Andrew White

I had the good fortune to attend an IBM SCM event recently (Chicago, IL).  IBM was sharing its updated SCM vision to the market, along with some interesting SCM survey data highlighting priorities facing SCM leaders. I will share more on this later – but there was an interesting case study by Wrigley. 

Wrigley’s Kristen Daihes, Senior Manager of Global Sourcing, presented how Wrigley had used IBM’s solution LogicNet Plus to help with strategic supply chain network design issues.  Such issues are often complex, periodically evaluated, and can result in capital investment activates (such as building, or closing, of plants and warehouses etc.)  Such programs are also very data intensive since complex models of the supply chain, and its possible behavior over time, has to be built.

Kristen described the use of this tool.  She highlighted how fully 60% of the process/project time was spent on “identifying, collecting, and validating data” that is used to model and create a view of the supply chain, while a further 20% was spent actually analyzing the output.  On first pass this might sound odd – why would users spend 3 times the amount of time on “data” and so little on the part that yields the greatest value to the business?

It turns out that the quality of the output (of the model) is highly dependent on the quality of the inputs – the data.  Remember that old adage, “garbage in, garbage out”?  So what kind of data are we talking about? 

• Product, relationships/structures, rules (used on quantity, popularity, affectivity etc.)
• Locations, and all the pertinent constraints (limits, boundaries, throughput limiters, costs etc)
• Lanes, or a representation of how products move from “a” to “b”, and all the pertinent constraints (alternative modes, costs, other constraints, availability etc.)
• Demand (actual orders short term and forecasted demand for many months, even years)
• Resource capacity (suppliers, plants, key bottleneck resources)
• Other data such as calendar, units of measure, currency, tax, duty draw back, etc.

Much of this data is master data: products, parts, and locations etc.  Much is reference data (acts like master data, but not a core entity, such as units of measure, currency conversions etc.  And there are other data that does not fall into either definition easily (such as calendar).  So the learning was this: an affective MDM and broader Enterprise Information Management strategy will help simplify the heavy data management side of such a complex supply chain activity.  

I found it interesting that I took a client inquiry last week that went like this: We see great value in mastering “single view” of master data with MDM – we would like to extend such governance efforts to other data types – how do we go about this?”  There are also many inquiries from users who are trying to establish active governance disciplines as they migrate their business data from legacy to ERP systems. 

This is a worthy question – that seeks to leverage the focus and success of MDM (its discipline) other data that also is shared across the business, but is not a core entity (master data).  This is also a good idea (where sought) but this implies a shift from (only) MDM to a broader EIM strategy.  Enterprise-wide metadata management would be used to manage metadata.  Content Management would be used to manage content.  Enterprise Information Management (EIM) should be used to link all these efforts together and ensure no duplication of effort.

So back to Wrigley presentation…

Another big idea I took away from this very effective presentation was that the quality of data, not just master data, is so critical to many initiatives that relay on data to be shared across enterprises or firewalls.  So much of SCM is like this; so little ERP (HCM, Finance) and CRM is quite like this.  CRM has evolved in this direction in the last few years, but SCM is streets ahead in this regard.  Only Procurement is as close to SCM – simply because procurement and SCM are twin brothers (perhaps CRM is a cousin).  Procurement and SCM have been sharing data with external trading partners for years, far in excess of simplistic transactional exchange.

Another big idea I heard clear from Wrigley’s Kristen was this: before you start implementing any aspect of SCM/SCP, be clear you know what the business question is that is being asked.  She explained that Wrigley had a clear idea of what the business really wanted to evaluate and test in terms of business changes.  Without such clarity, the initiative could have failed.  This is because you will not have guardrails enough to help you gather the right data, and build the right model.  The business won’t know what to do with the data – and won’t relate it to a question, an answer, or benefit.

It was a valuable day out of the office.