasting in-formation has been found and can make visiblesuch as crucial differences in the companies’strategic positioning and the impact on thedifferentiation of there is capabilities for SCM.For instance, where spillover effects from SCplanning to SC operations processes are veryimportant for company A’s strategy adoption,spill-over effects from relationship managementto SC operations are vital enablers for companyB’s strategic strengths as highlighted in Fig. 7.Hence, operational excellence of company B

is based to a significant extent on second-order effects of proper relationship managementproviding high levels of flexibility. The par-ticipants expect significant effort reduction inthe coordination of operational processes fromenhanced collaboration capabilities by modernIBP solutions in the future. Interestingly, thehighly innovative company A needs more de-manding planning capabilities in comparisonto the low-end company. This finding rebutsthe assumption of McLaren et al. (2011) thatcompanies showing Defender strategy typecharacteristics need a higher level of supportto fit from planning capabilities as companiesrelated to an Innovator strategy type. Fig. 7highlights the companies’ second-order effects.

1. High levels of support of fit of high-orderSCM capability ‘coordination’ indirectly byhigh levels of ‘IS capabilities for S&OP’ atsample company A.

2. High levels of support to fit of ‘coordination’indirectly by high levels from ‘IS capabilitiesfor relationship management’ at samplecompany B.

Differences in second-order effects through IScapabilities’ spill-over effects: The case studyat company A affirms the assumption thatsteel companies in the high-end sector, suchas automotive suppliers have very complexSCM processes, particularly in SC planning.On the one hand, they need to deal withvariant configuration throughout their planningand operations processes in the same way asEngineering-to-Order (ETO) producers in the

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Fig. 7: Strategic support from second-order effects of IS capabilities across the supply chain

discrete manufacturing industry. On the otherhand, they have the same logistics and SCMrequirements as the process industry in manag-ing their production flow. To find the optimalplan needs demanding SC planning capabilitiessuch as characteristic based demand planningcapabilities for modelling different levels ofprofit contributions by different customers forthe same product. Such information can be con-sidered by S&OP optimiser solutions to createmaster plans that prefer profitable orders forallocation at available finite resource capacity.However, the planned profitability needs to berealised by operational excellence. Hence, highlevels of support to operational excellence arerequired from SC capabilities such as resourcecollaboration and coordination of upstream anddownstream processes, which are implied fromantecedents of NPD and the S&OP domain.High levels of operational excellence have beenidentified at company B based on second-order effects from high levels of operationalcollaboration with customers. Hence, high levelsof support to fit by upstream and downstreamcoordination are implied by the capabilitiesof relationship management. The participantsexpect reduced efforts in operational coor-dination from enhanced S&OP capabilities.Interestingly, the highly innovative companyA needs more demanding planning capabilities

in comparison to the low-end company. Thisfinding rebuts the assumption of McLaren etal. (2011) that companies showing Defenderstrategy type characteristics need a higher levelof support to fit from planning capabilitiesas companies related to an Innovator strategytype.

Relationship capital as a DC: Collabora-tive NPD using engineers positioned at thecustomers as Key Accounts pro-vide companyA with relationship capital as an absorp-tive capacity, which provides insights on fur-ther demand and developments and enablesto identify business opportunities by verticalforward integration. Collaborative knowledgemanagement and knowledge assimilation actas antecedents of developed DC relationshipcapital, which enables company A to moderatethe business functions innovation management,NPD and SC planning due to business needs.Company A’s strengths in SC planning andsimulations act as antecedents for moderatingoperational excellence providing fast, usefulplan alternatives. Company B’s relationshipcapital provides degrees of freedom in fulfillingpromised service levels on an exceptional basis,and, therefore, acts as an absorptive capacityenable to re-plan and synchronise operationalorder volumes to deal with unforeseen events ina profitable way.


4.2 Assessment of the Strategic FitMeasurement Model

Findings of the model test and review of theapplicability: The model’s appropriateness tomeasure the degree of fit of steel firms’ SCMIS in a concise way was rated by all theparticipants between ‘fits’ and ‘fits very well’ asan average of a five-point Likert scale as shownin Tab. 4.

The approach of rating ‘actual levels’ againstideal levels of IS capabilities for SCM hasbeen approved as very useful for evaluating thefit of an SCM IS to a prevailing strategy indetail. Increased transparency of needed levelsof IS capabilities’ support to fit across end-to-end processes has been particularly usefulvalued, which can be identified using the modeland were regarded by the participants for thefollowing:

1. Transparency of improvement potentials –the strategic fit assessment can point outpotentials where SCM capabilities can bebetter utilised or where constraints at spe-cific points prevent the optimal utilisationof the whole supply chain (TOC analysis).

2. Balanced IS investments based on increasedtransparency – uncontrolled placed IT in-vestments result in a lack of clarity asto whether prerequisites from predecessorprocesses are given, which are required forexhausting the given advantages. Becauseit is difficult for big companies to reachtransparency throughout the supply chainto support the strategy on the right level,the model has been rated as a best practicefor SCM IS alignment in the steel industryand for large-scale enterprises in general.

3. Comparison of the capability approach andKPI measurements – benchmarking usingKPIs helps to improve the business pro-cesses continuously, but it will not help toalign the business and IS systems to fu-ture requirements. The capability approach,therefore, is more oriented toward the fu-ture, for showing structural problems in thebusiness model, and where they can arise incases of strategy refinements.

4. Secondary levels of support of fit throughspill-over effects – the ability to highlightthe essential relationships across SCM do-mains by assessing IS capabilities’ levelsof indirect support for business processesof other SCM domains has been ratedas particularly useful. This feature helpsto increase the transparency of where abusiness performance from given IS invest-ment is exhausted in the supply chain.Hence, the present study’s model helps toassess second-order effects in supply chainscaused by spillover effects, which help forTOC analysis across domains and on ISarchitecture level.

5. Contribution to organisational learning –The development of a common understand-ing of the actual situation of fit acrossall domains and a shared awareness aboutoverall business objectives and the effects ofone’s own efforts to achieve these is seenas very valuable among the stakeholdersinvolved. Hence, the model has been rated asvery valuable and is seen as a best practicefor strategic fit assessment of SCM IS.

4.3 Assessment of the CapabilityConcept and Strategy TypeSchema

The field experts rate the capability conceptand the capability content used by the differentlevels as very reasonable and coherent for inves-tigating their impact on strategic fit (1) at thestrategic level; (2) at the level of higher-orderSC capabilities; and (3) at the specific level ofIS capabilities. Because of the growing volatilityof business conditions in the steel industry,the experts see the high-order capabilities SCagility and SC responsiveness as a moderatorbetween business strategy and IS capabilitiesas particularly important. Furthermore, theyagree on the importance of capabilities such ascoordination, activity integration, and resourcecollaboration as drivers for SC integration. Thedomain shape has been assessed as very usefulfor investigating SCM IS impact from an SCMcross-domain perspective. The structure of themodel has been rated as flexible and generic

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Tab. 4: Validation result of the strategic fit measurement model assessment

Criteria Fit Quotation or paraphrased quotation (extraction) to validation criteriaTo the samplecompany

Fitsverywell

The model has been rated as very valuable for identifying levels of a fit/misfiton an aggregation level for informing senior management and on an individuallevel for creating plans of action. It has been rated as very useful for balancingcapabilities of end-to-end processes across supply networks to avoid ineffectiveIS investment and to increase the speed to fit by focused alignment efforts. Itenables to develop reference capability patterns for different product segmentsand for different business situations to increase agility.

Eligibility toassess thestrategic fit

Fitsverywell

The model fulfils requirements to assess the fit of steel companies’ SCM IS verygood, particularly at different levels, in levering processes of different domains.Different priorities can be considered for processes, capabilities, and domains.

The fit of theassessmentprocedure

Fits The two-step approach has been rated as valuable for identifying areas ofstrategic relevance in a first step, and for defining the required level of detail toassess the fit of SCM IS. Also, to assess fit in a second step. The procedureseems very useful for scoping activities, and for supporting EAM cycles anddue diligence activities.

Profile deviationapproach

Fits The profile deviation approach has been rated as valuable for benchmarkingactual levels against ideal levels. Also, it has been rated as valuable for rating‘actual levels’ of fit against target levels of transition stages/architectures.

Capabilityversus KPIapproach

Fits In comparison to KPI measurements, the capability approach has been ratedas highly valuable and more appropriate to evaluate the fit of levels that reflectfuture business strategies. KPI measures can remain the same after a change inprocesses, but the capabilities can be different for reaching the KPIs after astrategic change.

Organisationallearning

Fits The model’s application can develop a collective view of the participantsneeded levels to support fit and the overall objectives for improving theawareness of members’ contributions to performance. So can create, thereforean aligned flux of action.

SCM domainstructure

Fits The ability to assess indirect effects of IS capabilities help to increasetransparency on where in the supply chain given IS investments are exhausted.Priorities and relationships between capabilities and processes of differentdomains can be well identified and assessed.

Performancedrivers

Fits The model helps to identify critical drivers of SC performance. The assessmentcan deliver valuable information on IS capabilities’ contribution to SCperformance key driver.

Usability of themodel

Fits The usability of the model has been rated as easy to use. However, a commonunderstanding of the SCM capabilities’ meaning is needed.

The generalityof the model

Fits The model’s construct has been rated as highly adaptable to firms in otherindustries as a high degree of generality has been established. However, the IScapability content needs to be adjusted to the prevailing industry needs.

