www.process-relations.com

Process Relations GmbH

Make compliance fulfillment count double

Abstract The need for compliance fulfillment, like ISO quality assurance, information governance, etc., has arrived in R&D. If you like it or not in more and more industries the development of new products is regulated and you need to think early on about how to fulfill all the documentation and other compliance requirements. But this whitepaper is not about how to do that – it is about how you gain more out of the compliance initiative in your company than just compliance fulfillment.

Process Development Execution Systems (PDES) borrow on concepts from Manufacturing Executions Systems (MES) and Product Lifecycle Management (PLM) and provide an infrastructure tailored to support and help compliance in R&D projects. PDES can be used to organize and track the data and information gathered during R&D efforts and therefore provide solid documentation for compliance initiatives. But it is not only about compliant documentation, it provides capabilities to load, to manage and to retrieve data from various sources. It allows engineers to look at historical and current data and to make connections between the results gathered. By doing so a PDES like XperiDesk by Process Relations enhances data and converts it into information that can be used for new product developments.

This whitepaper gives an overview about the requirements and the approaches to make your compliance initiative count double. Not only to fulfill compliance but to go the next step bringing your documentation and knowledge handling to a stage where future projects can learn from previous successes and mistakes. This will make your R&D department ready for future challenges, faster markets and global partnerships.

Whitepaper

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Table of Contents Make compliance fulfillment count double..................................................................................................... 1

Abstract ......................................................................................................................................................... 1

The Challenge ............................................................................................................................................... 3

What is intelligent compliance? ..................................................................................................................... 4

Conclusions ................................................................................................................................................... 6

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The Challenge Let’s be honest, compliance fulfillment in R&D is mostly seen as a necessary evil. It is not liked because it seems to limit the freedom of research & development and creates (at least at first) a documentation overhead with perceived limited value. So let’s have a look at the documentation part of the compliance first.

What you gain out of documentation and compliance should be:

Storage of research data with (authorized) access from everywhere you may need it

Search for research data (either full text or by certain criteria defined in the compliance initiative)

In R&D however this poses several problems:

Data is available in structured (Tables with numbers and units) and unstructured (images, email, documents) form. In fact approximately 80% of the digitized data in a typical company is in the form of unstructured data as shown in Figure 1.

The structured data may change daily or even hourly. New parameters to collect and monitor are found and old ones deprecated.

Search criteria and reports change with each project and even within a project.

Full text search is not enough and it doesn’t deliver the context (In which project was this result achieved? How was the component produced? Where else did we use the same material? Show me all components that work in a range from -50°C to 120°C.)

The context of the data in general is most often not kept or partly lost due to limitations in categorization.

Figure 1: Ratio between structured and unstructured data

This leads to the undesirable result, that data is only used within one project or the live cycle of a component. The learning for future projects is limited since only specialists or people who entered and archived the data in the first place can find and reuse the gained information. And that is basically the situation where the “I told you so” person comes and tells you, that compliance in R&D is of limited use and it becomes more and more difficult to keep people motivated to continue the compliance conform documentation of research projects.

As mentioned, the creation and storage of data today is a technical (solvable) issue. Truth is, with today’s IT it is, in most cases, no big problem to store the large amount of data. An amount that is growing every

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year by approximately 50%1. But that is the crux of it. The data can be stored. Many technologies and methods used are optimized for storage… and are more than 30 years old. While that is fine for archiving your results and thus fulfilling regulatory requirements, in R&D you want to gain information and knowledge out of that data. But when you are drowning in that data with no effective way to retrieve it to generate knowledge for informed decisions, you have a problem. A problem that increases by 50% every year! Recent reports by IDC document that even today 40% of experiments are repeated due to inappropriate storage and retrieval capabilities.

The question is now: How can we break that vicious circle? We can’t get around compliance! So the only logical conclusion is to gain more from the compliance effort than just “documentation”. What we need is a more intelligent form of compliance fulfillment that is geared towards the needs of R&D and provides for collaborative knowledge management and learning.

What is intelligent compliance? The documentation situation in development organizations today can be summarized by the following:

Documentation on file servers, MS Excel, MS Sharepoint, paper notebooks, …

Untraceable and undiscoverable R&D results (people leaving the company, one dimensional search criteria, etc.)

Limited formalized data (numerical data being parameter AND unit aware) available

Formalized data is not really searchable if the units change (e.g. search for temperature in °C if it is stored in K)

Formalized data is “formalized” in different ways by different departments or even different persons even if they describe the same facts

R&D data is not interlinked / related, the context is missing in the documentation

Intelligent compliance needs to overcome these hurdles. But let’s look at some definitions first:

Figure 2: DIKW model2

Data: Data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.

