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The new omnichannel priority for retail supply chains

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Social distancing measures and shelter-in-place orders, have driven consumers in the U.S. to increasingly turn to ecommerce during the COVID-19 outbreak. The result was an increase in online sales by 25% overall in mid-March of this year compared to early March, as published in the Adobe Digital Economy Index. In addition, online grocery in the U.S. has seen a more than 100% boost in daily sales in the same period.

While consumer purchasing preferences have been shifting to online from brick-and-mortar over the past few years, the outbreak and associated lock-downs  have  further accelerated this shift. These elevated levels of online shopping in the U.S. will likely continue as long as shelter-in-place orders remain in effect. As consumers become used to online shopping with various fulfillment options, such as buy online and pick up at store (BOPIS) or home delivery, there is a likely to be a large segment of shoppers that get used to the convenience and will stick with it, even when social distancing measures are removed.

A new level in the online share of retail

Onlines sales have been growing at a blistering pace for the last decade and accounted for 11.1% of all retail sales in 2019. Estimates are that half of all gains in retail spend in Q3 2019 came from revenue generated online. BOPIS orders surged, with YoY growth between February 24 and March 21, 2020, of 62% as people began applying social distancing in shopping to limit their exposure.

Given the sustained growth of online sales as well as the recent surge due to COVID-19, retailers will have to get ready for new level of ecommerce—perhaps even as high as 16%–20% of all retail sales by 2022, which is a step change over the projection of 13.9% for that year.

The new omnichannel priority for retailers is to accelerate the changes to their supply chains, which were designed for a brick-and-mortar world and now need to address a wide variety of challenges introduced by e-commerce. These challenges range from SKU assortments relevant to consumer demographics, pricing strategies to sustain margin pressures, promotion calendars that span brick-and-mortar and ecommerce, more accurate operational forecasts, efficient transportation lanes and flows, optimal network design (location and capacity of distribution centers, ecommerce fulfillment centers, dark stores), robust inventory and replenishment planning and meeting the high service expectations from hard-to-please consumers.

Retailers can adapt to the new omnichannel landscape with AI

AI is an umbrella term that encompasses a whole range of algorithms supporting business processes such as forecasting consumer demand, allocating inventory, routing delivery trucks, optimizing web search, targeting advertisements and approving consumer loans. AI includes a wide range of mathematical and modeling techniques such as regression, linear programming, machine learning, neural networks, reinforcement learning, optimization and simulation.

AI offers tremendous opportunities for retailers to respond to this step change in ecommerce. With estimated global annual spending on AI by retailers exceeding $7.3 billion by 2022, there is already significant investment in this advanced technology to improve the customer experience, while at the same time increasing operational efficiency.

Key use cases 

There are some key use cases that address the omnichannel imperative in retail supply chains and unlock AI’s full potential.

Supply chain design is foundational

An essential piece to address the coming step change in omnichannel business is the design of the supply chain network. The network of central/regional distribution centers, dark stores and traditional stores needs be able to fulfill orders through methods such as ship from distribution center, ship from store or pickup from store. Traditional AI techniques such as linear programming and mixed  integer programming can be leveraged to design the fulfillment network:

  • Determine the optimal number and location of warehouses and e-fulfillmnt nodes to meet consumer demand using the existing network or start a greenfield evaluation if necessary
  • Understand optimal SKU-Location mapping with network product flows and stocking levels to meet projected demand
  • Plan for warehouse and transporation capacity including the ability to meet surges in seasonal demand
  • Determine the required capacity and product flows to handle returns
  • Optimal labor planning at fulfillment centers to meet omnichannel demand

Demand sensing for agile respnse

To succeed and thrive in what is likely to be the new normal with a step change in omnichannel demand, retailers will benefit by being more demand driven and orchestrate their supply chains to fulfill increasingly volatile demand. Retailers need to predict where demand will occur, across brick-and-mortar and online channels, and efficiently fulfill the right quantity of products to thousands and even millions of locations.

Demand Sensing addresses the critical need for the retail supply chain to respond quickly to changing consumer demand patterns, by leveraging newer mathematical techniques that enable pattern recognition with machine learning, while overcoming the latency issues associated with traditional time-series statistical methods. Demand sensing focuses on eliminating supply chain lags by continuously learning and reducing the time between demand signals (order frequency, order size, distribution center/store inventory, point-of-sale) and the response to those signals.

