Since the beginning of the COVID-19 pandemic in March of this year, we have implemented guidelines on social distancing, executed stay-at-home orders, and applied restrictions for brick-and-mortar stores. This has led to months of store closures, which made e-commerce the primary sales channel for many businesses.
These precautionary measures will have a lasting impact on consumer purchasing behavior long after the pandemic, meaning, consumers will continue to shop online for easy pick up and home delivery. To adapt accordingly, organizations will need to offer omnichannel capabilities and create a seamless experience between their online and offline offerings.
Big-Box stores have fared well during the pandemic due to previous investments in inventory visibility, already having on-line systems in place enabling shoppers to see available stock, and being able to pick up orders from nearby stores. As such, it was naturally easier for these organizations to shift operations fully or partially to curbside pickup. Other retailers have not done as well. This is likely due to fewer resources, lack of connectivity, and segregation of inventory between online and in-store inventory management systems.
However, Multi-Echelon Inventory Optimization (MEIO) advances inventories across the network to improve service levels while lowering costs. This level of connectivity enables insight into in-store inventory and seamless execution of on-line orders.
Leveraging Sales Data
Initial store closures caused a spike in online shopping traffic for which few retailers were prepared. Added to that, retailers had to start operating with reduced workforces to maintain safe distancing between employees and this has led to fulfillment delays. To avoid any additional challenges, retailers need better foresight to anticipate demand fluctuations and adjust their operations.
This is an area where demand sensing can help by making large volumes of data available to support decisions at all levels of business. Data can be collected in real-time allowing decision-makers to proactively understand:
- How much demand will rise/fall?
- What is driving demand?
- Sources of demand?
Knowing this kind of data allows distribution centers, fulfillment partners, and stores to prepare well in advance.
Organizations don’t have to calculate demand forecasts manually. With artificial intelligence (AI) models, organizations can produce accurate projections by considering multi-dimensional dynamics. Instead of spending time digging into data, retailers can use AI-generated insights to stay ahead of the curve by appropriately staffing warehouses, planning shipping routes, and ensuring timely fulfillment across all channels.
Ultimately, these strategies come down to transparency, connectivity, and agility. When organizations make upstream decisions based on real-time downstream data, it not only benefits omnichannel fulfillment and meets consumer expectations, it also builds adaptability and resilience for the future.