1 Oracle: Information Management – Get control of your Information

2 Following the DIKW model: http://www.systems-thinking.org/dikw/dikw.htm

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Information: data that are processed to be useful; provides answers to "who", "what", "where", and "when" questions. Information is Data that has been given meaning by way of relational connection.

Knowledge: application of Data and Information; answers "how" questions. Knowledge is the appropriate collection of information, such that its intent is to be useful.

So what we are really looking for in R&D (ok, basically in every domain) is at least information or even better knowledge. And intelligent, compliant documentation for R&D should deliver just that! But how do we gain that? Looking in the production area this problem is handled by a multitude of tools from the categories Manufacturing Execution System (MES), Product Lifecycle Management (PLM) and Enterprise Resource Planning (ERP). These tools use a database as background to accumulate data, provide it for evaluation and to derive appropriate actions out of these evaluations. So is this the answer? Simply use a database instead of Excel and be able to search?

Unfortunately the answer is no. Contrary to production, in R&D the data is changing constantly. New parameters to take care of are discovered daily and a big chunk of the data – such as images – is inherently unstructured and not suitable for database storage. Another point that we learn from the definitions is that information needs relational connections between the data points. These relations represent the context of data and transform data into information. But these connections and relations are not always known at the beginning of R&D projects.

A system to manage R&D data must be a comprehensive data repository for structured & unstructured data. It must provide for audit trail with versioning capabilities. The versioning must be applicable for structured (e.g. numerical parameter data) and unstructured (e.g. PowerPoint file) data. It must provide the audit trail with a strong “what was edited” component for formalized data that goes down to the specific parameter edited. This is a special need from R&D that “normal” audit trails don’t require. Comparison between versions of e.g. a processing instruction must clearly show which parameter was changed.

But to handle R&D data it must offer more! It must provide multi-dimensional access and even graphical navigation possibilities. A system to support R&D must cater for multi-disciplined working environments (e.g., providing the electrical engineer with E-Test data and the mechanical engineer stress test data) without media brake. Users must be allowed to simply manage and create relations between different entities (like components, experiments, assessments, projects and processing instructions). By allowing users to create relations between the different entities a semantic web is formed. This semantic web can be used to perform powerful relations based searches (e.g., show all experiments within a project where a certain processing instruction type was used and an assessment shows a certain electrical and another assessment a certain mechanical property). Additionally a semantic web can be used to provide graphical navigation through historical data (e.g., with tree and graph based views) and those enhance the visibility and retrievability of the data (for example looking at the same data from a project manager’s perspective - project driven - and from an engineer’s perspective - component driven).

Other important requirements to allow for data-discovery are sophisticated searching capabilities. These need to allow searching in structured as well as in unstructured data such as documents. While text based unstructured data / files can be searched relatively easily using advanced index services, the structured data management needs to be equipped with physical awareness. This means that searching for a Voltage only really searches Voltage parameters to comprehensively retrieve the information. Both means of searching must be combinable and the system must also be able to use freely definable relations in the searches.

The most important requirements for such a system are the im- and export capabilities. A data management system for R&D must provide means to import data from existing data sources (Excel sheets, File servers, SQL databases, etc.). It must allow to integrate data from diverse sources to provide a comprehensive, holistic picture of all activities, data pieces, etc.. It also requires means to export e.g. search results into other tools (Excel, Statistical software, ERP, MES, etc.) for further processing. It must enable engineers to spend more time on evaluating than on collecting and managing data.

But the import capabilities play other important roles in the compliance initiative: Engineers can continue to work with tools (like MS Excel) that they know! This makes the change for them gradual and the

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transition into the new work methodology is eased. On the other hand the import also enables the system to gather “historical” data. Thus the system is not empty when the work starts and reference data can be used right from the start. The “why should I start with entering MY data” hurdle is considerably lowered.

PDES (Process Development Execution Systems) like XperiDesk from Process Relations aim to fill exactly this role. They provide a centralized platform to collect, evaluate and export data in a multidisciplinary research facility. PDES are geared to cope with the ever changing structured and unstructured data that is experienced in R&D organizations.

Conclusions We have to face the truth – if you want to survive your R&D needs to be compliant with certain rules and documentation standards. Regulatory compliance initiatives can be used – if done right – to gain more than just compliance fulfillment. Since you need to do the work to be compliant you can push changes now that would not be able to be implemented by themselves. Bringing your documentation and knowledge handling to a stage where future projects can learn from previous successes and mistakes will make your R&D department ready for future challenges, faster markets and global partnerships.