One of the benefits of my position in the industry is I get to speak with a lot of supply chain leaders. I hear about their challenges and see how they have created world-class, high-performance supply chains. A common theme is how supply chain teams face a daunting task every day—a task that, at its most fundamental, boils down to this: minimize your supply chain costs delivering great customer service. Now, that’s a balancing act worthy of Cirque du Soleil.
One of the key elements of this balancing act is to establish the right safety stock inventory policies. The optimal polices will minimize cost, maximize service levels, and take into account both demand and supply uncertainties. The complex nature of this can require weeks or months to solve using spreadsheets, producing inflexible and approximate rules-of-thumb that can’t stand up to real-world market dynamics and changing conditions.
The discipline of inventory optimization—especially the full-spectrum form called multi-echelon inventory optimization (MEIO)—provides proven tools for modeling and optimizing the placement and amounts of inventory held across a multi-stage supply chain. In just minutes, MEIO performs analytical passes that calculate the full impact of alternative inventory strategies on the performance of an entire supply chain network. These analyses can be part of the ongoing supply chain management process, so that both strategic and tactical decisions are made with confidence. MEIO frees up working capital trapped in excess inventory, ensures service level goals are met at minimum cost, and enables collaboration by giving all supply chain stakeholders one accurate view of the network.
Reports from the MEIO front lines
I have had the opportunity to participate in online webinars with several successful supply chain executives. I would like to share some of what I have learned from recent events with HAVI Global Solutions and Stanley Black & Decker.
Service level expectations
HAVI Global Solutions is a provider of packaging solutions, analytics and supply chain services for many industries, with a high concentration of customers in the demanding food service industry including Campbell’s, ConAgra Foods, McDonalds, and RalCorp just to name a few.
The food service industry has very high service level expectations. When a customer wants a product it simply must be available. If not, food service companies run the risk of losing the sale as well as the customer. The key for HAVI Global Solutions is to help customers maintain high service levels and accomplish this cost efficiently.
To strike this balance, the supply chain team identified a key barrier—visibility. They spent considerable time to establish data connectivity with its vast number of retail locations, distribution centers and partners to gain access into the entire supply chain. The team confirmed each supply chain kept safety stock based on its own needs and plan. Each node acted independently of the network.
The company deployed Logility Voyager Inventory Optimization to take this information and develop a safety stock plan that helped identify when and where inventory should be placed. The impact of this exceeded the HAVI team’s own expectations. Initially, they identified an inventory reduction opportunity for finished goods between 10 and 20 percent. In deployment, the divisions realized between a 20 and 30 percent reduction, a decrease of 5 – 13 days of supply (DOS) inventory and a 2 percent improvement in truckload utilization.
With a better understanding of their network, the demand, and the priorities of its customers and partners, HAVI Global Solutions is able to deliver the exceptional service required without driving up costs.
Inventory locations drive service levels
When The Stanley Works merged with The Black & Decker Corporation in March 2010 Black & Decker was in the midst of a supply chain initiative to improve fill rates, reduce its inventory investment and minimize lead times. This initiative focused on multi-echelon inventory optimization (MEIO) with a single assembly line.