Forecasting with demand sensing  techniques typically leverage actual sell-thru at the point of consumption, whether it’s at a physical store or an e-commerce channel. An accurate and responsive point-of-sale forecast enables the complex orchestration of the end-to-end supply chain, so that the right item is at the right location, at the right time and in the right quantity. The more accurate the sell-thru forecast, the more efficient the supply chain can respond to ensure the highest customer service, with the lowest investment in working capital.

Demand sensing can leverage machine learning techniques to improve the accuracy of the omnichannel forecast in a number of ways including (a)  the use of newer algorithms such as gradient boosting and support vector machines (b) leverage internal causals such as every day price, placement, offers, digital coupons (c) incorporate a host of external causals such as weather, GDP, new housing starts, interest rates, inflation, debt to income ratios, etc.

Omnichannel fulfillment to ensure availablity at the shelf

The best supply chain design and robust demand sensing would be of limited value, if the item the consumer wants is not available at the shelf (virtual or physical), or if an order promised for store pickup (BOPIS) is not ready in time. This is where the application of AI/ML techniques can be very valuable.

  • Diagnostic: Machine learning techniques can identify the root causes for fulfillment failure. There could be one or a chain of root causes that lead to fulfillment failureitems not in store and have to be procured from a regional distribution center, workforce unavailable for BOPIS orders, missing items at pick locations, limited pickup window duration, excess number/variety of items in the order or inadequate retail store space.
  • Predictive: Once the root causes have been diagnosed with predictive weights for order fulfillment outcomes, the order book can be looked at and machine learning can then predict which ones are in jeopardy. Alerts can be sent to various roles in the fulfillment supply chain so that this can be addressed.
  • Prescriptive: Machine learning can also recommend an action or a sequence of actions to the appropriate parties so that fulfillment execution is enhanced.

The application of AI for supply chain design, demand sensing and supply chain operations has the potential to materially move the needle on omnichannel fulfillment.

Consider a scenario in the complex and highly seasonal fashion retail sector—planning, merchandizing, sourcing, allocation and supply chain teams work up to a year in advance, taking into account a plethora of factors, to get the right items to the right locations. Planning is done across an array of tools (often in Excel) with incomplete data and robust analysis too tedious, and therefore often limited to high-value products, or done at a summary level. Quite often, the business cannot detect shifts in demand signals in time to respond effectively.

As a result, when the product arrives at the distribution center, stores or ecommerce fulfillement centers, it is often not where the consumer demand is—leading to expensive inventory transfers or markdowns.

If this process was orchestrated through AI as described earlier, it might look quite different. At the start of the season, merchandizers select products and quantities to order based on recommendations from an AI engine that has knowledge of historical sales, emerging market trends, macroeconomic factors, new product attributes and category strategies. Then the planning teams review suggestions the system has made on how to distribute the products across physical stores and online channels, to optimize sell-through and minimize markdowns.

The Monday morning jumpstart powered by AI

A common occurrence at many retailers around the world—Monday morning rolls around, and sales and operations teams are frantically piecing together the previous week’s performance. They scramble to put together the sales revenue, measure the effectiveness of promotions and markdowns, identify products that beat their targets or underperformed, uncover inventory issues and assess labor shortages. A lot of blood, sweat and Excel is devoted to root cause analysis of operational issues.

This weekly ritual involves teams from every corner of the business and frequently does not deliver the answers that are needed in time to take action. Now, imagine that Mondays are driven by AI systems. By the time the teams arrive in their offices, the AI engine has done all that number-crunching, highlighted exceptions and served up recommendations to address sales weaknesses, reduce stockouts and grow margins. Now, Monday mornings  consist of less conflict and retailers now receive action-oriented recommendations to move forward immediately, and more time to focus on how to delight the consumer.

This may seem to be a dream that is many years out, but there are many retailers in the grocery, apparel, beauty, electronics and luxury segments that are already using AI to drive significant improvements to their businesses. They are able to layer AI systems on top of existing platforms and leverage existing investments to deliver insights and recommendations in a matter of weeks or a few months—without the need for multi-year transformations or science experiments that do not scale beyond pilot efforts.

Retailers that are primarily brick-and-mortar today, will need to prepare for the coming step change in consumer preference for omnichannel, and leverage AI systems for supply chain design, demand sensing, demand shaping and order fulfillment.

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