How Prescriptive Analytics can complement a Social Enterprise in PLM- A Perspective
Sumesh Sadasivan posted on June 12, 2013 |
Social Enterprise technologies are one of the major enablers in business transformation. I have been thinking about how a social media approach to enterprises, and PLM in particular, can best be leveraged to create an impact on an organization.

Social Media in Enterprise
We’ve seen an increasing adoption of Social Media in the enterprise space over the last few years. There are multiple solutions, from independent products like Yammer and Vuuch to products like 3DSwYm from established PLM Vendors . A social approach to enterprise product development can change the way people collaborate in an organization making it more agile and fostering team-work. The impacts can be seen across the entire value chain, be it knowledge management, collaborative design, decision making or customer feedback.

All of this provides tangible benefits, but what more can be done with the information that is generated? A social approach to enterprise creates humongous amounts of information, most of it unstructured. When this unstructured data is harnessed properly it can influence, for example, a product designer by changing his perception of what a customer wants.

Analytics- From mundane reporting to being a crucial cog in business decision making
How do we put this information to best use? How is this going to disrupt PLM? For that, we need to look at Analytics which itself is redefining its relevance in the enterprise space. Analytics, in a reporting form, has been part of PLM for some time. PTC’s acquisition of Relex Software Corporation and Aras-Microsoft collaboration on Enterprise BI are just two examples of an increasing trend towards bringing analytics reporting to the mainstream.

Analytics are moving from a descriptive to a predictive form and thereby providing more value to product decision making. We are seeing further evolution of Analytics to a prescriptive model where it will be able to providedecision support for future business directions.

Imagine a footwear manufacturer that is planning its new line for the summer season. A Prescriptive system should be able to analyze all available information channels and provide an accurate estimate of sales by product line. The sources of information might include:

  1. Feedback Forums- Review and feedback of earlier products from various forums, internal and external. This would provide insights to product performance.
  2. Design Stage Inputs- There could be discussions about earlier designs including audio transcripts, design worksheets, and review comments that provide insights into why a certain design was chosen or not. There might even be un-released designs from which certain aspects can be borrowed.
  3. Historical Data- Sales figures of older models
  4. Competitor Info- How did the competitor's product, in the same category, fare?
  5. Government Regulations- There may be new regulations on materials used in the product
  6. Geo Political imbalance- Any global issues or impending troubles can impact the industry in general.

Below is an illustration of how Social Information and Analytics can collaborate to support a product design process.

How accurate the prediction will be depends on how good the inference model and the prediction algorithms are.

Now, we need to start somewhere to implement this. We cannot look at all the information streams and start doling out prescriptions for each and every thing. That is going to dilute the leverage we can derive and result in loss of focus.

Instead, we need to identify a Business Goal and the strategy to achieve that. Theaction plan to implement the strategy would describe how and what aspects of the information channel to leverage while remaining cognizant of the maturity of its analytics process. Getting such a system to a useful level would require a good number of analysis and feedback cycles.

Looking at the future, I see Social Enterprises and Analytics taking off in a big way by significantly shortening the business decision cycles and improving our ability to forecast what the future of product design will shape itself to be.

About the Author:
Sumesh Sadasivan is a PLM Consultant, software developer and technology enthusiast. Sumesh holds a Bachelor’s Degree in Computer Science and is working in the Engineering division of Infosys Ltd. He has been part of the software industry for more than 13 years and has vast experience in implementing PLM solutions across industry domains and packages.

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