Using Analytics to enhance Supply Chain Performance

In the book "Competing on Analytics", authors Davenport and Harris, argue that as business competition gets fiercer, what’s left as a basis for competition is to execute their business with maximum efficiency and effectiveness, and to make the smartest business decisions possible. They indicate that companies, which are currently considered as analytical competitors, are "wring every last drop of value from business processes and key decisions" using analytics. With data becoming easier to collect, store and use, it makes all the more reason for a larger swathe of companies - small and medium enterprises included - to accept the inevitable to train themselves into such an analytics mindset in order to go after maximum efficiency and effectiveness.

The question is no longer about whether companies can use analytics to enhance their operating performance but do they have it in them to become analytic competitors? What are the major barriers to entry for organizations? The following are some of the known barriers for wide adoption of business analytics within companies:

• Leaders not fully familiar/convinced about the ROI from business analytics
• Leaders aware of ROI, but employees (business end-users) not familiar with right tools for their problems
• Leaders aware of ROI, but end users unable to interpret the recommendations from analytics tools and translate them into business decisions.

Let’s take the SCOR Upside Supply Chain Flexibility measure. This measure is described as, “the amount of time it takes a supply chain to respond to an unplanned 20 percent increase in demand without service or cost penalty.” This begins with knowing about the change, understanding the risk associated with the change, and then determining how best to respond to the change. Knowing sooner means having all of the right data along with the analytics that will not only highlight the risks, but also provide insight into your response alternatives.

The ERP system may give you most of the data, but some customer and supplier data may still exist outside ERP. Getting all the data and then applying the analytics, can be difficult, especially if you want to run multiple what-if planning scenarios.

Of course, the most popular “simplified analytical tool” is Excel. It may be easier to get data into Excel but it is unlikely you would have all of it. The analytic piece of the equation is the most difficult to replicate in Excel. The one advantage of Excel over ERP is that it tends to be easier to use. However the challenge becomes similar to using a heating pad to warm up at the rink, it is only good for one. As soon as the Excel file gets passed around, changes are made and then the “right data” question comes back into play. This is important because in most cases any response to the change, for example the unplanned 20 percent increase is not going to happen in isolation.

Davenport and Harris listed four traits of companies that drive them to become strong analytic competitors”

• The need to sharpen an already distinctive capability with analytics
• Complete leadership buy-in about the ROI from analytics
• Commitment to leverage analytics strategically in the long term to preserve this competitive lead
• Enterprise-wide adoption of analytics

There exist strong interconnections between these characteristics, with feedback loops and causal relationships. A company which is already well known for one distinctive capability (e.g. Wal-mart in supply chain management) will want to keep its leadership position and thus emphasize on data and quantitative methods to ensure this lead is preserved. Once the leadership is convinced that analytics is what will provide them with this edge, there must be a decision made to continue with analytics as a strategic initiative.

Finally, there must also be a realization that this will require more than localized adoption in one or two departments. Hence the need/thrust on enterprise-wide adoption.