To gain competitive advantage from supply chain analytics, companies need to reduce the time it takes to act on the insights those analytics generate.

Hanesbrands Inc., a manufacturer and marketer of basic apparel based in Winston-Salem, North Carolina, is using analytics to close the gap between insight and supply chain responsiveness. For example, the company recognizes that knowledge of a shortage of men’s T-shirts two weeks from now is of no benefit if the minimum lead time necessary to acquire more T-shirts is four weeks. So Hanesbrands is turning to machine learning to design predictive models to sense supply chain issues in time to execute prescriptive measures.

The predictive models incorporate supply chain data from external and internal sources to determine the likelihood of an inability to satisfy demand at a particular time. Once an impending supply chain issue is detected, a prescriptive action can be launched to mitigate it. For example, if an inventory outage is predicted, Hanesbrands will assess available options, depending on the time available for a response. The actions prescribed could involve a change in mode of transportation or a resequencing of manufacturing and purchase orders. Concurrently, the prescriptive model works to balance mitigation cost with the benefit to the company.

If that’s not how supply chain analytics works in your company, you’re not alone. Only a handful of companies have developed an ability to react quickly to supply chain signals. Despite analytical advances, many companies still do not fully understand the sources of their competitive advantage, even though they now have more data than ever about their operations.

The results of a recent study of supply chain professionals we undertook suggest that despite a growing interest in using analytics to better understand complex performance relationships, many companies still struggle to develop this capability as a competitive resource. (See “About the Study.”) Although the supply chain is recognized as a particularly rich area in which analytics could be used to improve performance, supply chain analytics is still in the early stages of development and implementation in many companies.

1 Comment On: Unleashing the Potential of Supply Chain Analytics

  • Dr Rabindranath Bhattacharya | August 27, 2017

    Dr. Rabindranath Bhattacharya, Kolkata, India

    A large-scale survey of North American businesses, found that the primary users of supply chain analytics are operations (49%) whereas supply chain management users are only 24%.
    It is really unfortunate that in spite of having methods/tools and algorithms for use in SCM the users are reluctant only because, I feel, there is no importance attached to operations in supply chain management . I teach both operations and supply chain Management in reputed institutions like Indian institute of Technologies in India and I feel now I could have achieved much higher returns for the company I worked in my service life had I known all the techniques available now.

    Analytical tools have become very very powerful and the only gap lies in the fact that very few people are capable of formulating the problems properly. Once the formulation is correct you can solve with the help of Excel solver, Mathematica or Matlab, which are easily available anywhere in the world.

    For example, I can use the Travelling salesman problem for Milk run system by SCM people for collection of materials from suppliers everyday and save a huge amount of money. This is nothing compared to benefit you can get by using the OR techniques for Distribution and Networking. I believe firmly that Analytic and SCM are inseparable and there must be dedicated people to work for formulation of the problems of SCM and that is where success lies. It requires the knowledge of both Domain as well as software.

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