Jul 15, 2021 4 Min READ

How to Utilize Supply Chain Predictive Analytics

4 Min READ
How to Utilize Supply Chain Predictive Analytics


Last week we showcased the winner of our scholarship essay contest, in which we asked students studying Supply Chain or Logistics Management to describe what they believe to be the next big advancement in supply chain or warehouse management. 

As we noted last week, this scholarship was created not only to help the aspiring youth in their academic endeavors, but to help the industry as a whole. During conversations with our warehouse management system (WMS) customers, they shared the difficulty in finding good talent for their warehouses. Talent with vision, talent with expertise, and talent with drive to take their third-party logistics (3PL) business to the next level.

To assist in the development of these future logistics professionals, Extensiv created the Supply Chain Scholarship. It is our hope that these scholarships will get future supply chain and logistics professionals thinking about how they can evolve our industry—thus helping our customers, the supply chain, and end consumers counting on the supply chain to deliver their goods.

This week, we'll be showcasing the essay of one of the runner-ups, Alexandra Pirsos, as she describes the place of supply chain predictive analytics in improving efficiency. We hope you find value in her vision of the future events. And without further ado, here is Alexandra's essay describing the role of supply chain predictive analytics.

What would shopping look like if the supplier knew exactly what the customer wanted and when they want it? While this seems like the start of a science fiction movie plot, the truth is that supply chain predictive analytics are already in place to aid. The real question then becomes how can suppliers efficiently utilize supply chain predictive analytics to meet their goals? I believe that answering this question will be the next big advancement in supply chain management.

Before exploring the question above we need to come to some basic understandings of the impact of analytics on supply chains. Within the exploding world of artificial intelligence, there are many different analytical approaches including descriptive, predictive, prescriptive, and cognitive. All of these techniques use advanced technologies in order to understand the customer and their needs. When trying to optimize supply chains, these data-driven insights are crucial to adequately stock stores to keep up with supply and demand. Utilizing analytics is incredibly useful in order to reduce costs, minimize waste, monitor stock, prepare for future changes in demand or predict future demand, predict risk management, and so much more. A lot of these concepts are easy to understand with common retail examples, but let’s challenge ourselves to compare these concepts to the world of banking. Knowing the supply of money in the ATMs, the branch, the vaults, and the central bank alongside the demand of customers as they take out and deposit money will allow the branch to efficiently track their transactions. In addition, by utilizing customer information, they can predict if a new customer would default on a loan allowing them to make better business decisions.

Another common example of analytics in supply chain management is market basket analysis with ecommerce stores. Market basket analysis is used to determine what items customers typically put in their shopping carts at the same time. This is helpful from a marketing perspective because the data analysts can recommend similar products or products likely purchased together to their customers in hopes of increasing the monetary value of the shopping cart. From a supply chain perspective, this is also incredibly useful information because companies can increase the production of item “B” when the demand for item “A” increases. Staying ahead of demand changes will ensure that customers can purchase what they want when they want it which in turn will increase profits and reduce waste.

We can already see those analytics provide a robust opportunity for many different businesses supply chains, so what new advancements are coming into this space? In order to build upon successful predictive models, I believe that supply chain analysts need to capitalize more on the in-depth data they collect on their customers. One failed example of this concept is the Amazon Dash. After a short run, the Amazon Dash was discontinued in the winter of 2019. This product was a button that you could purchase for common household items like toilet paper or detergent, and with the click of a button, shoppers could automatically reorder the product they wanted. Why didn’t this work? The idea was great in concept because it allowed shoppers to easily get the products they needed; however, it was honestly too much work for the customer. Each button was personalized for one specific product, which meant that in order to be effective each shopper would need tons of Dash buttons around their homes. In addition, they had to actively click the button which in reality would not save them much time as compared to clicking reorder. This is the perfect example of how a company tried to better utilize personalized customer information to improve its supply chain but failed.

To see success in this area, which I do think is possible, companies need to take control of the hardest step of the shopping experience, remembering to order. By using supply chain predictive analytics, large companies like Amazon will know that shopper X always buys the same new mascara in March every year or that the first week of each month she purchases the same bottle of shampoo. Currently, supply chain predictive analytics and market basket analysis uses this information to make suggestions to shoppers when they’re shopping. But how beneficial would it be if a retailer like Amazon emailed customer X in March and said, “it’s the time of year you’re going to need a new mascara.” The Amazon Dash didn’t work because it relied on the customer to remember what they needed when they needed it and that they have to take action on it. Instead, suppliers, especially those in the retail space, need to take that step away from the customer and instead present back to them the information that the business already knows about them.

Of course, some suppliers may be skeptical of the ethical considerations of historical data collection and usage. With that said, I think the most ethical way to utilize a customer’s data is in order to help them have a better shopping experience. Data should not be sold to other suppliers for their advantage, but if a customer puts enough trust in a company to provide their own information, it is the company’s job to use that information to help the customer. Overall, I think this strategy would be extremely impactful to the supply chain management world. As the world continues to open up with the pandemic in a promising, manageable spot, consumers are going to be busy re-adjusting to their new normal once again. This is the ideal time to use pre-existing customer data to benefit their customer experience as well as the efficiency of internal supply chains. When analyzing the future of supply chains, we’re lucky to know that we already have the tools needed to reach success, now it’s all about using those tools in the most effective ways possible.


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