Key points needto be considered

Fits The interpretation of the capability levels (low, medium, and high) depends onthe organisation’s individual strategic needs. For assessing the fit ofcross-organisational end-to-end processes, it is suggested to rely on levels suchas process throughput.

Missing aspects Fits The model focuses on the measurement of fit and misfit, and, therefore, onscoping of alignment requirements. There is no aspect missing.

for companies in other industries to adoptit for measuring SCM IS’s fit the strategy.Hence, it has been thought as reasonable bythe involved experts to establish and maintaina repository for sector-specific measurement

templates. The model has been rated as valu-able for informing senior management as itprovides a quick overview of the right balancebetween strategy and a company’s SC model,and critical gaps. There is a shared view among


the participants that, in comparison to KPImeasurements, the capability approach helpsto identify structural strengths and gaps infirms’ SCM models and provides informationabout how to address these gaps. Hence, thestrategic-fit measurement model has been ratedas very valuable to provide transparency offirms’ capability patterns.

Strategy types and capability pattern of steelcompanies: From their professional experiencein the steel industry, the experts at SAP faceda mix of different strategic characteristics andrequirements with a focus on the defenderstrategy type according to the Miles and Snowarchetypes. However, at the same companies,there are also, in most cases, products and areasof high innovation. They conclude that steelcompanies show, by their product segments, atrend in two types of strategic orientation:1. ‘high-end products’ such as those for auto-

motive customers, which relates to the Milesand Snow strategy archetype Innovator; and

2. ‘low-end products’ such as those for theconstruction industry, which relates to theMiles and Snow strategy archetype De-fender.

The strategy categorisation approach is ratedas very useful for identifying areas with asignificant deviation from the leading businessstrategies. Reference capability patterns canhelp companies to transform strategic changesto business and IS infrastructure more quickly,and, provide enhanced transparency of thecapability levels needed for target architectures.Such artefacts can also support activities forharmonising, adopting and scaling of steelcompanies’ SCM models as part of M&Aprojects. These findings provide evidence of thecontribution to the development of DC andabsorptive capacity provided by maintainingcapability patterns.

Comparing predefined levels to fit of previousresearch against levels from the case studies:McLaren et al. (2011) identified five genericSCM capabilities (operational efficiency, op-erational flexibility, planning, internal analy-sis, and external analysis) with ideal levels(McLaren et al., 2011, p. 918) of support offit in reference to Miles and Snow strategy

archetypes. They compared actual levels iden-tified by strategic fit assessments against thesepredefined levels. By comparison, the presentstudy identified the actual levels and theideal levels for each capability separately. Forassessing the classification scheme of McLarenet al., ideal levels predefined to generic SCcapabilities were compared with ideal levelsof present assessed SC capabilities that referto the former in a highest possible accurateway as presented in Fig. 8. As the strategytype identified by the questionnaire surveyshows a hybrid with a stable Defender positionand significant Prospector strengths, the ideallevels of both strategy archetypes are shownas reference levels for the five generic SCcapabilities.

Context dominates levels of fit by IS capabili-ties for SCM: The review shows high deviationbetween ideal-levels of the five generic capa-bilities identified by previous research and theideal levels of the present study’s capabilities;for example, medium levels of ‘operational effi-ciency’ of the present study A being comparedto high levels from previous research for boththe Defender and the Prospector strategy char-acteristics. The result of this comparison affirmsthe assumption of the present study that ideallevels identified by previous research are notsuitable as reference levels for determining thefit of the steel company in the accuracy requiredto express needed context-specific levels to fit.The review of the classification scheme usingdata from case study B strengthens this findingwith more evidence.

The average ideal levels identified for all SCdomains in case study B show a high correlationwith the ideal levels identified by McLarenet al.’s previous research. However, there arehigh variations in ideal levels for the same SCcapabilities of different SC domains that lead toa core finding as follows.

Core finding to SCM IS fit measurementa businesses’ strategy types: For aligning afirm’s SC operating model onto the businessstrategy, ideal levels of SC capabilities’ supportto strategic fit need to be identified according tothe company’s individual needs rather than touse predefined ideal levels of reference strategy

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Fig. 8: Comparing ideal levels of company A with predefined ideal level by McLaren et al. (2011)

types. Moreover, for identifying ideal levelsof support for companies’ SCM IS’s fit, it isessential to investigate antecedent capabilitiesof SC integration such as SC agility, SC respon-siveness, and activity-integration to considerappropriately business conditions steel compa-nies are facing. Finally, ideal levels of supportto fit by high-order SC capabilities from IScapability need to be investigated separately foreach SCM domain to consider first-order effectsand second-order effects across the supply chainappropriately.

4.4 Causes and Reasons for the ITProductivity Paradox

The declining contribution of IT to productivitygrowth: The steady growth of IT spendingover the last three decades reflects the tenetof technology-enhanced productivity. However,the evidence suggested that IT’s contributionto growth in productivity has been declin-ing since 2001 (e.g. Zukis et al., 2008). Thephenomenon of the gap between projectedand realised performance is referred to as the‘IT productivity paradox’ by researchers suchas Brynjolfsson (1993) and Pinsonneault andRivard (1998). There are several explanationssuch as by Brynjolfsson (1993, pp. 67–75): (1)miss-measurement, (2) redistribution–there areprofits, but they come at the expense of others,leaving little net gain, (3) time lags, and (4)mismanagement–there are no gains because ofthe unusual difficulties in managing IT or in-

formation. A primary objective of research intothe phenomenon of the IS productivity paradoxis to improve the balance of investments in ISand the exploited business value concerning thecompany’s competitive advantage (Bashiri etal., 2010, p. 2). Hence, the causes and reasonsfor the IT productivity paradox have beenexplored for the steel industry, using the quali-tative case study at SAP and the industrial casestudies. The phenomenon has been exploredfrom the perspective of more transparency inthe contribution of IS investments in strategicfit.

Leading causes of the phenomenon in thesample industry: Based on the observation fromcustomer projects in the global steel industry,the experts at SAP for SCM and businesstransformations stated that ‘quick wins’ weremade fast by SCM implementations in theearly 80s and 90s but win behind these ‘lowhanging fruits’ are highly dependent on in-tegration aspects of the SCM processes tothe organisational environment. Finally, ‘talentmanagement’ has been identified as a corechallenge for mastering the complexity of steelcompanies SC processes. The following topissues were identified:

1. insufficient alignment of SC objectives inmultiple tier supply chain networks;

2. insufficient SC modelling use for dealingwith supply chain complexity;

3. insufficient SC integration and end-to-endvisibility caused by heterogeneous SCM IS;


4. lack of project governance as a reason forSCM implementations’ misfit.

Alignment effectiveness by a focus on valuemanagement: One of the main reasons forinsufficient alignment results are fragmentedIS landscapes as a result of M&A and toosmall scopes of IS implementations. Different ISsystems have not only an impact on increasedTCO but, more important, they have differentapproaches to address the business needs andfor reaching the objectives. This phenomenoncan be addressed by increased IT budgets’proportion released for innovation at the sametime, while IT complexity must be managedout and focus needs to be set on businessvalue creation. The technology can addressdrivers and inhibitors of IT value by impactinginnovation and complexity and inflexibility. Forexample, software as a service (SaaS) and Cloudsolutions allow users to ‘pay for usage’ andsignificantly reduce the capital wasted in sup-porting software and hardware. By managingin favour of innovation and against complexity,companies can once again drive value throughIT spending. The following high-level prioritiesare suggested to drive IT value contribution toorganisations:

1. Prioritise IT value management to createdifferentiated business value by focusingsimultaneously on IT costs and – primarily– on sources of competitive advantage.

2. Management out of complexity allows thereallocation of funds and higher levels of ITproductivity for innovation by identifyingcases of a misfit on all IS levels.

3. ‘Management in’ of innovation – the use ofIT for creating innovation most effectivelymay be the best to generate sustainablevalue from IT spending. Emerging digital-isation technology provides new sources ofIS innovation, and Enterprise ArchitectureManagement (EAM) provides a methodol-ogy for adopting the value in time (Ross,2006).

4.5 The Impact of Business ModelTypes onto SC Dynamics

Market-oriented versus sales-driven businesseshave been identified as main differentiatorsregarding who is driving the SC processes andhow the SC dynamics is characterised. Accord-ing to the interviewees, sales-driven businessesare able to adapt their SC model fast tonew requirements from the market. In suchbusinesses, sales can determine the portfolioand the forecast, whereas the production unitshave to follow and fulfil. Marketing-orientedbusiness – where the steel companies belongto – need a taller horizon to plan and achievetheir business as they often cannot adapt theirSC models in the short-term. Hence, they needsophisticated planning capabilities to be ableto make the best of each business situationthat will come with the available resourcesby considering contradicting objectives of thestakeholders. These are such as high plantutilisation that is wished by plant managersbut also prioritised order fulfilment for strategiccustomers that is wished by sales managers,and the senior management focuses on theoverall profitability. Due to these findings, thepresent study draws on the view combiningMiles and Snow archetypes with the marketing-oriented and sales-driven view for categorisingcompanies and their segments regarding theirneeds in SC dynamics and SC modelling. Thex-axis in Fig. 9 shows increasing SC dynamicsbased on adoption activities such as scalingout business models, while the y-axis showsincreasing SC complexity from innovations anddifferentiation.

