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Capacity and flow planning drives efficient replenishment for retailers

Capacity and flow planning (1)
Published: Reading time: 3 min
Vikram Murthi Vice President Industry Strategy
Vikram MurthiVice President Industry Strategy
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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 extensive assortment updates. Improving on-shelf availability is a constant struggle, and capacity planning is primarily 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 were 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 use flow plans that forecast at 90-day, 21-day, and daily intervals. Here’s an example of how this flow pattern can be incorporated into retailers’ demand forecasting process. 

90-day flow plan

For many retailers, moving goods efficiently, responding to merchant asks, evaluating levers such as adjusting demands, adding another shift at the DC, or accessing flex transportation capacity is challenging. 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 must 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 the 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 are managed correctly 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 confirmed store order pickups drive the day plan, 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 fulfillment 

In summary, retailers face challenges caused by network flow volatility that can result in bottlenecks due to the availability of storage capacity, labor, and transportation. They 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 and lower transportation and labor costs. The retailer gets operational efficiency in terms of lower total landed cost to the consumer while simultaneously delivering higher in-stock levels at the point of purchase.

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About the author

Vikram Murthi Vice President Industry Strategy

Vikram Murthi

Vice President Industry Strategy

Vikram Murthi, Vice President of Industry Strategy at o9 Solutions, engages with companies to understand their merchandising and supply chain challenges and helps shape their investment strategy and transformation roadmaps. He has extensive experience in supply chain transformation initiatives focusing on business case development, strategic roadmap planning, leading client workshops and solution definition. Vikram is interested in helping consumer-facing businesses leverage Big Data, Artificial Intelligence, Machine Learning and Optimization techniques to improve merchandising, forecasting, inventory planning, omni-channel fulfillment and new product introductions. Vikram has a B.Tech in Electrical Engineering from Indian Institute of Technology (IIT) in Kanpur, India and an M.S. in Computer and Systems Engineering from Rensselaer Polytechnic Institute.


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