Creating an analytics organization for supply chain
I’ve been leading a few calls for the International Institute of Analytics, as a faculty lead for the manufacturing group. We are preparing for the Chief Analytics Officer meeting coming up in Chicago in June. One of the issues that comes up is where the analytics function should reside – in IT or in the business function?
We have heard from people on both sides. The people in the business function complain that IT is “old school”, and that “we can’t rely on IT”. They are saying “we need to down the analytics platform, and define the roles and responsibilities of that area.”
Another individual from a newly formed mfg. analytics group noted that a lot of what they had done was in constraint management and inventory and demand. But most of the work that had been done was done internally – and not by IT. IT “stores it in places, but doesn’t know how to create a good analytics foundation.”
I believe that IT organizations need to be viewed as a partner. For instance, one IT analytics leader we spoke with noted that “we want to be partners with the business – and understand business needs and what drives the business. We are focused on building an enterprise data warehouse, and providing the tools (in this case, in an Oracle environment) that allow people to click and drag and build their own reports, to get summary trends and detailed level data on what customers are ordering on a day by day basis for any product line at any region in the world. It is viewed as “putting the platform out there and making it easy for people to create their own analytics.”
Another issue that comes up often is related to the process used to focus on an analytics problem and application. This is an area where analytics can play an important role in leading the business function team to the right endpoint. You need to be able to provide people with a “can-do” attitude. This means being able to provide a definite process for engagement, which begins with identifying the problem, and building a set of hypotheses around what the relevant data are important that can provide insight into the problem.
One of the things that is important is being able to open up your ears and eyes to drive creative thinking into other types of data that might be available that are not currently in your standard database. Is there proxy data that can be used to approximate other related variables that can provide good insights into the parameter of interest. Are there other pieces of data that might be directly related but correlate to what we are trying to solve? This could include public domain data or economic data. Don’t just limit yourself to one or two data sources, but drive creating thinking. This is what I mean by a process.
In the supply chain area, we are certainly seeing a strong need for analytics in areas like collaborative forecasting, inventory management, production planning, and logistics network design. One of the big themes is the need to drive end to end integration – so that demand sensing algorithms can provide early warning of surges (or slowdowns) in demand, that can be tracked and driven back into the supply chain, beginning with improved warehouse and distribution management, transportation management, production planning, and supplier capacity and order collaboration. People need to have metrics that drive transparency on events both in the short-term, but that also provide input into broader strategic decisions, such as longer-term needs for capacity, or even slowdowns that translate to postponing major capacity investments.
There is also a huge need for analytics in the area of talent management for supply chain and manufacturing. We are working on a project looking at future talent requirements, and understanding what you need to be doing today that will translate into requirements for people and talent five to ten years from now. We are working on building a talent management analytical tool that we believe is unique, and are looking for people to help validate it.