Balancing market-grow and market-shareusing product portfolio analysis: The Port-folio Analysis provides practitioners with awell-recognised concept for balancing fit of acompany’s portfolio regarding the dimensionsmarket growth and market share. It providesinformation such as the proportion of productswith a high market share but a low growthrate those come to the end of their life-cycle and need to be replaced by innovations.This approach shows which products are atthe end of their life-cycle and needs to be

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Fig. 9: Market-oriented vs. sales-driven business and the impact on SC dynamics (an author’s view)

replaced by innovations. Hence, the portfolioanalysis provides a valuable tool for identifyingareas of innovations and for deriving focuseddevelopment of DC for SCM that promisessignificant value potentials.

Ambidexterity–dynamic-capability for balanc-ing exploration and exploitation: Ambidexterityrefers to managing the inherent conflicts fromexplorative variability creation and exploitativevariability reduction process on multi-levels oforganisations (Bledow et al., 2009). Hence, itrefers to a company’s ability to balance explo-ration activities and exploitation activities ina way to optimally acquire and assimilate newknowledge that can be exploited in innovationsand will result ultimately in business perfor-mance. Ambidexterity refers in this context tothe balance between absorptive capacity acqui-sition and business performance exploitation,whereas balance needs to consider structural,contextual, domain and organisational dimen-sions. In this regard, an organisation’s PortfolioManagement (PM) and Innovation Manage-ment such as New Product Management (NPD)and Research and Development (R&D) need tocollaborate in a closed way to sustain revenueand with balanced innovation for a consistentvalue stream. A company’s core strengths andcapabilities determine the portfolio with a focuson high-end products in case of Innovatorbusiness model characteristics and focus on low-end products for the Defender strategy type.According to the case study findings, the extent

of Marketing-based versus sales-driven businesshas a significant impact on the short-term andmid-term agility for changing resource capacityand capabilities.

SC differentiation using customer and prod-uct segmentation: The modelling of capability-patterns as yielded from the strategic fit anal-ysis have been estimated as very useful forexpressing needed levels for SC differentiationthat can be used for IS configuration per com-panies’ product segments. Based on this find-ing, a company’s customer/product segmentscan be enhanced by the capability approachby dimensions and characteristics useful fordescribing SC strategy and SC differentiation.By this approach, the model combines informa-tion that needs to be interpreted together forreaching coherence between portfolio strategy,marketing strategy and supply chain strategyand differentiating criteria. The SC strategycan be presented for each product segment bycapability-pattern describing the requirementsand characteristics of architectural artefactsneeded for SC differentiation.

The impact of SC dynamics on SC complexityand SC performance: According to the SCMexperts’ experience, the complexity implied inSCM IS has been often the primary concern ofcompanies against the implementation of thesesolutions. When systems settings were made inone place in the SCM IS to align processes, side-effects on other places within SCM processes areoften not predictable. The complexity of supply


Fig. 10: Ambidexterity: balancing exploration and exploitation (an author’s view)

chains and SCM IS implementations often can-not be handled. Moreover, SCM processes areoften highly automated and balanced againstthe known business situation but failed in caseswhen things change. Therefore, configurationsof SCM IS are often not robust against amassive change in business conditions. Anotheraspect the experts observed is that physicalresources of steel companies’ supply chains areoften not balanced against the actual busi-ness requirements. When a bottleneck resourceneeds to be utilised to their available capacity tofulfil customers demand, often SCM IS systemscould not help to get feasible and consistentplanning results. For that reason, the expertsestimated SC design and SC modelling as themost significant practices for dealing with theSC dynamic and SC complexity that steelcompanies are facing. Mainly, balance long-term business objectives against short-termorder fulfilment in a way to maximise sales-profit and utilise resources effectively but toavoid extensive stock-inventory presents a keychallenge. Fig. 12 shows the main processes thatneed to be mastered in a steel company’s supplychain to reach business objectives. The targetsare partly conflicting, but highly related to eachother, so that interdependencies are complexto predict in the case of changing businessconditions.

Key trends that contribute to increasing SCcomplexity: The individual characteristics ofsteel companies’ products and the productioncapabilities have a direct impact on SC com-plexity. As a result, there is a high inter-dependency between planning processes andsales of products and the manufacturing of theindividual product. Hence, the following keytrends have been identified that contribute toincreasing SC complexity at steel companies:1. outsourcing of manufacturing and globalisa-

tion of operations;2. demanding customers and shorter lead times

and special delivery requirements;3. more use of managed inventory programs–

vendor managed inventory (VMI);4. increase in numbers of customised products.

These industry trends have a direct impacton SC design and on the question of how tooptimally plan elements such as sales items,facilities and locations, customers and suppliers.In dealing with the wide variety of resultingrequirements, planning in varying granularityis practised. These covers high-levels of aggre-gation at the long-term planning level and highlevels of detail on the planning and fulfilmentlevel of individual pieces and orders that needto be synchronised in a detailed and continuousmanner. According to the case study findings,in today’s typical SCM set-up, the processes are

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mostly covered by different IS applications thatare connected by interfaces. As a consequence,the existing setup of IS applications does coverthe company’s business from a local point ofview. However, the sum of all the local bestresults is mostly not the best overall possible,as existing information in neighbouring areascannot leverage based on missing transparencythroughout the supply chain processes. Theexperts see the transformation of such ITbrownfield landscapes into new SCM IS asthe most critical challenges of steel companiesfor optimising their overall business objectives.For that reason, SCM IS has to provide mod-elling capabilities to design integrated feasibleplans meeting profitability and other criteriaaccording to corporate objectives. Hence, SCmodelling and visualisation capabilities areessential for mastering these objectives in adynamic environment.

Vital capabilities for mastering SC dynamics:Comprehensive IS capabilities have been iden-tified in the SAP case study and integrated intothe measurement model that has been appliedto the industrial case studies. For orchestratingthe IS capabilities of steel companies’ SCMprocesses so as to moderate SC performanceand antecedent SCM capabilities regarding SCdynamics, the following key capabilities havebeen identified by the present study:1. SC planning and optimisation capabilities;2. SC simulation and visualisation capabilities;

and3. SC design and SC modelling capabilities.

Reducing SC complexity: Constraints andsensitivity to business conditions in SC plan-ning and execution scenarios make SCM insteel companies a highly sophisticated task. Forthis reason, simulation capabilities have beenestimated to be a high priority for dealingwith SCM complexity. Moreover, participantshave rated simulation capabilities as very usefulfor managing SC dynamics by preparing plansfor different situations in advance. SC mod-elling and SC simulation capabilities have beenidentified for managing both low-end productsand high-end simultaneously and dynamically.Hence, the experts see the approach of creatingcapability patterns as architectural artefacts

as very useful to drive fast re-modelling ofsupply chains, responsive to changing businessconditions. Fig. 11 illustrates how capabilitypatterns can support SCM processes that needto be balanced to reach business objectives.Moreover, predefined capability patterns cancontribute to more transparency o SC processesand can help to develop alternative SCMscenarios to respond faster to changes.

The impact of SC moderating capabilities onSC performance: The findings of the presentstudy on moderating SCM capabilities are inline with the process industry’s SC problemsidentified by Shaw et al. (2005) such as inSC network design, SC simulation, and SCplanning (Liu, 2011, p. 21; Shaw et al., 2005).Moreover, Papageorgiou (2009) divided theprocess industry’s key capabilities into SCM inSC design, SC planning and scheduling, andsupply control (Liu, 2011, p. 21; Papageogriou,2009). The impact of changes which influencefactors such as environmental ones needs tobe moderated by ‘SC performance’ antecedentcapabilities that are orchestrated by ‘IS capa-bilities for SCM’.

4.6 Dynamic CapabilityManagement usingthe Study’s Concept

DC from architectural artefacts from for mod-erating fit of SCM IS: The model has beenestimated as very useful to determine new levelsrequired for SC capabilities and IS capabilitieswhen the company’s business strategy needsto be adjusted to new market conditions. Thisfinding shows the value of the concept for pre-developing capability pattern as architecturalartefacts the act as absorptive capacity and canbe exploited in business performance by an ISGovernance cycle by moderating fast alignmentof SCM IS capabilities to changing environ-mental conditions. Business Model stress testsaccording to Haaker et al. (2017) can pro-vide a systematic analysis of BM components’robustness in different future situations andenvironments and enables to identify sourcesof dynamic capabilities for SCM IS. BusinessModel Canvas methods and SWAT and PESTE


Fig. 11: Supporting SC dynamics using capability-patterns (an author’s view)

analysis can help to identify and predict furtherstates with a high probability of occurrenceand significant impact on BM componentsand IS capability for SCM. Digital Twins andsimulation and optimisation methods of SCMIS helps to redefine the actual required tech-nical and organisational configurations figuredout by the artefacts. Before developing theartefacts, the Return on Investment (ROI) canbe calculated using the discounted cash flow(DCF) method to balance the expected businessvalue against the Total Cost of Ownership(TCO) that reflect all initial and ongoingcost related to an IS investment (Kirwin andMieritz, 2003). Fig. 13 shows how the modelcan be applied in an Enterprise ArchitectureManagement (EAM) cycle. The right side of thecycle shows the artefact development that refersto DC exploration and the left side highlightsthe IS implementation that refers to exploitingbusiness performance from the materialisedDC/absorptive capacity. In referring to Raischet al. (2009), the model enables exploration andexploitation of DC in an ambidexterity way forthe dimensions domain, organisational, contextand the timely and situational, where the degreeof integration between relates to the extent ofthe dynamics the organisational units face.

Due to the proven procedure, the strate-gic alignment model needs to be calibrated.

