As we catch our breath after a tumultuous 15 months, it’s natural to look back and question if any process or mode of operation needs an overhaul. What worked and what didn’t? What capabilities would have been beneficial to have already had in place when it became business-critical?
Retailers understand that accurate demand forecasting requires a full consideration of the complexity and reach of today’s retail supply chain. An accurate demand forecast is key to maintaining a competitive edge, staying profitable and increasing sales. With all that’s taken place in retail since the onset of the pandemic, it makes sense that organizations are reconsidering current demand forecasting processes.
If “are my demand forecasting processes working for me, or am I working for them?” is a question that keeps you up at night, there are three additional questions to ask to ensure you’re meeting the demand of today’s shoppers.
Can you connect the dots between categories across the store?
Shopping behavior is fluid. With the exception of a quick run to grab a forgotten or last-minute item, most people shop across categories. Their basket makeup will vary, but they will buy some amount of fresh produce or prepared foods while also making selections from shelf-stable, frozen and non-food categories. Because of this dynamic, it’s important for retailers to have a holistic understanding of shopping behavior, taking into account demand signals from both the perimeter and the center store. A fluctuation of demand in one area will have a ripple effect into others.
Only 36% of supply chain professionals say they operate on a single supply chain platform, which complicates cross-category demand planning. Because fresh item management has its own nuances and complexities, many retailers will manage fresh-specific demand forecasts in one system, while forecasting for the center store in a disparate view. But, a siloed understanding of demand won’t deliver on the big-picture goals retailers have for sales and revenue. Fresh cannot be isolated from the center store.
Fresh and perimeter items can be fickle to forecast, as they are more sensitive to weather or other external events. In the past year, however, we’ve also witnessed how demand for center-store items can throw store demand into disarray. Items like toilet paper, hand soap and flour were in high demand with pandemic-induced panic buying. How did that reality impact a shopper’s purchases in other areas of the store? If dry, packaged pasta was missing from the shelf, for example, did a shopper opt for fresh pasta from the deli, or perhaps a frozen option? Answering questions such as these is critically important to understanding all influences on demand.
Are you incorporating data sets across all channels to forecast demand and fulfillment?
In late 2019, prior to any notion that a public health crisis would shake things up, Symphony RetailAI asked retailers about their supply chain priorities. Seventy-five percent said that cross-channel fulfillment would drive them to rethink their supply chains in the next five years. This shows an appreciation for the importance of a holistic view of demand across channels, even before the Coronavirus disease (COVID-19) drove consumers to drastically change their shopping behaviors.
Just as a demand forecast has to account for factors across categories, it must also incorporate demand data from all retail channels. This is especially important when it comes to order fulfillment, which was amplified during the pandemic with the explosion of online grocery shopping and curbside pickup. To be effective at omnichannel, retailers’ demand forecasts must be fully integrated into inventory allocation and replenishment, plus click-and-collect capabilities.
Retailers should be able to map demand forecasts to each channel in order to effectively prioritize and execute orders, determining the best source from which to pull inventory. Whether from a brick-and-mortar store, dark store or warehouse, confidently determining the best source for inventory will ensure a retailer fulfills the customers’ needs at the lowest fulfillment execution cost. Plus, a synchronized inventory view bleeds over into accurate pick times and locations for store associates and third-party fulfillment partners.
Struggling to understand the complete picture of demand from each fulfillment channel is not just an immediate problem for things like order picking, on-shelf availability and out of stocks, but for the long-term effects on shopper loyalty. Consistently missing the mark in fulfilling customer needs will be more costly than just the logistical headaches. As a result, these shoppers could be driven to competitors.
Are you able to react quickly enough to meet changing shopper needs?
We’ve established that having clarity around cross-category and cross-channel demand is critical. However, that knowledge alone won’t translate to business success if a retailer can’t take action on those insights immediately, or if the information is irrelevant and out of date by the time that a retailer decides how to execute.
It’s important that a retailer has the ability to understand shifting dynamics as they happen, so that they’re not operating from an unprepared and reactive stance when disruption occurs. With the right technology, a retailer can immediately identify an event as an anomaly that would not normally exist in the demand cycle, incorporating historical and real-time data to make daily or even intra-daily inventory decisions. Analytics can even make recommendations on the next best steps to optimize inventory in response to demand changes.
The ability to react quickly also depends on alignment with other players. Retailers and their suppliers must be more aligned than ever to ensure any supply-chain disruptions don’t result in forecasts for promotions that cannot be accurately executed. When a demand forecast doesn’t recognize a disruptive event for the unique changes it causes in demand, it has a cascading effect throughout the retail organization and with partners, which results in inaccurate plans, poorly executed promotions and strained relationships. To increase agility and improve inventory availability, retailers must share forecast information with suppliers. This collaboration will lead to faster reaction times, better executed promotions, and stronger relationships across the supply chain.
Demand forecasts “work” best when fueled by AI
Demand forecasting works well when there is a perfect balance of people, processes and technology, which is why a unified artificial intelligence (AI)-based forecasting system with machine learning has become the new standard. AI recognizes demand patterns that may go unnoticed by the human eye or siloed forecasting methods, freeing up supply chain managers to execute more strategic work. And through AI, retailers can have confidence in their forecasts, and in doing so will reduce inconsistent inventory buys, overstocks and their resulting markdowns, out-of-stocks, and ultimately, margin erosion.
Ensuring that demand forecasting “works” is incumbent on a complete view of demand. AI acts as an automated data scientist, ingesting and analyzing the massive data sets from all categories and all channels to inform the forecast. Retailers today must have a holistic and interconnected view of demand in order to fully understand what customers want and when. AI-enabled analytics across the supply chain make that possible.