Read this blog to discover the retail challenges solved with AI in Inventory Management
Consumer shopping habits have changed significantly due to COVID-19, impacting retailers through 2020. While vaccines have become available and restrictions on social gatherings have lifted, many of the shopping behaviors adopted in 2020 are here to stay. Modern retailers need to adjust to anticipate what consumers will be buying, at what frequency, and through what channel. With advances for AI in inventory management, an increasing array of solutions are now available.
In 2020, consumers spent $861.12 billion online with US merchants, 44% more than in the previous year. Throughout the year, grocery businesses transitioned from 98% foot traffic business to fulfilling orders across multiple channels. This extreme shift put a significant amount of stress on operations, supply chains, and retailers’ gross margins. As consumers were panic buying in March of last year, retailers were making strategic buys in anticipation of increased demand for specific categories and items. To capitalize on demand spikes without risking out-of-stock inventory, retailers need to have a resilient and agile supply chain to respond quickly and maintain service levels while offsetting increased costs.
How do you keep your entire supply chain agile and flexible without increasing operating costs? And how can you do all of this while placing the right product, in the right place, at the right time to meet consumer demand? By leveraging an AI-enabled forecasting solution, such as CCH® Tagetik Supply Chain Planning, retailers solve these challenges and benefit from AI in inventory management.
Challenge One: Carrying Too Much Inventory
In their desire to meet customer expectations and provide seamless experiences, retailers are frequently vulnerable to over-stocking products. These overstocks, while ensuring availability, increase storage and logistical costs. For grocery companies, overstocks are particularly troublesome due to the added effect of product spoilage and expiration.
With the pressure to reduce inventory and increase customer satisfaction, retailers who leverage AI to align business strategies reduce excess inventory levels through algorithmic optimization calculations and machine learning. CCH® Tagetik enables supply planners to leverage “what-if” scenario planning to help them understand service and cost alternatives that put the correct stock levels in place and release millions in working capital.
Challenge Two: Not Knowing How Much Inventory is Needed
When disruptions occur, this results in disappointed customers, lost opportunities, or a surplus of inventory, not to mention the rising costs to fulfill the unplanned demand. The wrong mix is just as costly as too much inventory.
The goal is to anticipate rather than react to changes in demand through predictive forecasting for optimal inventory replenishment. Inventory replenishment process details vary for every organization. For best outcomes, use a supply chain planning platform, such as CCH® Tagetik Supply Chain Planning, to achieve revenue targets and drive the right mix of inventory through:
- Seasonal forecasting
- Omnichannel optimization
- True lead time
- Support for bills of distribution and bills of material
- Automation of inventory strategies
Challenge Three: Inventory in the wrong place
COVID-19 has shown that companies cannot afford to be out of stock on critical items; otherwise, they risk losing customers. It is not enough for a retailer to say that they have it, but they need to have it at the location or channel the customer wants to get it. Inventory in the wrong place drives increased markdowns at locations with excess inventory and lost sales at locations with no inventory.
Many retailers’ end-to-end inventory management processes are often manual and time-consuming. With an AI-enabled solution, retailers can quickly move inventory between locations and channels to meet customer demands – seamlessly. And when retailers meet customer demand with strategically located inventory, customers come back, and profits go up. Optimized history, data correction services, and a location planning strategy are three simple, automated processes that help retailers analyze and correct data for historical anomalies.
Conclusion
What if inventory management processes were adaptive and could learn over time? CCH® Tagetik Supply Chain Planning uses AI in inventory management to run a “best-fit” analysis on forecast records at the beginning of each forecast cycle. The AI engine automatically uses the most appropriate forecasting method for each record. The best-fit analysis looks at the most recent demand data available and ensures the most accurate forecast for every item as it progresses through its lifecycle. Forecasts that need attention are flagged for review by a planner and shared with relevant colleagues.
Our AI-based, cloud-native platform predicts the unexpected better, giving your forecasts the certainty needed to make your supply chain resilient.