Therefore, a two-stage review is to recommendusing the first step to identify areas that needfurther in-depth analysis in a second step. Theareas of misfit and their types of misfit suchas functionality, data, role, control, usabilityand organisational needs to be identified, anal-ysed and documented as input for developingartefacts. Moreover, for addressing the neededactions to close the technical and organisationalgaps in SC interoperability required to sup-port SC integration at the level required forstrategic fit. In referring to Strong and Volkoff(2010, pp. 747–749), misfit can be addressedby coverage, which relates to functionality andfeatures provided by SCM IS, and it can beaddressed by ‘enablement’ through context-specific SCM IS modelling and configuration ofthe application and data. This differentiationis seen as very important as the ability to dealwith the complexity in SCM in a simplified wayrelates to a significant extent on SC modellingand secondly on technical features of purchasedIS solutions.

Organisational learning mechanisms: Alllearning stages that are crucial for enhancing or-ganisations’ competitiveness such as structural,cultural, psychological and policy mechanisms(Knoppen et al., 2015, pp. 544–550) are seensupported by the present IS Governance ap-proach. Moreover, the evidence for organisa-

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Fig. 12: The SCM IS dynamic alignment methodology (an author’s view, encasing TOGAF)

tional learning is seen by sensemaking effectsaccording to Weick et al. (2005) at applyingthe model, such as arriving at a commonunderstanding of SCM IS capabilities effectsand their use for directed actions with clear,prioritised objectives. The approach supportsdeutero learning by providing the participantswith the big picture of where their efforts havean impact on business objectives and by theircontinuous reflection of developed artefactsand routines, which develops and align theircognitive profiles as well.

4.7 Dynamic Capabilities of DigitalBusiness Models

Trusted collaboration in SC ecosystems by aBlockchain approach: A blockchain, as a shared,ledger technology, improves business networkefficiency by the increased visibility of businesstransactions to all members of an ecosystem.The key benefits are significantly reduced set-tlement time and overhead costs and reducedrisks of collusion and tampering because offull transparency to all actors. Hence, thesystem inherent fraud prevention and reducedintegration complexity that results in increasingefficiency. The Internet of Things (IoT) andthe Blockchain technologies offer new ways to

do business even in complex supply chains(Armstrong, 2016). Internet-capable sensorscapture granular real-time data about productsand logistic events with timestamps at differentlocations throughout the supply chain. Hence,the blockchain technology can support trustedcollaboration in digital SC ecosystems by capa-bilities such as real-time transfer of control dataand ‘digital assets’ and Smart Contracts forsystem-enforced inter-company business-rulesfor the process autonomy. Neutral collaborationplatforms for shared business data and businesslogic enable new business models with trustedscenarios for multiple parties, which led to thefollowing benefits:1. cost reduction and risk minimisation of

automation scenarios;2. secure and reliable tracking of SC events and

fraud reduction by increased transparency;3. increased real-time SC visibility within

ecosystems and consistent progress informa-tion;

4. real-time ad-hoc processing of exceptionsusing machine-learning capabilities.

4.7.1 Platform and Data-Driven BusinessModels

Platform-driven business model–outside in in-novations: Digital business models are char-acterised by innovations in the value chains


and their transformation to the cross-industrydigital ecosystem. Platforms are used for con-necting intelligent products and driving pro-cesses personalised to user/customer throughdata-driven and dynamically configured smartservices. Moreover, service platforms enablemodular orchestration of value chains throughthe dynamic configuration of digital and phys-ical services. Big data technology allows datacollection across various technical and physi-cal domains (e.g. Energy sector, traffic, andwhether to name a few) and from different socialand business contexts (e.g. consumer behaviourand perceptions, demographics). Such cross-context data seems useful for analysing demandpatterns and analogies across markets and pro-vides enhanced capabilities for searching net-work effects and scaling effects in the markets.Moreover, new sources of value are searchedfrom collaborative business models enabled byPlatform technology as they support the orches-tration of entire ecosystems of interconnectedcustomers, producers, service providers andsuppliers. Initiatives such as industry 4.0 enablecompanies to create ecosystems with enhancedcollaboration capabilities allowing new businessstrategies as ecosystem partners can commonlyshare services and can complement resourcesand processes in a highly integrated and au-tonomous way.

Rules and value enablers of platform-drivenbusiness models: Among the most importantrules for designing and optimising platformbusiness models belong (1) network effects,(2) distribution power law and (3) asymmetriccompetition for providing different ways togrow (Daugherty et al., 2016). Digital businessmodels that are developed based on these rulescan scale exponentially, and, can complementexisting traditional business models. A distri-bution power law relates to scalable platformbusiness that allows others to generate profitsin a way avoiding diminishing returns thatwould be related to traditional value chains(Daugherty et al., 2016).

Network effects and macroeconomic impactof platform business models: Network effectscome from products that are attractive to usersand from interoperability capabilities rather

than from scale effects of the organisations’quantitative size. While the economy of scale inSCM typically are realised on the supply side,network effects arise primarily on the demandside. The concept of network effects has beenpopularised by Metcalfe (a co-inventor of theEthernet). Metcalfe’s law state that “the costof an IT network was directly proportional tothe number of network cards installed, but thevalue of the network was proportional to thesquare of the number of users (cost of N resultsin a value of N2).” The actual numbers ofthis proposition are not affirmed so far, but,the stated positive and negative effects of theconcept are indisputable. IoT Platforms providethe infrastructure to create intellectual propertyand to offer the right to use it to other usersby earning income from it. Finally, platformsprovide the infrastructure to transfer digitalgoods to others and to enforce property rights.Demand-side economies-of-scale focuses on net-work effects of two-sided markets, where valuecreation is enabled by platform ecosystemsincorporating stakeholders such as customers,partners, producers, retailers, transportations,service providers and others. (Daugherty et al.,2016). Tab. 1 contrasts significant differences intraditional business models and platform-basedbusiness models.

Asset-light business models: A core trend ofthe digital transformations is a move from asset-heavy to asset-light business models, whereintangible assets create value. The advantagesof these are growth with reduced risks throughshared investments with others. Companieswithout tangible assets seem at first sight asnot solidly based, but, the market power seemsto distribute opposing to this assumption,which can be observed by companies such asAmazon and Google. Market platforms aresuccessful in asset-light business models. Alltypes of outsourcing physical production toother companies such as by sub-contracting,where the intellectual property is protected oris not a source of differentiation can be seenas asset-light business models (Kachaner andWhybrew, 2014). Finally, product-to-service-transformation such as the transition fromcomputer selling to offering services like IT

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Tab. 5: Traditional vs digital business models (author’s view enhanced from Daugherty et al., 2016)

Traditional industrial business models Digital, platform-driven business modelsProducts, services Products, services, platform servicesLinear value chains Non-linear ecosystemsThe supply-side economy of scale The demand-side economy of scaleOrganic grow and merger & acquisition Network effects & asymmetric driven growthPhysical assets Digital assetsAsset-driven market value Ecosystem driven market valuePower from controlling the supply chain Power from optimising the ecosystem

consulting shows useful when the products getcommodity.

Between ‘asset-heavy’ and ‘asset-light’ busi-ness models: Due to the present study’s find-ings, there are seen two main areas of value fromplatform-driven innovations as follows:

1. Asset-heavy business can be supported byplatform innovations in operational excel-lence by methods such as (1) operationalsensing and (2) condition monitoring forprocess autonomy and (3) performancemanagement. Preferred Artefactual Intel-ligence (AI) methods are Cyber-PhysicalSystems (CPS), machine learning capabili-ties, optimisation, simulations and businessmodel stress tests using digital twins. More-over, they can be complemented with asset-light business such as outsourcing, resource-collaboration, payment-per-use and market-places.

2. Asset-light business can focus, for instance,on network effects of the demand sideand resource-sharing with partners for com-pensating short-term capacity overloads tominimise capital invested. Preferred AImethods for demand-side processes are ‘usermodelling and personalisation and serviceadaptation’ (UMPA) for autonomous ser-vice adoption to meet users needs at mar-ketplaces.

Fig. 13 shows the levels and methods ofplatform business architecture. The platformprovides the technical infrastructure for devel-oping and operating intelligent business serviceswith learning and self-adapting capabilities.The platform orchestrates sensing capabilities

for supply-side monitoring and condition man-agement. Example companies with platformstrategies for enhancing their existing businessare Fiat (connected car), Caterpillar (connectedmachines), Schneider Electric (smart cities,buildings, and homes), and Philips (smarthealth). Asset-light business models are suchas those of Google and Uber, while Appleand Amazon drive both, asset-heavy businessby retail of physical products and asset-lightbusiness (iTunes, Amazon-Prime) by digitalproducts via marketplaces.

New value propositions and profit models:New value can come from the product andservice individualisation for customers’ partic-ular situation. Moreover, it can come fromsignificant improvements in quality and cost-effectiveness preferably realised by simplifiedprocesses. New profit models can be basedon flexible pricing models that can be usage-based, output-based, and value-based (profitsharing). Moreover, the monetisation of smartdata can be a value driver and contributorto profit. Finally, the integration of servicesfrom partners and competitors into the ownoffering can play a significant role in exhaustingnetwork effects with highly economical way.Hence, Smart Services and Platforms form thebasis of innovative digital business models.To summarise, the primary value drivers are(1) Improved performance, functionality andreliability of processes and products, (2) BMinnovations and new commercial models (as-a-service, pay per use) and new sources ofrevenues and profits (performance guarantees,smart data), and (3) faster innovation cycles.

