Brick-and-mortar shopping experiences have been rare since the beginning of the COVID-19 pandemic, and there has not been a more convenient option than to shop online. For some time now, consumer buying habits have shifted toward e-commerce; however, the pandemic has accelerated this shift. The chances are that the more consumers become accustomed to online shopping, and the convenience it offers, the more likely they will stick with it even when social distancing measures end.
That leaves retailers with the looming question of how to re-engineer their supply chains to fulfill the demands for the omnichannel imperative.
Supply Chain Design
The first stage in addressing change in the omnichannel business is the design of the supply chain network. An organization’s network of distribution centers, dark stores, and traditional stores needs to be able to fulfill orders through multiple methods. Retailers can design the network to meet these increased demands by using these AI techniques:
- Inventory Optimization determines the optimal number and locations of warehouses and fulfillment nodes.
- Capacity Planning optimizes warehouse and transportation capacity through SKU-location mapping.
- Product Portfolio Planning determines the required capacity and product flows to handle returns.
- Workforce Planning optimizes labor requirements at fulfillment centers.
Organizations benefit from being more demand driven. To succeed and thrive in this retail shift, an organization must be able to predict where demand will occur across brick-and-mortar and online channels, and efficiently fulfill the correct quantity of products to many locations.
Demand sensing uses machine learning techniques to enable pattern recognition and eliminate supply chain lag. Provided you have the right data, demand sensing can do this by continuously learning and reducing the time between demand signals, such as order frequency, order size, inventory levels, POS, and the responses to those signals. But planners need to avoid being too sensitive to small signals and changes. This is why planners are still needed as part of the marriage with technology.
Omnichannel demand shaping activities, such as placement on a website, free shipping, markdowns, promotions, and social media campaigns, all help organizations drive sales. Robust modeling of these demand shaping activities can greatly benefit from machine learning techniques. Using machine learning, organizations can run what-if scenarios and look at the impact changing the timing and duration of these promotions, different product placement strategies, discounts, or free shipping would have on expected online orders.
These techniques can be used to identify the root causes of fulfillment failures and recommend actions to the appropriate areas so that fulfillment execution is enhanced. The application of AI-enabled technology allows retailers to detect shifts in demand in time to respond swiftly and effectively. Retailers that are primarily brick-and-mortar today need to prepare for the changes in consumer preference for omnichannel brought on sooner than expected due to COVID-19. Leveraging a cloud-based AI system, like Vanguard Predictive Planning™, enables organizations to use supply chain design, demand sensing, and demand shaping to enhance their omnichannel presence.