Creating Smoother Collaboration with Data, Analytics, and Methodology

| Product Lifecycle Management
Posted By: Michelle Duerst

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Gartner clearly defines the benefits of an effective Product Lifecycle Management (PLM) system, “The primary business benefit is faster time to market for cost-effective and quality products. This class of software has reduced the cost of executing approval processes to change formulations or labeling by more than 40%. Some manufacturers have cut the time to execute change processes by 70% or more.”[1]

 

How do you find the beginning of a continuous cycle?

Many companies can understand the overall benefits, but cannot determine how to begin.  The analytics, methodology, business processes, and data form a continuous cycle, perpetuating and magnifying the quality levels of each. For example, if you have bad data, the data will continually appear throughout the business processes, influence the analytics, and determine an inaccurate methodology. 

Imagine a string of lights.  Each string equals data, methodology or analytics. One piece of “bad data” can cause the entire string to go dark.  It becomes more complex as you add additional layers, tangling all processes. As with our ball of jumbled lights, IT departments must isolate and untangle the processes before adding more strings.

Gartner explains further, “Adopters should define and validate desired processes before deploying the software or defining data schema to be used in specification and recipe/formulation management.”[1]

 

Building Blocks of Better PLM Implementation

1.      Vision (Who)

Before you can begin analyzing the efficacy of your data, analytics, and processes, you must first understand what is expected and needed from each key driver in New Product Development and Introduction. Keep in mind that while you want to include the heads of each department, you should also include power users of each system.  They can more easily identify the most critical issues that encompass data, analytics, and processes.

 

Read more about PLM Vision and download the PLM Vision checklist.

2.      Data (What)

After defining the vision, or blueprint for your NPDI, you must examine the initial foundation-- data.  There are several aspects that could impact the quality of data: outdated, failed synchronization, partial or duplicate entries.

Your drivers will have provided some key areas to focus.  (Ex: I cannot see the vendor specifications or actual costs in development.)  Use these as the initial map to find the bigger underlying problems.

Read more about Data Harmonization.

 

3.      Analytics (Why)

Analytics enable team members to make key decisions regarding the processes.  These can be long-term or the immediate choices.  All choices impact overall ROI, costs, and profitability. Effective analytics should act as a tool to each user, providing them with the data they need quickly, accurately, and in the correct level of detail.  For example, if there is a new law limiting a defined ingredient to a new level, your team should be able to easily search under these constraints, optimize the formula, and validate compliance.

 

Read more about Recipe Management and download a checklist.

Read more about Labeling and download a checklist.

4.      Methodology (How)

Gartner states, “PLM applications keep audit trails of past actions and changes made to the formulation throughout the development life cycle, which assists with historical analysis and tracking actions in the case of regulatory or compliance issues.”[1]

Methodology acts as the catalyst or engine for your entire PLM implementation.  Best practices will determine that the most effective methodology should be standardized, controlled, and tracked with defined checkpoints. These can be most easily achieved specifications and project management.

 

Read more about Specifications and download a checklist.


[1] Source:  Gartner, Hype Cycle for Process Manufacturing  and PLM, 2014 by Janet Suleski, Marc Halpern, Simon F Jacobson, July 2014