Smart Service, as core components of dig-ital business models, are generating value for


Fig. 13: Layers and opportunities of platform-driven business for asset-light and asset-heavy business models (anauthor’s view), adapted from Kagermann et al. (2015)

businesses and customers and further ecosystemmembers. They are managed via platforms andcan be provided by internal and external serviceproviders for orchestrating and operating themodular processes of ecosystems. They interactwith sensors, systems and actors, and are basedon algorithms and be able to adapt to changingcontexts over time. Cost of smart servicesis minimal as the marginal costs of digitalvalue creation are nearly zero (Rifkin, 2015)and can be distributed across the ecosystemthat uses the service. While economists andmanagers have focused so far on the reductionof marginal cost, the paradigm change fromphysical assets to digital assets can reduce thesenearly zero. Finally, Smart Services shift thefocus from product ownership to their value-oriented usage.

Potentials of Smart Services are the detectionof deviations in business processes and derivingmeasures for responses. Moreover, they canorchestrate processes by predefined rules andcan be combined flexibly with other servicesand adapt to environmental needs. Based onthese capabilities, they can increase processeffectiveness and efficiency, help to avoid waste,increase resource usage and solve unforeseenproblems at an early stage. Smart Servicespotentials for external processes cover the au-

tonomous interaction with customers to gatherand analyse data individually and on a largescale. In sum, the potentials can lead to increaseinnovative strength and increase turnover andprofitability as well as increase customer loy-alty and can provide a significant competitivedifferentiator. Fast innovation cycles for SmartServices can be accelerated by generic enablessuch as Open Source concepts and scalable plat-forms. Wahlster (2018) reported the followingsolutions based on Smart Services:

1. Sensing: Collaborative Robots, VirtualAgents, Autonomous System (Cars, Ships,Trains);

2. Understanding: Intelligent Smart Home,Answering Engines; Digital IT Assistants;

3. Acting: Intelligent Help Systems; Recom-mendation and Persuasion Systems, Intelli-gent Tutor and Training Systems (Wahlster,2018).

AI methods such as the following are support-ing these Smart applications:

1. Sensing is supported by methods suchas signal symbol transformation, multi-sensor fusion, pattern recognition, emo-tion/user/context recognition;

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Fig. 14: Using the research theory for a GA-ANN based autonomous alignment (an author’s view)

2. Understanding is support by methods suchas text/video/dialogue understanding, in-formation extraction, machine learning, ac-tion planning and plan recognition.

3. Acting is supported by methods such as sen-sor motoric feedback, adaptive user interac-tion, multi-agent collaboration techniques,personalised presentation (Wahlster, 2018).

Demand-side Artificial Intelligence (AI)methods enable to generate and adapt user datamodels and services of web-based applicationsat their usage by identifying the users’ needs,interests and preferences. By this approach,user models present properties of individualusers and their cognitive and behavioural char-acteristics established by variables and theirdeclaration, which can be used to personaliseproducts and services to these users. Becauseuser modelling covers many aspects of the hu-man nature those vary in different contexts, in-teroperability of application-independent mod-els that merges psychology and artificial in-telligence and ontology modelling standardsare significant. Wahlster (2018) reported thefollowing AI techniques used in practice for self-learning smart services:1. Interactive configuration using constraint

processing to get desired features.2. Recommendation techniques for configura-

tions such as knowledge-based and demo-graphically based recommendations; rec-ommendations based on previous choices

and ratings; collaborative filtering acrossdomains.

3. Personalised service-interaction to cus-tomers’ concerns using knowledge abouttheir interests that influence their choice.

4.7.2 The Study’s Contribution to DigitalBusiness Models

Absorptive capacity allocation and effects inSC ecosystems: The study’s approach makesvisible where relationship capital that has beeninvested in certain places of the supply chainshows a positive effect on business performance.The author of the study sees this effect asparticularly useful for digital business modelsas these focus on network effects as sources fornew business value that arise from collaborationacross SC ecosystems partners and interop-erability capabilities. For this purpose, theapproach supports transparency of SC interop-erability on different levels as a core enabler ofnetwork effects and the efforts and effects acrossthe value chain. Moreover, the focus on cross-organisational value development of digitalbusinesses through utilising AI methods andSmart technology drives increasing dynamismof SC business models, which underpins thevalue of the present study’s methodology.

Dynamic alignment using GA and adoptingthe concept’s ontology to an ANN: The ontologyof the presents study’s concept can be trans-lated into a machine-readable semantic suchas the Web Ontology Language (OWL). OWL


and the vocabulary of the Resource DescriptionFramework (RDF) are components of the Se-mantic Web, which is an initiative by the WorldWide Web Consortium for standardisation ofknowledge digitalisation as an extension of thewww. Fig. 14 demonstrates how the concept canbe adapted to an Artificial Neuronal Network(ANN) for optimisation using a Genetic Algo-rithm (GA), where Note-to-Note relationshipsof the ANN show the high-order SC capabilities.The idea is to identify the changes in the noteweight factors necessary to compensate for the

changes of the sensed conditions to reach theobjectives of the fitness function again afterenvironmental changes. The identified deltascan be used to determine ideal response by cat-egories of misfit/interoperability (inference) forre-configuration. The concept can be used as abasis for autonomous alignment of SCM IS andfor simulation purposes using Digital Twins. Forthat purpose, the physical supply chain needs tobe monitored by sensors to collect data of therelevant conditions and transform these for theinput vectors of the GA optimisation process.

5 CONCLUSION

A best practice for the strategic alignment ofSCM IS–a holistic view by combined concepts:The study closed the gaps of missing bestpractice for assessing the strategic fit of SCM ISand missing methodology for orchestrating DCon multi-levels related to SCM IS. In referringto Minsberg’s P’s for plan and pattern, themethodology has been developed for identifyingand aligning patterns of a company’s SCM IScapabilities in detail–that refers to the resource-based view–to match context-specific needs ofthe market-based view. The combination of aprofile deviation approach (that was used in2011 by McLaren et al.) that based on theconfigurational theory with a domain approach(that is related to Henderson’s and Venkatra-man’s 1996 Strategic Alignment Model (SAM)and used by Avison et al. (2004) for strategic fitanalysis), the methodology enables to managestrategic alignment with regard to the dimen-sions domain, organisational and contextual.According to the contingency theory, thereis no universally superior strategy or way tomanage in a given environment (Venkatraman,1989b). Instead, the context and structure mustfit together if an organisation is to performwell (Van de Ven and Drazin, 1985). BecauseSC integration is not a question of ‘highintegration fits all’, rather the degree of inte-gration depends on several situational factors(Bagchi et al., 2005; Childerhouse and Towill,2011; Godsell, 2008), a hierarchical capabilitystructure (according to Grant, 1996a) has been

integrated for assessing fitness at differentlevels of aggregation to identify context-specificcharacteristics of SC integration antecedentsand their impact on SC performance. The studyenables the analysis of antecedent capabilities ofSC integration such as ‘information exchange’,‘coordination’, ‘activity integration’ and ‘re-source collaboration’ (Wu et al., 2006; Rai etal., 2006; Childerhouse and Towill, 2011) andtheir needs and contribution to strategic fit ofSCM IS for individual business contexts.

Based on the integrated domain concept,the model enables to identify second-ordereffects of IS capabilities along the supply chaincaused by spill-over effects (Tallon, 2012). Thisapproach helps to identify unused potentialsfrom IT investments that Brynjolfsson calledthe IT Productivity Paradox and to identifybottlenecks from IS capabilities across domainsthat refer to Goldratt’s Theory-of-Constraints(TOC). While TOC focus in practice on perfor-mance management of business process usingbalanced scorecards and KPI management, thepresent study’ model enables TOC analysis atthe architectural level before IS investments areallocated and shows cross-domain effects thatare not visible before. By the use of the modelin an IS governance cycle, a methodology hasbeen provided for exploring, materialising anddeposit DC as an absorptive capacity for fastexploitation into business performance. Basedon the configurational theory for identifyingideal configurations and the SC domain struc-

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ture, the concept is in line with the systemstheory and enables strategic alignment of SCMIS to contingencies across organisations, con-text, domains and situations on multi-levels.Operational excellence, as a hot topic in SCM ofmanufacturing companies, will be provided bythe effectiveness of the SCM IS architecture bythe present approach, and secondly, be realisedby operational efficiency controlled by SCPMusing business metrics. Moreover, the model en-ables harmonisation and simplification of SCMIS architectures to prevent from implementingtoo much complexity. Finally, the approachhas been assessed as particularly useful forsupporting scale-out scenarios and due diligenceassessments of M&A activities by enabling toidentify the potentials and synergies of thecompanies’ SC models as a more reliable basisas the approach using only financial key figures.

DC enabled by IS Governance: Developmentof absorptive capacity refers to a firm’s abilityto acquire, assimilate, and deposit externalknowledge and commercialise it by exploitingit for organisational performance (Liu et al.,2013, p. 1454; Brettel et al., 2011, pp. 164–174). According to Liu et al. (2013, p. 1460),developing absorptive capacity and DC relatedto SCM has an indirect positive effect on‘SC agility’ as a driver of ‘SC performance’.In referring to Blome et al. (2013) and Liuet al. (2003), developing absorptive capacityrelated to antecedent capabilities of ‘SC in-tegration’ contributes to DC of ‘SC agility’and ‘SC responsiveness’, and, hence contributesto ‘SC performance’. The present approachenables organisations to predefine patterns ofcapabilities as architectural artefacts, whichexpress industry-specific and context-specificconfigurations to fit business strategies. Theseartefacts will lead to increased SC agility andSC performance through faster IS adoptionin fast-changing business environments. Hence,the study contributes to the DC renewal pro-cess, and SCM IS alignment by providing aframework, methods and routines for managingDC through sensing, assessing, managerial-decision-making, artefact development, selec-tion and implementation for aligning businessmodels to the prevailing strategy. Finally, the

methodology supports to manage the balanceand dynamics of DC exploration and exploiting(Brettel et al., 2011), which O’Reilly andTushman (2007) refer to ambidexterity.

