Many manufacturers grow through acquisition. But a series of acquisitions can bring an organization to a state where there is no common PLM platform. In these cases, there are generally multiple PLM applications catering to different product lines. Developing a roadmap for bringing these applications into a single consolidated PLM entity is a daunting challenge. Applications can have wide variations in business process and functional layers. Their technical implementations can also range from home-grown legacy systems to standard package-based solutions.
PLM consultants often address the unification requirements by proposing merger of applications with similar technical backbones, data models, business processes and so on. Although this looks like a simple low risk approach, aligning customers to such a unification methodology can be a tough job. User communities fear the ensuing changes to their way of working. There are also downstream processes that have to be addressed with extreme caution. PLM consultants generally approach this issue by presenting unification proposals through series of workshop sessions, subjective discussions and presentations. However users may still remain unhappy with how factors for unification have been addressed.
Quantitative assessment approach
To mitigate the risk of these ambiguities, PLM consultants can think of taking a quantitative assessment route as a strong alternative to the conventional qualitative approach. The inputs to the scoring system can be set by the most important parameters that influenced unifying the PLM system. Scoring of different unification options will then clearly differentiate the winners i.e. best proposed options from laggards.
Complete unification through multiple Iterations
Figure 1: Application Unification through Iterative Cycles
This quantitative analysis exercise should be carried out in multiple iterations. The focus of the initial iteration is to target merging of only a subset of the applications, preferably not more than 50-60%. That's because if you try to accommodate a large number of applications in a single iteration, you can give rise to ambiguities around assessment scores that might defeat the very purpose of taking up a quantitative approach. Once all the applications planned in the first iteration have been merged, you can consider that merged entity as a single application for analysis with the other existing applications in the next iteration. The iteration process should continue untill all applications are merged on a single platform.
Iterations: Stage – 1 and Stage -2 Analysis
Analysis preferably should be carried out in two stages – Stage-1 and Stage-2. The objective of Stage-1 is to deduce options that can be good prospects for merging. There can be different approaches for analysis in Stage-1. The simplest way is to take two application combinations and evaluate them against different parameters. Application combinations that are the top scorers are then analysed for deducing possible unification roadmap options. These options then undergo Stage-2 analysis to come up with best aligned roadmap approach in the given iteration. Unification options can also consider application consolidation happening through multiple phases. Scoring for such options are typically taken as the average score across all phases.
Unification Parameters
Some of the influencing parameters for defining a unification roadmap can be:
- Base Package Overlap
- Functional Layer Overlap
- Technical Layer Overlap
- Data Model Alignment
- Interface Consolidation
- Security
- Performance Impacts
- Effort/Cost Influencers
- Business Inputs
These parameters can be analysed in either or both stages. Quantitative models around these parameters should be built up by assigning them weightings and calculating their scores on an absolute or relative basis. Cumulative scores of all the parameters will give the final overall score for an option.
Parameters like Package Overlap, Functional Overlap, Technical Overlap and Data Model alignment should be considered in Stage-1 analysis. These parameters can help in ascertaining the best application merging combinations. Security, Performance Impacts, Interface Consolidation and Effort/Cost Influencers parameters are conclusive factors and should be considered in the Stage-2 analysis.
Figure 2: Scoring results in one of the application unification exercises for a Hi-Tech customer
This Quantitative approach can effectively translate data into easily quantifiable charts and graphs. These are good tools to represent relative advantages of one unification option over the other. It is strongly recommended to involve customers at each of the relevant steps of assessment to bring transparency to the exercise. It's also important to ensure complete alignment with customers on weightings assigned to different unification parameters. This approach can definitely help in overcoming the shortcomings of subjective proposals, but even here a strong factor for success is justification of the entire scoring process through documented data points.
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