There is a thoughtful article today by Colin Snow of Ventana in Intelligent Enterprise. In it he points out some of the limitations today in trying to analyze a supply chain. At first sight this may seem odd, since there are well established supply chain vendors like Manugistics and I2, as well as the capabilities of the large ERP vendors like SAP and Oracle. However, just as with ERP, there are inherent limitations with the built-in analytic capabilities of the supply chain vendors. They may do a reasonable job of very operational level of reporting (“where is my delivery”) but struggle when it comes to analyzing data at a broader perspective (“what are my fully loaded distribution costs by delivery type”). In particular he hits the nail on the head as to one key barrier: “Reconciling disparate data definitions”. This is a problem even within the supply chain vendors’ software, some of which have grown through acquisition and so do not have a unified technology platform or single data model underneath the marketing veneer. We have one client who uses Kalido just to make sense out of data within I2’s many modules, for example.
More broadly, in order to make sense of data across a complete supply chain you need to reconcile information about suppliers with that in your in-house systems. These will rarely have consistent master data definitions i.e. what is “packed product” in your supply chain system may not be exactly the same as “packed product” in you ERP system, or within your marketing database. The packaged application vendors don’t control every data definition within an enterprise, and the picture worsens if the customer needs to work with external suppliers more closely e.g. some supermarkets have their inventory restocked by their suppliers when stocks go below certain levels. Even if your own master data is in pristine condition, you can be sure that your particular classifications structure is not the same as any of your suppliers. Hence making sense of the high level picture becomes complex since it involves reconciling separate business models. Application vendors assume that their own model is the only one that makes sense, while BI vendors assume that such reconciliation is somehow done for them in a corporate data warehouse. What is needed is an application-neutral data warehouse in which the multiple business models can be reconciled and managed, preferably in a way that allows analysis over time e.g. as business structures change. Only with this robust infrastructure in place will the full value of the information be able to be exploited by the BI tools.