In the past year, tens of thousands of words have been written about "big data" and how to manage it. The fact of the matter is that big data is simply a new term for an old condition. Almost from the first day that technology became widely used in managing the supply chain, we've had more data than we could use. Rather than spending time and resources trying to manage what we don't need, I think it might be interesting to try to reduce the amount of data to that which we really can use effectively. This could be particularly important in managing the performance of logistics service providers (LSPs), where in too many cases, outsourcers will become so enamored of data that they measure far more than they need to.
In 1610, Galileo Galilei said, "We must measure what can be measured, and make measurable what cannot be measured." (Over the years, this statement has evolved into the more direct, oft-quoted axiom, "You cannot manage what you cannot measure.") But today, some 400 years later, many supply chain managers still struggle with the application of that premise. Different companies will have different criteria for measuring their LSPs' performance. For example, a pharmaceutical client would be much more concerned about batch controls and error rates than an appliance manufacturer would. But four basic rules should apply over all industries and providers:
Certainly, as our technology continues to improve, we will learn more about our supply chains, i.e., generate more and "bigger" data, and the information no doubt will be helpful. The phrase "Information is power" probably has been quoted on millions of occasions; but in my mind, the real power lies in being able to take the information and use it effectively. This includes rejecting the information you don't need to manage your activities.