Evolutionary vs behavioural economic or bestpractice vs innovation: For making the mostfrom DC both evolutionary and behaviouraleconomics aspects are essential and consideredby the present concept. For Teece (1982, 2018),best practice are not necessarily DC as hefocuses on Schumpeterian’s innovation (1934)combined with higher-order routines (Arndtand Pierce, 2018, p. 414) such as asset orches-tration activities, which involve “new combi-nations” that are not merely adaptive. Suchre-combinations can be new technologies aswell (Arndt and Pierce, 2018, p. 413). However,Eisenhardt and Martin (2000) suggest the use ofbest practices and simple rules such as decision-making heuristics as DC as they see companiesin the same industry with a reasonable amountof competitive homogeneity expressed throughsignificant commonalities across capabilities(Arndt and Pierce, 2018, p. 414). In referringto Arndt and Pierce (2018, p. 413), the presentconcept’s mechanisms, routines and reposito-ries for developing and exploiting DC is seenfounded in evolutionary economics. The meth-ods identified for developing Smart businessmodels allow a significant degree of freedomfor creativity to consider the Schumpeterianview of innovation for developing DC. However,aspects of the behavioural theory play also anessential role in the concept such as dealing withheterogeneity, individual expectations and goalformation.

Sensemaking and organisational learning: Inconsidering design-science aspects according toHevner et al. (2004) and sensemaking aspectssuch as ‘framing’ according to Klein et al.(2006a, p. 71), a coherent research theory andmethodological framework have been devel-oped, which enabled to prove the model onframing data as meaningful for strategic fit.By this approach, content analysis, accordingto Mayring (2014) is implied in the modelfor deductive category formation by fittingmeaningful data to predefined categories totriangulate qualitative and quantitative data


at applying the model. Hence, high levels ofconstruct validity are provided by the approach(Yin, 2009). According to Weick et al. (2005),sensemaking in organisations is not a questionof accuracy, but rather about plausibility andthe development of a collective mindset forunderstanding the past and the present, andto develop a directed flux of action to masterthe future. Sensemaking properties according toWeick et al. (2005) were identified as supportedto a high degree in applying the model bysupporting a collective view of objectives anddirected actions and increased shared awarenessabout the value of supply chain members’efforts spent on SCM processes on the overallbusiness performance.

Contextual factors are dominating the levelsof IS capabilities required to fit: The presentstudy shows a high variability of levels neededto fit by (1) generic/high-order SC capabilitiesand (2) detailed IS capabilities for companiesthat show the same strategy type due toMiles and Snow. Hence, the present studyproposes Miles and Snow strategy archetypesas a reference for high-end product and low-end product strategies, but it is highly rec-ommended to identify ideal levels of supportof fit using firm/context-specific assessments.Hybrid strategy types have been identified forboth sample organisations. The study providesevidence that a higher level of detail in IScapabilities and their levels of support to fitis needed sooner then it was used in previousresearch for considering strategic orientationsand the resulting requirements for SC differenti-ation appropriately. Moreover, SC domains’ IScapabilities need to be assessed individually fortheir ideal levels rather than to use predefinedideal levels of reference strategy types becausesituational and context-specific support need tobe prioritised in today’s business dynamics forinferring appropriated levels of SC capabilities.Finally, ideal levels of support to fit by second-order effects across the supply chain need to beconsidered appropriately.

SC models’ dynamics driven by complexityand adoption: We found out that the combi-nation of strategy types according to Miles andSnow with the categories of marketing-oriented

business and sales-driven business helps todifferentiate companies’ SC models in regardof their dynamics and whether complexityor adoptions drive it. This finding helps tocategorise SC models in their design require-ments regarding the extent of complexity inSC differentiation and the required degree ofdynamics for adoptions. Moreover, it providesthe basis, in combination with the customerand product segmentation, for defining de-tailed SC strategies and expressing the relatedSC differentiation by capability pattern persegment. This approach provides significantlyincreased transparency in companies’ productsegments and their related SC strategy anddifferentiation and finally, of IS capabilities formanaging the supply chains. Finally, it helpsin benchmarking implemented configurationsagainst ideal ones to firms’ competitive strat-egy, and fast scaling out IS capabilities to newbusiness models.

The contribution to digital business models:As shown by the study, the approach makesvisible where and when relationship capitalallocated as absorptive capacity shows a pos-itive effect on business performance in thesupply chain, which can significantly dependon the businesses’ strategic orientation. Theauthor sees this effect significant for digitalbusiness models as these focuses on new sourcesof value such from network effects across SCecosystems. Essential sources of network effectsare – among the right products – high levels ofinteroperability for scaling at minimal marginalcost (Rifkin, 2015). Hence, the present approachsupports transparency of SC interoperabilityon different levels and efforts and value con-tributed from ecosystem members. The focuson cross-organisational and collaborative valuedevelopment utilising AI methods and Smarttechnology drives dynamism of business models.For this reason, the concept shows high poten-tials for support autonomous strategic align-ment of digital business models by adopting theontology to machine-learning services, Cyber-Physical Systems (CPS) and Smart Services.

Criticism of previous research: Henderson’sand Venkatraman’s 1996 SAM provides a usefulmethodical framework for assessing the rela-

74 Jochen Nürk

tionships of strategic fit and functional integra-tion between IT and business domains but lacksquantitative measurement and benchmarkingcapabilities, which have been integrated andcombined by the present study. Five genericSC capabilities were used by McLaren et al.(2011) for predefining ideal levels of supportto strategic fit in reference to Miles and Snowstrategy archetypes. The present study showsthe categorisation of high-end products that re-late to the Innovator strategy type and low-endproducts that relate to the Defender strategytype – according to Miles and Snow – as usefulfor expressing ideal levels of support needed for

the strategic fit of product segments in the steelindustry. However, the present study providesclear evidence that for deriving reasonableactions from assessment results, a higher levelof detail in IS capabilities’ levels of support tofit is needed to consider strategic orientationsand the resulting requirements for SC differ-entiation appropriately. Moreover, SC domains’IS capabilities need to be assessed individuallyand for each SC domain for their levels assituational and context-specific support needto be prioritised in today’s business dynamicsfor providing the right levels of responsiveness,agility and resource-collaboration.

6 REFERENCES

Amit, R. and Schoemaker, P. J. H. 1993. StrategicAssets and Organizational Rent. StrategicManagement Journal, 14 (1), 33–46.

Armstrong, S. 2016. Eight Things YouNeed to Know About the Future ofRetail. Wired UK [online]. Available at:http://www.wired.co.uk/magazine/march-2016.

Arndt, F. and Pierce, L. 2018. The Behaviouraland Evolutionary Roots of Dynamic Capabilities.Industrial and Corporate Change, 27 (2), 413–424.

Avison, D., Jones, J., Powell, P. and Wilson, D.2004. Using and Validating the Strategic AlignmentModel. The Journal of Strategic InformationSystems, 13 (3), 223–246.

Bagchi, P. K., Ha, B. C., Skjøtt-Larsen, T. andSørensen, L. B. 2005. Supply Chain Integration:A European Survey. The International Journal ofLogistics Management, 16 (2), 275–294.

Bashiri, I., Engels, C. and Heinzelmann, M.2010. Strategic Alignment: Zur Ausrichtungvon Business, IT und Business Inteligence.Springer-Verlag, Berlin, Heidelberg. ISBN978-3-642-11437-3.

Bledow, R., Frese, M., Anderson, M., Erez, M.and Farr, J. 2009. A Dialectic Perspectiveon Innovation: Conflicting Demands, MultiplePathways, and Ambidexterity. Industrial andOrganizational Psychology, 2 (3), 305–337.

Blome, C., Schoenherr, T. and Rexhausen, D.2013. Antecedents and Enablers of Supply ChainAgility and Its Effect on Performance: A DynamicCapabilities Perspective. International Journal ofProduction Research, 51 (4), 1295–1318.

Blomberg, J., Giacomi, J., Mosher, A. andSwenton-Wall, P. 1993. Ethnographic FieldMethods and their Relation to Design. In Schuler,D. and Namioka, A. (eds.). Participatory Design:Principles and Practices, Chapter 7, pp. 123–155.

Brettel, M., Greve, G. I., Flatten, T. C. 2011.Giving up Linearity: Absorptive Capacity andPerformance. Journal of Managerial Issues, 23 (2),164–189.

Bryman, A. and Bell, E. 2003. Business ResearchMethods. 3rd ed. Oxford Press.

Brynjolfsson, E. 1993. The Productivity Paradox ofInformation Technology. Communications of theACM, 36 (12), 66–77.

Chan, Y. E., Huff, S. L., Barclay, D. W. andCopeland, D. G. 1997. Business StrategicOrientation, Information Systems StrategicOrientation, and Strategic Alignment. InformationSystems Research, 8 (2), 125–150.

Childerhouse, P. and Towill, D. R. 2011. Arcs ofSupply Chain Integration. International Journalof Production Research, 49 (24), 7441–7468.

Conant, J. S., Mokwa, M. P. and Varadarajan, P. R.1990. Strategic Types, Distinctive MarketingCompetencies, and Organizational Performance:A Multiple Measures-Based Study. StrategicManagement Journal, 11 (5), 365–383.

Daugherty, P., Carrel-Billiard, M. andBiltz, M. J. 2016. People First: The Primacy ofPeople in a Digital Age. Accenture TechnologyVision 2016 report.


