For organizations, as well as consumers, COVID-19 has had a major impact. Our old day-to-day lives have become alien due to worldwide disruptions, and activities we once took for granted have shifted in new directions. As more purchases have shifted to online, with their expectations of fast shipping and ready availability, the importance of unified, predictive demand planning tools has grown.
Consumer buying habits have been slowly, but increasingly, focusing on e-commerce for some time now. However, the COVID-19 pandemic has caused this process to accelerate rapidly. While the lockdowns in place have certainly caused disruption in consumer’s lives, there are some habits that will remain beyond this pandemic. Online shopping is very likely going to be one of the habits that prevail, given its convenience and wider choice selection it presents.
To meet this omnichannel demand, retailers will have to redesign their supply chains and focus on demand-driven processes to deliver the needs of the new consumers. Here are some ways retailers can achieve this:
Artificial Intelligence (AI)
AI offers one of the most impactful means for retailers to respond to the rapid movement toward e-commerce. A proper AI solution automates routine and non-routine decisions within its own parameters, and flags planners for human resolution when needed. The alerts and notifications planners see are backed by as much knowledge as the system can provide, enabling well-informed human decision-making when decisions are out of scope for the system. AI can help retailers manage all aspects of the supply chain design for the digital age, all while optimizing the online shopping experience for consumers.
Demand Planning Tools Design
One of the first stages in addressing the change in the omnichannel business is the design of the supply chain network. Retailers’ network of distribution centers, dark stores, and traditional retail locations needs to be able to fulfill orders through different methods, such as shipping from a distribution center, shipping from a store, or pick up from a store. AI and inventory optimization can be used to design the fulfillment network by assisting retailers to:
- Determine the optimal number of warehouses and fulfillment nodes
- Plan warehouse and transportation capacity though optimal SKU location mapping with network product flows and stock levels
- Plan optimal labor requirements
To recover from COVID-19 and thrive in this e-commerce shift, organizations will also benefit from being more demand-driven. They must be able to predict where demand will occur across both brick-and-mortar and online channels.
Demand sensing is the ability to pick up on short-term trends immediately, allowing you to predict what consumers want and when they want it better. Short-term forecasting leverages high levels of data granularity, analyzing demand information closer to the end consumer and detecting changes in demand behavior. Rather than working with the same forecast over time, you can challenge forecasts with the latest data and make improvements that boost profits. Demand sensing gives you the ability to analyze demand data rapidly and to decide how and when to act.
Demand sensing makes use of machine learning and AI techniques to enable pattern recognition and eliminate supply chain lag. It accomplishes this by continuously learning and reducing the time between demand signals, such as order frequency, order size, inventory, and the responses to those signals.
Using demand sensing techniques in forecasting typically uses real data collected at the point of sale, whether it’s a physical store or an e-commerce channel. An accurate and responsive demand sensing forecast ensures the supply chain is properly coordinated, which secures the correct item is at the correct location, at the correct time, and in the correct quantity.
Demand can be unpredictable due to short product life cycles, frequent technology updates, and the introduction of new products. It is even more unpredictable in these unprecedented times. Omnichannel demand planning and forecasting activities, such as placement on an e-commerce channel, shipping rates, markdowns, email offers, digital coupons, and social media campaigns, can all help retailers drive sales.
A robust remodeling of these demand shaping activities can greatly benefit from machine learning and AI techniques. Using these techniques enables planners to run what-if scenarios. This allows planners to look at the impact of changing the timing or duration of promotions, plan different placement strategies, and see the impact that discounts or free shipping could have on expected online orders.
For retailers who are still not prepared for the rapid omnichannel switch, now is the time to take action. The change is no longer a matter of if, but rather, when. If they do not, they may go the way of Woolworth or K-mart. For retailers to react swiftly and effectively, they need to adopt AI and a state-of-the-art Demand Planning tools like Vanguard Predictive Planning.