A woman uses a handheld mobile computer to scan the barcode of a package being prepped for shipping from a retailer
By Guy Yehiav | March 23, 2020

Retailers: This is the Truth about True Demand

Demand planning isn’t as simple as it used to be, and traditional demand planning doesn’t necessarily help you assess “true demand.” Here are three reasons why, and how you can harness it.

With the COVID-19 novel coronavirus on the rise worldwide, retailers are being forced to rethink their supply strategies on the fly. Demand for sundries and medical essentials is booming – especially via “buy online, pick up in store” (BOPIS) as well as curbside and home delivery channels that aid with social distancing. Sometimes this requires inventory relocations or fulfillment from stores outside of customers’ locales. Therefore, retailers really need to assess where the demand truly originates. Then they can adjust their fulfillment behaviors to keep up with this trend and understand what further changes should be made as demand fluctuates in the coming months. There are several ways to successfully navigate this new challenge, but they all start with the same first step: identifying and understanding true demand and the ability to sense demand as close to real time as possible.

Determining “True Demand” Can be Demanding without the Right Analytics Tools

Unlike more traditional retail demand calculations, which were made by simply looking at historical data and making a prediction based on past trends, “true demand” is now based on date, time and geolocation, which is the location to which the demand should be attributed rather than consumed.

Why the change?

Physical, in-store inventories are no longer the only, or even main, metric of demand consumption. The rise of BOPIS, “buy online, return in store” (BORIS), ship to store, ship from store, direct home shipping from warehouses and even drop shipping from manufacturers has forced both brick-and-mortar and e-commerce retailers to assess inventory availability, fulfillment trends and consumption patterns in an entirely new way.

It’s not enough to know where somebody bought a given product. You also need to know where the buyer is located and where the product is being consumed. This information can be answered by leveraging geolocation — the hallmark of true demand and the key to understanding demand well enough to optimize your supply chains in the modern retail environment. For example, imagine I ordered a winter coat online from a national retailer to be shipped to my house in Boston. If this order was fulfilled and shipped from one of the retailer’s local Boston stores, the true demand location for this item is simple: it’s Boston, which is where the product was bought, fulfilled (i.e. from a Boston-area store) and consumed.

Now, say the retailer’s Boston store (the ideal fulfillment location) is sold out of these coats and the order must now be fulfilled from a store in Atlanta in order to optimize cost and shipment time.

Where should the demand be attributed, and, by extension, to where should the product’s replenishment be reallocated after the depletion of the goods? Should it go to the Boston store, where I live? Or should it go to the Atlanta store, from where it was fulfilled? This is the dilemma that many retailers face when trying to gauge true demand and optimize their supply chains.

Assuming the retailer has an analytics solution with true-demand allocation capabilities, the retailer would find that the answer is still Boston — the order was fulfilled in Atlanta, but Boston is where the demand originated and where the product will be consumed. Demand should be attributed to Boston, and the retailer should allocate more of the coats there to accommodate demand.

Why Is Prescriptive Analytics the Best Tool to Determine True Demand?

Predictive analytics solutions have been used quite extensively in demand planning over the past decade or so. But the truth is that only prescriptive analytics can combine all these data points – and more – to accurately calculate your true demand.

In one increasingly common situation, we see prescriptive analytics used to determine the impact of gift shipping during demand planning.

For example, say I decide to buy that same winter coat; only this time, I send it to my nephew in Detroit as a birthday present. Now where should the retailer attribute the demand? Should it be attributed to Boston, where I (the buyer) am located? To the Atlanta store that will fulfill the order? Or to the Detroit area where my nephew (the consumer) is located (and therefore the product’s final destination)?

A less-advanced analytics solution might tell you the answer is Atlanta, the fulfillment location, meaning that additional coats will be allocated there. But that doesn’t make logical sense – Atlanta’s winters are usually mild, and people are unlikely to need heavy coats. You will suffer margin erosion from shipping more unneeded coats to Atlanta, as well as marking down the unsold inventory at the end of the season.

A prescriptive analytics solution with true demand capabilities will identify the correct answer as Detroit, where the consumer lives and where more coats will likely be needed. In addition, the solution will automatically adjust the retailer’s allocation algorithms accordingly. This is not a capability of less-advanced analytics solutions, which neither properly assess true demand nor provide any actionability on their insights.

Actionability is crucial in supply chain situations like this. Don’t forget that demand planning is just that – planning. It does not give you, your stores or your fulfillment partners directives on what to do in the moment to adjust demand. Prescriptive analytics is the only way to effectively and efficiently optimize your global supply chain, as increasingly complex demand drives greater dependency on imports and omnichannel fulfillment and therefore more advanced model stocks.

Remember, customers don’t care how their orders are fulfilled as long as they arrive at the requested locations at the promised time. But that’s exactly why you need to invest in advanced analytics tools that empower you to create a profitable, reliable and flexible supply chain with optimized cost-to-serve models. One miscalculation can result in a disappointing customer experience. It can also lead to lost sales and, in some cases, lost customers. Let’s work together to ensure your retail operation is equipped to identify and react quickly to current market demand, particularly given the effort required to maintain proper inventory levels throughout your business.

You can learn more about prescriptive analytics’ many benefits on Zebra’s website or contact my team to talk about how this robust solution can improve your inventory management and fulfillment operations. 

Retail, Retail, Retail, Retail, Retail,
Guy Yehiav
Guy Yehiav

As General Manager of Zebra Analytics, Guy Yehiav is responsible for setting the organic and non-organic growth, leadership strategy, and customer success for the Zebra Analytics business unit.

Formerly CEO of Profitect, recently acquired by Zebra Technologies, and a 25-year veteran of the supply chain industry, Mr. Yehiav has held senior leadership positions at Oracle and was previously founder of Demantra US (acquired by Oracle in 2006).

Fluent in English, French, and Hebrew, Mr. Yehiav has a passion for teaching, which started with educating high-school students pro bono in his native country of Israel. He continues to teach pro bono, now as a guest lecturer on professional selling, entrepreneurship, and statistics for the Massachusetts Institute of Technology (MIT) and Babson College.

Mr. Yehiav holds a Bachelor’s degree in Computer Science & Industrial Management from Shenkar College of Israel and an MBA in Entrepreneurship from Babson College. He currently lives in Wellesley, Mass. with his wife, Maya, and their three daughters.

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