Rapidly changing consumer item and channel preferences, along with the recent step-change transition to omnichannel fulfillment and global sourcing risks, have made retail supply chains a textbook example of a system subjected to VUCA – Volatility, Uncertainty, Complexity and Ambiguity. At the same time, there has never been a greater need to focus on operational execution to drive down costs, while delivering the highest possible customer service.
As a result, retailers constantly experience near-term network flow volatility due to changing promotion calendars, holidays, weather impacts, store resets, supply disruptions, and large assortment updates. Improving on-shelf availability is a constant struggle, and capacity planning is largely managed with manual spreadsheet driven processes.
However, more retailers are investing in AI technologies including Machine Learning (ML), to help with everything from creating more accurate demand forecasts, to streamlining inventory management. In 2021, global retailers are expected to spend $11.8 billion on AI technology compared with $9.36 billion in the previous year, according to market research firm IDC.
For many retail planners, flow planning at various levels of granularity is becoming the best practice to align various fulfillment capacities with volatile demand patterns, across categories and regions. To align capacity with demand, retailers are using flow plans that forecast at 90-day, 21-day, and daily intervals. Here’s an example of how this type of flow pattern can be incorporated into retailers’ demand forecasting process.
90 Day flow plan
For many retailers, it’s a challenge to move goods efficiently, while also responding to merchant asks, and evaluating levers such as adjusting demands, adding another shift at the DC, or accessing flex transportation capacity. Developing a logistics forecast, which involves taking the prior year’s data and applying Machine Learning (ML) algorithms enriched with drivers such as future events at the stores, is critical. Additionally, mid-term scenario planning can drive plans for DC labor, store receiving labor, DC storage capacity (ambient, cold, frozen), and outbound truck capacity.
21 Day flow plan
About three weeks out, real demand filters in through orders at the item-store level. Planning teams need to understand if they have enough staff, dock doors, labor, and material handling capacity to meet the demand. A weekly plan at the item/store/day granularity drives tactical capacity planning, allowing planners to make informed decisions when limitations cannot guarantee on-shelf availability of all items. Planners run various scenarios to understand the cost implications to service demand, so that costly overtime and flex transportation capacity is properly managed to meet margin targets. Decisions are driven by demand prioritization rules set up by the merchant teams, that help smooth the flow of items into the stores and reduce overstocks that result in costly consequences like markdowns or obsolescence.
Daily flow plan
Daily planning is also a necessity, considering near term capacity restrictions. The day plan is driven by the confirmed store order pickups and specific orders can be blocked based on capacity limitations at the DC, transportation lane, or store. This daily flow prioritization prevents backlogs from building up and causing havoc in subsequent days.
Flow planning can deliver ongoing efficiencies in retail fulfilment
In summary, retailers face challenges caused by network flow volatility that can result in bottlenecks due to availability of storage capacity, labor and transportation, and must have systematic processes in place to address this.
Ultimately, flow planning at mid-term, short-term, and daily intervals helps to solve this, allowing retailers to optimize planning across the supply chain, as well as lower transportation and labor costs. The retailer gets operational efficiency in terms of lower total landed cost to the consumer, while at the same time delivering higher in-stock levels at the point of purchase.