Process excellence in master data management for life sciences manufacturers

It makes sense to analyze and ensure that the foundations of master data governance are solid and provide the business with strong processes to manage and share master data across development, finance, operations and quality assurance.

Speaking one common language, breaking down the silos and sharing one single version of the truth sounds like a very obvious advice, however, there often is a gap between common sense and reality. How many companies have a high quality, validated, shared and secured central repository for company master data? Who owns the data and who is driving the data maintenance processes across your organization?

We need to challenge the status quo on how we work in the area of master data management, go back to the fundamentals and come out with new ideas on how to become more agile and lean as an organization.

1. Challenges in MDM
Master Data Management (MDM) is a crucial and complex process needed for timely introduction of new pharmaceutical products, biotech products or medical devices. Figure 1 below depicts the product life cycle and the required master data activities from creation through to changes and deletions. Creation of master data starts very early during in the product life cycle. Well before the product has been launched, there is a vast amount of master data activity required for registration, demand planning, production planning, production, quality, finance and distribution. After the product is launched the master data must be supported and maintained, additional country launches addressed as well changes and deletions will need to be well managed.

Figure 1: Product life cycle and master data activities

The problems that might arise are not only in the area of master data changes and deletions. Life Sciences companies must be aware that there is a risk of delayed or even missed product launches if there are delays or issues in master data creation. The effort to mitigate such risks is extremely high. Therefore companies should be constantly striving to implement better processes and improve the organization in order to reduce the costs associated with this risk.

Since the stakeholders in this process are spread across the organization at R&D sites, regional or corporate logistic centers, financial shared service centers, sales branches and manufacturing plants, the coordination of master data activities is highly complex. There are numerous people involved in master data management at various levels. Experts from across the organization are required to ensure the content and accuracy of the data while others are required for general maintenance activities.

Table 1: Work time per stock item

* In case of creation of simple products, the effort is very low while the number of people involved is not much different for medium or very complex products. Those metrics are not shown in above table as our analysis of high number of interactions for low work time becomes even more significant.

In a survey, Life Sciences companies rated their master data issues resulting in the following 6 statements:

Figure 2: Survey results

These issues and other need to be addressed by Life Science companies by analyzing the processes and organization to gain insight and identify opportunities for improvement.

2. Best practice model for Master Data Governance
After gaining insight in to the issues being faced the company can then identify high level needs to build a best practice model for Master Data Governance. Different maturity models exist to evaluate the company's current situation and to identify gaps that need to be closed before reaching the next level. In practice a company must validate the needs to be covered in an implementation project, for example:

1. Give master data the right priority and implement one overall senior process owner to manage the end-to-end process. This role collaborates with sub process owners for functions like Development, Manufacturing, Quality Assurance, Regulatory, Finance and Logistics.
2. Establish clear roles & responsibilities on local, regional and global level. Centralize where necessary and leave local flexibility to speed-up the process.
3. Define the right level of information required. Ensure that there is no data overload resulting in data graveyards and inaccurate master data.
4. Assign the tasks to the people having the relevant knowledge. Try to automate rules where possible to relieve the experts or reduce unnecessary interactions.
5. Adequate training of people must be ensured.
6. Use a staged approach to create or modify master data: upstream business processes like forecasting must be executed at an early stage.
7. Support Master Data Management with reliable workflow processes that have triggers and timing agreements across users.
8. Specific processes like Technology Transfer (change sites) need extra attention.
9. Implement process performance measurements for Master Data Management.

From our experience we have concluded that the needs of many Life Sciences companies are converging.

With and effective and balanced Master Data Governance model in place companies can then look for opportunities to leverage technology to further streamline and improve master data processes.

3. How SAP technology can help
SAP technology may significantly streamline and improve the master data creation and maintenance processes. The client case below illustrates the improvements of a master data solution that can be achieved in a heterogeneous system environment.

Client Case
A global pharmaceutical company was running their product portfolio management and product launch processes in a home breed system (non ERP / non SAP based). The related master data was spread across several systems. Best of breed and legacy tools were used for marketing processes like artwork for packaging, print publishing and catalogue management. Even the code generation was triggered from a non ERP based system. This highly heterogeneous landscape resulted in numerous interfaces, redundant data and inefficient processes.

The proposed solution was to store the data for all processes in tool that was solely focused on Master Data Management. This allowed users the benefit of having one single point of entry for all data and processes via a portal or web enabled user interface.

This "single source of truth" solution resulted in fewer interfaces, non redundant data and processes which are highly integrated into the overall landscape. Furthermore the architecture has enabled the organization to integrate internal and external web services (e.g. translation management). Data is now harmonized and distributed to the target systems independently.

A phased implementation approach resulted in the end-state architecture in Figure 3.

Figure 3: Master data architecture of a pharmaceutical company

Table 2: Definition of different layers

Experience has shown that a heterogeneous complex system environment requires a central repository for master data functionality with web access and easy distribution & workflow functionality. However it must be clear that systems are only tools and are there to facilitate and support the overall process. Many master data initiatives fail as they are tool focused and do not sufficiently address the governance issues. Excellent organizations should first define all dimensions of the master data governance model before considering a MDM systems implementation.

4. Conclusion
In conclusion Life Sciences companies should consider and address their pain points in order to create a more lean & robust Master Data Management process.

The integrated process should span across all departments covering all stages of the product lifecycle.

Figure 4

It should become a key business objective to create accurate, compliant, on-hand master data resulting in a significant reduction in costs for master data management.

5. Authors

Michael Stein, Partner and Executive Board Member, Switzerland.
Michael has been involved in SAP enabled business transformation programs for more than 15 years mainly in the Life Sciences industry. In his client delivery role he supports clients in ERP Transformation programs, ERP strategy, organization design and master data initiatives.

Geert Crauwels, Director, Belgium.
Before Geert worked as a consultant, he gained a broad experience in the Electronics and Machine industry in supply chain functions and he was responsible for various change and performance improvement projects in an international context. Geert has 11 years consulting experience, mainly into supply chain management projects in logistics centers, manufacturing plants and distribution. Geert is focusing both on innovative Life Sciences SAP projects and on SCM transformation projects.

Martin Schiesser, Director, Switzerland, Head Practice Unit "Master Data Services" and managing the SAP Special Expertise Partnership for SAP MDM/PLM.
Martin has Industry knowledge in industrial companies (High Tech, Automotive, Discrete and Process) and in telecommunications with focus on PLM, MDM and CRM. He has 15 years of SAP project experience in global transformation programs able to provide end to end implementation support from strategy development to implementation.