Imre Szabo

There is a constant focus on stock levels and production capacity optimization. We have to plan in a dynamic environment with variable market demands. Internally, however, it takes a considerable amount of time for changes that impact on production to be implemented because of regulatory requirements and difficulties in matching regulatory data with the logistic data. How can we analyse production planning data with regulatory data efficiently and improve controllability?

A lot of communication, manual searching and analyses of the data stored in regulatory and logistics (Enterprise Resource Planning (ERP)) systems is needed before an answer can be formulated. Most companies have this problem because their IT systems are designed for a specific, single purpose and are not developed to handle the multiple aspects of the pharmaceutical business spectrum, as is the case, for example, within logistics and regulatory affairs (RA). The regulatory domain deals with documents and regulatory compliance where as logistics involves working with, for example, data tables, production planning and product ID with a focus on production efficiency domain. There is a considerable gap between logistics and regulatory data exchange and the problem is deeply rooted.

Depending on the type of change, the impact on market authorizations (MAs) can be limited to one or a few dossiers. An example of a change with such limited impact could be a packaging change, but many changes often occur on the lower hierarchical level of the product structure (i.e., on API intermediary technology level) and can potentially impact the submissions of hundreds of MAs. The scheduling of the variation submission because of the proposed change often makes the implementation procedure painfully long.

The solution is to develop an integration tool capable of analysing regulatory requirements and logistics fact data within a company. As regulatory systems are based on submission modules, chapters and documents, and the logistics system is based on material ID, it is important to create a common language or, using IT terminology, a number of common identifiers. We must then assess the differences in identifiers and determine up to what level of detail they play a role. For this, we need to answerthe following questions:					

Is the material ID integrated into an R&D process?

Can the material ID be changed independently from MA variations?

Do you use any higher level component ID that can identify the API or product component data as the MA submission identifies them? 

Is there a code in the technology documentation that is also written on batch record?

Is there a logic in the technology documentation that enables you to compare the different component stages in a long production chain independent of the chemical batches with the content of the drug master file? 

Do you directly use the quality or technology specification document identifiers during quality release? If not, what is then the logic in coding when you release a batch?

What is the process of specification transfer from RA to the system where you release the batches?

The answers to these questions should provide the information required to build a custom-specific analysing tool based on the common identifiers between RA and the logistics system.

The last step is to determine how these common identifiers can be best maintained to ensure the analysing tool remains consistent and reliable (maintenance is to be read as "correct and timely capturing all (planned) changes to the identifiers") and to mould the identifier maintenance into a set of business rules to guarantee that modifications are performed in a controlled way.

The system should now be ready to be put to work.

 is Consultant and Managing Director at Iperion BV (The Netherlands).

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