Or at least is seems that way when you need to optimize locations and layout to drive customer satisfaction. Are you leaking money to service customers and markets?
Have you wondered how retail stores have what you need regardless of how obscure it may be. A Fortune 100 company learned a valuable lesson when it switched from a distribution model to a direct-to-retail supplier. The problem they were trying to solve through this change was extended lead time which lead to empty retail shelf space resulting in lost sales. The company’s customer base was loyal and always came back (they were the sort who even if they wanted a Pepsi would settle for a Coke). What they did to solve the problem was not unique or groundbreaking, however it did require discipline and data mining……
As a global manufacturer with distribution centered in the middle of the United States, the company developed a global “Pull System,” a grocery store model. By using demand data and product management, the company created inventory levels based on demand rounded up to the nearest packaging size for each retail store. Those units were inventoried at: the customer, the distribution and the individual plant locations. As inventory moved at the retail store, this triggered other locations to ship based upon lead time. This is a classic lean pull system which is much more customer focused and efficient. The data was created to provide a 98% level of delivery at each of the customers for all 600 US locations! Sounds easy…. That’s where the discipline comes into play. All the way through the supply chain, daily management had to be done to avoid air freight or other expediting costs. At the manufacturer, orders had to be created internally once inventory levels dropped to a certain level. Action had to be taken to fill those levels immediately which sometimes required overtime. At distribution centers, shipments had to move in a timely manner to assure proper signals were set to overseas manufacturers. The manufacturer’s import and receiving teams also had to run like clockwork in the correct timeframe. In addition, the data had to be managed to handle quarter-to-quarter shipping variation.
In the end, customers were able to buy what they wanted from a much more efficient process, resulting in improved customer loyalty and a 25% growth in sales!