Donaldson, L. 2006. The Contingency Theoryof Organizational Design: Challenges andOpportunities. In Burton, R. M., Håkonsson,D. D., Eriksen, B. and Snow, C. C. (eds.).Organization Design: The Evolving State-of-the-Art, Chapter 2. Information and OrganizationDesign Series, Vol. 6.

Doty, D. H., Glick, W. H. and Huber, G. P. 1993.Fit, Equifinality, and Organizational Effectiveness:A Test of Two Configurational Theories. TheAcademy of Management Journal, 36 (6),1196–1250.

Eisenhardt, K. M. 1989. Building Theories from CaseStudy Research. Academy of Management Review,14 (4), 532–550.

Eisenhardt, K. M., Martin, J. A. 2000. DynamicCapabilities: What Are They? StrategicManagement Journal, 21 (10/11), 1105–1121.

Godsell, J. 2008. Developing Customer ResponsiveSupply Chain Strategy: An Empirical Investigationof the Relationship between Market Segmentationand Supply Chain Strategy. PhD thesis, School ofManagement, Granfield University, UK.

Grant, R. M. 1996a. Prospering in Dynamically-Competitive Environments: OrganizationalCapability as Knowledge Integration. OrganizationScience, 7 (4), 375–387.

Grant, R. M. 1996b. Toward a Knowledge-BasedTheory of the Firm. Strategic ManagementJournal, 17 (S2), 109–122.

Haaker, T., Bouwman, H., Janssen, W. andde Reuver, M. 2017. Business Model StressTesting: A Practical Approach to Test theRobustness of a Business Model. Futures, 89,14–25.

Henderson, J. C. and Venkatraman, N. 1990.Strategic Alignment: A Model For OrganizationalTransformation via Information Technology. CISRWorking Paper No. 2017.

Henderson, J. C., Venkatraman, N. and Oldach, S.1996. Aligning Business and IT Strategies.In Luftman, J. N. (ed.). Competing in theInformation Age: Strategic Alignment in Practice,pp. 21–41. New York: Oxford University Press.

Hevner, A. R., March, S. T., Park, J. and Ram, S.2004. Design Science in Information SystemsResearch. MIS Quarterly, 28 (1), 75–105.

Hsieh, H.-F. and Shannon, S. E. 2005. ThreeApproaches to Qualitative Content Analysis.Qualitative Health Research, 15 (9), 1277–1288.

Johnson, M. W., Christensen, C. M. andKagermann, H. 2008. Reinventing Your BusinessModel. Harvard Business Review, 86 (12), 50–59.

Kachaner, N. and Whybrew, A. 2014. When “AssetLight” Is Right. The Boston Consulting Group.

Kagermann, H., Riemensperger, F., Hoke, D.,Schuh, G., Scheer, A.-W., Spath, D.,Leukert, B., Wahlster, W., Rohleder, B. andSchweer, D. (eds.). 2015. Smart Service WeltRecommendations for the Strategic InitiativeWeb-based Services for Businesses. Acatech –National Academy of Science and Engineering.

Kaplan, B. and Duchon, D. 1988. CombiningQualitative and Quantitative Methods inInformation System Research: A Case Study. MISQuarterly, 12 (4), 571–586.

Kaplan, R. S. and Norton, D. P. 1992. The BalanceScore Card – Measures that Drive Performance.Harvard Business Review, 70 (1), 71–79.

Kaplan, R. S. and Norton, D. P. 2001. Transformingthe Balanced Scorecard from PerformanceMeasurement to Strategic Management: Part II.Accounting Horizons, 15 (2), 147–160.

Kearns, G. S. and Lederer, A. L. 2000. The Effectof Strategic Alignment on the Use of IS-BasedResources for Competitive Advantages. TheJournal of Strategic Information Systems, 9 (4),265–293.

Kirwin, B. and Mieritz, L. 2003. Total Cost ofOwnership as a Common Denominator. GardnerResearch Note in Business Management of IT:Financial Management.

Klein, G., Moon, B. and Hoffman, R. R. 2006a.Making Sense of Sensemaking 1: AlternativePerspectives. IEEE Computer Society, 21 (4),70–73.

Klein, G., Moon, B. and Hoffman, R. R. 2006b.Making Sense of Sensemaking 2: A MacrocognitiveModel. IEEE Computer Society, 21 (5), 88–92.

Knoppen, D., Johnston, D. and Sáenz, M. J. 2015.Supply Chain Relationships as a Context forLearning Leading to Innovation. The InternationalJournal of Logistics Management, 26 (3), 543–567.

Koulikoff-Souviron, M. and Harrison, A. 2005.Using Case Study Methods in Researching SupplyChains. In Kotzab, H., Seuring, S., Müller, M.and Reiner, G. (eds.). Research Methodologiesin Supply Chain Management, pp. 267–282.Physica-Verlag HD.

Liu, S. 2011. Supply Chain Management in the ProcessIndustry. PhD Thesis, University College London,UK.

Liu, H., Ke, W., Wie, K. K. and Hua, Z. 2013. TheImpact of IT Capabilities on Firm Performance:The Mediating Roles of Absorptive Capacity andSupply Chain Agility. Decision Support Systems,54 (3), 1452–1462.

Luftman, J. 2003. Assessing It/Business Alignment.Information Systems Management, 20 (4), 9–15.

76 Jochen Nürk

Makadok, R. 2001. Toward a Synthesis of Resource-Based and Dynamic-Capabilities Views of Rent.Strategic Management Journal, 22 (5), 387–401.

Mayring, P. 2014. Qualitative Content Analysis:Theoretical Foundation, Basic Procedures andSoftware Solution. Social Science Open AccessRepository.

McLaren, T. S., Head, M. M. and Yuan, Y. 2004.Using Competitive Strategy Patterns to DetermineIdeal Supply Chain Management InformationSystems Capabilities. International Journal ofInternet and Enterprise Management, 2 (1), 45–61.

McLaren, T. S., Head, M. M., Yuan, Y. andChan, Y. E. 2011. A Multilevel Model forMeasuring Fit Between a Firm’s CompetitiveStrategy and Information Systems Capabilities.MIS Quarterly, 35 (4), 909–929.

Miles, R. E. and Snow, C. C. 1978. OrganizationalStrategy, Structure and Process. New York:McGraw-Hill.

Mintzberg, H. 1978. Pattern in Strategy Formation.Management Science, 24 (9), 934–948.

Mithas, S., Ramasubbu, N. and Sambamurthy, V.2011. How Information Management CapabilityInfluence Firm Performance. MIS Quarterly,35 (1), 237–256.

Oh, W. and Pinsonneault, A. 2007. On theAssessment of the Strategic Value of InformationTechnologies: Conceptual and AnalyticalApproaches. MIS Quarterly, 31 (2), 239–265.

O’Reilly, C. A. and Tushman, M. L. 2007.Ambidexterity as a Dynamic Capability: Resolvingthe Innovator’s Dilemma. Harvard Business SchoolWorking Paper No. 07-088.

Papageorgiou, L. G. 2009. Supply ChainOptimisation for the Process Industries:Advances and Opportunities. Computers &Chemical Engineering, 33 (12), 1931–1938.

Pennings, J. M. 1975. The Relevance of theStructural-Contingency Model for OrganizationalEffectiveness. Administrative Science Quarterly,20 (3), 393–410.

Penrose, E. 1959. The Theory of the Growth of theFirm. Oxford Press: New York.

Pinsonneault, A. and Rivard, S. 1998. InformationTechnology and the Nature of Managerial Work:From the Productivity Paradox to the IcarusParadox? MIS Quarterly, 22 (3), 287–311.

Porter, M. E. 1987. From Competitive Advantageto Corporate Strategy. Harvard Business Review,65 (3), 43–59.

Prieto, V. C. and de Carvalho, M. M. 2011.Strategic Alignment and Performance: BrazilianCompanies in the Medical Diagnostics Sector. TheService Industries Journal, 31 (9), 1405–1427.

Rai, A., Patnayakuni, R. and Seth, N. 2006. FirmPerformance Impacts of Digitally-Enabled SupplyChain Integration Capabilities. MIS Quarterly,30 (2), 225–246.

Raisch, S., Birkinshaw, J., Probst, G. andTushman, M. L. 2009. OrganizationalAmbidexterity: Balancing Exploitation andExploration for Sustained Performance.Organization Science, 20 (4), 685–695.

Rifkin, J. 2015. The Zero Marginal Cost Society:The Internet of Things, the CollaborativeCommons, and the Eclipse of Capitalism. PalgraveMacmillian. ISBN 978-1-137-28011-4.

Ross, J. W. 2006. Enterprise Architecture: DrivingBusiness Benefit from IT. CISR Working PaperNo. 359.

Sabherwal, R. and Chan, Y. E. 2001. Alignmentbetween Business and IS Strategies: A Studyof Prospectors, Analyzers, and Defenders.Information Systems Research, 12 (1), 11–33.

Shaw, N. E., Burgess, T. F., de Mattos, C.and Stec, L. Z. 2005. Supply Chain Agility:the Influence of Industry Culture on AssetCapabilities within Capital-Intensive Industries.International Journal of Production Research,43 (16), 3497–3516.

Silva, L. and Hirschheim, R. 2007. Fighting againstWindmills: Strategic Information Systems andOrganisational Deep Structures. MIS Quarterly,31 (2), 327–354.

Simatupang, T. M., Wright, A. C. and Sridharan, R.2002. The Knowledge of Coordination for SupplyChain Integration. Business Process ManagementJournal, 8 (3), 289–308.

