Today’s supply chain is no stranger to big data, at least not when it comes to generating it. Think about the millions of transactions passed between trading partners every day that are triggered by the billions of register transactions caused by retail sales. These deluges of data have been both the lifeblood of the supply chain and part of its biggest challenges.
Standard practice for most companies is to retain data for a limited length of time then roll it off into oblivion. Their only reason for holding on to the data at all is to keep it for accounting purposes. Orders and the resulting document trail are used to verify order processing and payment. Once the time frame for refunds and counter-charges has passed, the order information is no longer needed. At least that’s the old view.
The practice was understandable when costs of data storage were high and analytic tools all but nonexistant. But those days are gone.
Similar logic is pervasive when it comes to retail sales transaction data. The detail of every retail transaction amounts to tremendous amounts of data. On the positive side many retailers offer this data in one of several formats - either as raw data or through a 3rd party provider. There are costs to both methods and the normal costs of storage and analytics apply too. So most suppliers don’t bother collecting POS data as they don’t see the value outweighing its cost.
Changes in profitability
According to a study by McKinsey on the effects of applying analytics to big data are telling. They report, “The average initial increase in profits from big data investments was 6 percent for the companies we studied. That increased to 9 percent for investments spanning five years, since the companies that made them presumably benefited from the greater diffusion of data analytics over that period.” An increase in profits of 6% to 9% on existing products by better understanding the business makes a compelling argument for looking more closely at retaining data that’s easily obtainable.
It seems obvious that knowing more detailed information about where and when products are purchased could deliver insights that could lead to better decisions about manufacturing, shipping, pricing, and all the other activities that affect products. The problem with getting to these insights has been the difficulty in turning multiple silos of data into something that business people could access. The good news is that analytics applications are much more accessible and business-friendly.
This is not to downplay the effort involved in getting to the actionable results. But attaining a significant profit boost by using data that’s easily available makes sense for any company participating in the supply chain.