Multi-echelon inventory optimization looks at the whole picture
As more companies begin to sell their products through multiple channels—retail, catalogue, and online, through intermediaries or direct-to-consumer—they need to be able to monitor, balance, and correctly allocate inventories when and where needed. By applying inventory optimization software companies can look across multiple levels, or echelons, of a supply chain to detect changing conditions early on and suggest responses to them, thereby enabling faster decisions that reduce risk by anticipating rather than simply reacting.
Multi-echelon inventory optimization represents a dramatic advance over traditional inventory decision making. Inventory planning typically has not been centralized, and service-level commitments have been managed and measured at the warehouse and DC level, based on individual or location-based metrics. As a result, inventory was optimized with no view into total supply chain stock levels, resulting in lower inventory turns, inconsistent service levels, expediting of products, and a lack of understanding of supply chain-wide inventory drivers.
By considering locations in almost every echelon, these inventory optimization tools have the numbers-crunching and analytics capabilities to handle this complexity. They minimize the chance of making decisions that benefit one channel to the detriment of another, or to the entire supply chain. In addition, cross-channel, multi-echelon visibility—essentially, the ability to see supply chain activities across the entire supply chain—provides early warning of changing patterns of demand and supply. Companies can sense change sooner, and react more adeptly to manage it.
As an example, a US-based household products manufacturer faced the daunting task of trying to optimize inventory across an extended, multi-echelon supply chain network. Its goal: avoid stock-outs while preventing excessive inventory build-up. Using information such as lead-time, customer service and forecast error history, future forecast, and supply variability from the manufacturer’s enterprise resource planning (ERP) system the manufacturer used optimization technology to compute optimal inventory requirements.
By considering the end-to-end network holistically, the optimization system may determine that an increase in the inventory of raw materials allows the reduction of the finished goods inventory in the distribution network. In fact, the additional raw material inventory better buffers against long or uncertain supply lead times and thus reduces the need for finished good inventory in the shorter and more predictable (relatively speaking) distribution network.
Clearly, these considerations can only be done by a system with a global supply chain view. Supplier lead times, replenishment frequencies and transit times have a strong influence, not only on one warehouse or one plant, but on the entire supply chain. In many cases service times can be determined (or tuned) to reduce the total working capital for the company. The right amount of inventory coverage maintained by the trading partners can also be suggested to improve inventory turns in the system.
A different customer uses inventory optimization to determine the right trade-off between transportation cost and inventory cost. A central distribution centre reduces inventory levels through risk-pooling and more accurate forecasts. Furthermore, the larger volume at the central locations means more frequent shipments from suppliers, which results in lower inventory levels. But this strategy incurs additional transportation costs between central and store-facing DCs, as well as extra handling costs, as products have to flow between DCs and then on to the stores. In this case a centralized strategy is more cost effective than a decentralized one where the goods flow directly to the store-facing DCs. Inventory optimization helps determining the optimal flow of each product across the different nodes of the supply chain as well as its mode of transportation.
Beyond optimization, dynamic what-if scenario modeling can be used to address key questions relating to inventory at almost every level of the supply chain—the supply base, manufacturing, packaging and distribution and customers. This type of analysis can challenge conventional wisdom to yield improved results. In most companies a strong sales and operations planning (S&OP) process is critical to correctly balance supply and demand, and deliver the right products in the right quantities. In particular, companies whose revenue and profits are highly dependent on their performance during prime sales and shipping seasons (such as the Christmas holidays) must pay special care to the analysis of alternative demand scenarios for those periods. A consumer product manufacturer was able to enhance its S&OP process by combining capacity planning with inventory optimization across several demand scenarios. Since this company commercializes many products that are only sold during the holiday season, any excess of inventory is sold under cost after the end of the season. Inventory optimization helped them optimize their financial results by reducing both stock-outs and after-season sales.
Only by optimizing inventory across the entire supply chain can companies truly give their supply chain the flexibility to meet unexpected high demand, even with a minimal amount of inventory invested in the system. Such planning is the key to avoiding customer disappointments and making sure customers are getting what they want, when and where they want it even during peak busy seasons.
Aberdeen Group, in its December 2011 report, State of Cross-Channel Retail Supply Chain Execution: Reduction in Inventory Holding Costs and Out-of-Stock, noting over half of retailers do not have good inventory visibility into their supply chain and 30 percent of retailers reported out-of-stock rates higher than seven percent, resulting in lost sales or unhappy customers.
In its IBM benchmark report, the company reported that in the US Cyber Monday 2012 online sales increased over 30 percent from figures recorded in 2011. The survey incorporates real-times sales data from over 500 US retailers into its findings.
Ronan O’Donovan is IBM supply chain applications product manager.