Spósito, M. A. F., Dias Neto, A. C. andBarreto, R. S. 2016. Business-IT AlignmentResearch Field – A Systematic Literature Review.Proceedings of the 18th International Conferenceon Enterprise Information Systems – Volume 1,pp. 549–558.

Strong, D. M. and Volkoff, O. 2010. UnderstandingOrganisation–Enterprise System Fit: A Path ToTheorising The Information Technology Artifact.MIS Quarterly, 34 (4), 731–756.

Tallon, P. P. 2012. Value Chain Linkages andthe Spillover Effects of Strategic InformationTechnology Alignment: A Process-Level View.Journal of Management Information Systems,28 (3), 9–44.

Teece, D. J. 1982. Towards an Economic Theory ofthe Multiproduct Firm. Journal of EconomicBehaviour & Organization, 3 (1), 39–63.

Teece, D. J., Pisano, G. and Shuen, A. 1997.Dynamic Capabilities and Strategic Management.Strategic Management Journal, 18 (7), 509–533.


Teece, D. J. 2018. Business Models and DynamicCapabilities. Long Range Planning, 51 (1), 40–49.

Van de Ven, A. H. and Drazin, R. 1984. TheConcept of Fit in Contingency Theory. StrategicManagement Research Center, Indiana University.

van Donk, D. P. and van der Vaart, T. 2005a.A Case of Shared Resources, Uncertainty andSupply Chain Integration in the Process Industry.International Journal of Production Economics,96 (1), 97–108.

van Donk, D. P. and van der Vaart, T. 2005b.A Critical Discussion on the Theoretical andMethodical Advancements in Supply ChainIntegration Research. In Kotzab, H., Seuring,S., Müller, M. and Reiner, G. (eds.). ResearchMethodologies in Supply Chain Management,pp. 31–46. Physica-Verlag HD.

Venkatraman, N. 1989a. Strategic Orientationof Business Enterprises: The Construct,Dimensionality, and Measurement. ManagementScience, 35 (8), 942–962.

Venkatraman, N. 1989b. The Concept of Fit inStrategic Research: towards Verbal and StatisticalCorrespondence. The Academy of ManagementReview, 14 (3), 423–444.

Wahlster, W. 2018. Smart Service Welt: TechnologicalEnablers for Smart Services. Week 2, Part 5.

Wang, R. 2011. A Taxonomic Study of AgilityStrategies and Supporting Supply ChainManagement Practices. PhD Thesis, University ofExeter.

Weick, K. E., Sutcliffe, K. M. and Obstfeld, D.2005. Organizing and the Process of Sensemaking.Organization Science, 16 (4), 409–421.

Wu, F., Yeniyurt, S., Kim, D. and Cavusgil, S. T.2006. The Impact of Information Technologyon SC Capabilities and Firm Performance: aResource-Based View. Industrial MarketingManagement, 35 (4), 493–504.

Yin, R. K. 2009. Case Study Research: Design andMethods. 4th ed. Applied Social Research MethodSeries 5, SAGE.

Zukis, B., Loveland, G., Horowitz, P., Verweij, G.and Bauch, B. 2008. Why Isn’t IT SpendingCreates More Value? Pricewaterhouse-CoopersReport.

7 ANNEX

Fig. 15: The Supply Chain Dynamic Capability Framework (an author’s view)

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Tab. 6: Dynamic capabilities for SCM IS and digital SC business models (an author’s view)Dynamiccapability

Explorationmethods

Depositmethods

Exploitationmethods

Implication& effects Antecedents Antecedent to

Sensing Data collection,informationextraction

User modelling,Artefactdevelopment

Service &artefact adoption

Autonomousservice adaptionfast ROI

– Inference, Serviceadoption,Sensemaking

Sense-making

consensus onobjectives, levelsof fit and gaps;fit measurements

Artefacts that ascommon view ofcontextcontingency

Utilise artefactsby adoptionalso,alignconfigurations

Common viewand sense ofobjectives &directions toreach these

Transparencyinfo-sharing,Collectiveassimilation &Common view

Commonobjectives, idealplans,transparency &agility

Optimi-sation

ANN, GA, LPOptimisation,Reference Plans

Reference Plans& configuration

Inference;Plan-recognition;configuration

Optimal plans forprofit; fastalignment atvolatility

Sensing,Synchronisation,Planning

Operationalexcellence,contextcontingency

Inference premises,arguments, rules& policies;AI-basedmachine-learning

References; idealplans; premises;Artefacts forrules, policies

Smart Services;SC visibility;autonomoussystems, CPS

Plans, serviceswith ideal levels;Autonomous planrecognition

Optimisation,reasoning,verification,validation

Plan recognition,conclusiondrawing,Autonomoussystems

Integration Misfitidentification byassessment ofintegrationantecedents

Artefact & ISservicedevelopment

Artefactselection,recognition andadoption

Contextcontingency ofcapabilities

Activity-integrationcollaboration,agility and more

Strategic fit,operationalexcellence

Alignment Strategic fitmeasurementalso,consensus

EAM repository,Service Libraries

IS Governance,ontology, DCframework

Contingency fitof capabilities tothe context

Ideal future stateand presentstatus are known


Orchestra-tion

IS components,services, rules,modularconfiguration

Libraries ofServicesArtefacts, ISassets, contracts

Smart ServicesCPS, Servicecomposition

Better RIO, fastand reliablerealignment usingprovencomponents

SensemakingPlan recognition;Consensusartefacts

Businessmodelling,Architecturealignment

Adoption Sensing for thescale of economy,EA architectureHarmonisation

Templates of SCmodel,architectures,processes; ISservices

Template roll-outand localisation(configurationalignment)

Generalityscalability,Effective businessscale-out

Sensemakingbetween host andlocal businessunits, Integration

Successfulreplication ofbusiness models

Reconfi-guration

Sensing, Recogni-tion,artefacts,rules for genericservices

Predefinedconfigurationrules, policiesartefacts

Artefact selectionconfigurationadoptions

Better RIO fromfast and morereliablereconfiguration

Identification andrecognition ofideal plansalso,settings


Inter-operability

Measurements &simulations toidentify levels offit & misfit andgaps

Service andArtefactrepository,aligned SCM IS

Plan recognition,implementationof services andartefacts

Closed gaps ofmisfit,High-levels of fit& businessperformance

Levels of fit forFunction, datacontrol, role,organisational

Integration,strategic fitoperationaleffectiveness

Synchro-nisation

Strategic Fitassessment;Alignment ofdemand andsupply

Master plans andplans references,schedulingcapabilities

Utilise plan- andschedulealternatives thatfit the situation

Operationalexcellence, planand deliveryreliability

SC visibilityOptimisation, SCmodelling,Strategic Fit

SC agilityresponsiveness;Plan andexecutionreliability

Autonomy CPS, SmartServices UMAPmachine learning

Predefinedrulesets andpolicies

Ad-hocconfiguration

Responsiveness tounforeseen events

Sensing,self-learningconfiguration

Acting,self-learningservice

Responsi-veness

Strategic fitassessment ofcapabilities & SCdomains

Continuousdevelopment ofartefacts

Continuous fitmeasures andartefactutilisation

High servicelevels andoperationalexcellence

SC agility; SCvisibility;Integration;Collaboration

Operationalexcellence byeffectivenessservice levels

SC agility Strategic Fitassessment ofcapabilities & SCdomains

Ideal plans;priorities &procedures tosituations

Alignment ofplanningpriorities andexceptionmanagement

Flexibility tochanges, but,remainingeffectiveness

SC visibility,relationshipmanagement;Interoperability

Integration,Responsiveness

Collabo-ration

Strategic Fitassessments

Activerelationshipmaintenance

Activate physicalrelationships

improved agilityand end-to-endperformance

Relationshipcapital

SC agilityIntegrationResponsiveness

Simulation Optimisation, SCmodelling Stresstests

SC artefacts fordifferent idealplans

Artefact, Planselection andimplementation

Visibility on theimpact of changes

Optimisation SCmodelling SCvisibility

Decision-making,SC agilityResponsiveness

SC visibility Strategic Fitassessment ofcapabilities & SCdomains

IS artefacts; SCData models, DBschemas

alignment, ITfootprintsimplification SCmodelling

ImprovedprioritisationTransparency

SC modelling ITfootprintsimplification

Operationalexcellence,Integration SCagility

SCmodelling

ArchitecturalartefactsBlueprints

Artefacts,repository,services

Recognition,adoption,alignment

Improvedvisibility reducedcomplexity

Segmentationdifferentiation SCstrategy

Integration SCvisibility,effectiveness

Activityintegration

Levels of extentidentification byassessments

Artefacts andservice definitionfor needed levels

Utilise artefactsby it IS servicesconfiguration

Smooth flow ofinformation,material andvalue,

SC modellinginformation flowcollaboration

SC agilityIntegration SCvisibility

Appendix Tab. 6 shows DC for SCM, their antecedents and methods for exploring and mate-rialising and deposit, and exploiting these in business performance.


Fig. 16: Examples of Strategic fit assessment sheets

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Fig. 17: Part of the S&OP calculation sheet of case study B


Fig. 18: The upstream and downstream management calculation sheet of case study B

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Fig. 19: The relationship management calculation sheet of industrial case study B

AUTHOR’S ADDRESSJochen Nürk, Faculty of Business and Economics, Mendel University in Brno, Czech Republic;SAP SE, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany, e-mail: [email protected]

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Dynamic Alignment of Digital Supply Chain Business Models