2002 New York Yankees: Team Salary $126 million; 103 Wins 59 Losses; Division Winner
2002 Oakland Athletics: Team Salary $ 40 million; 103 Wins, 59 Losses; Division Winner
2012 New York Yankees: Team Salary $198 million; 94 Wins 68 Losses; Division Winner
2012 Oakland Athletics: Team Salary $ 55 million; 93 Wins, 69 Losses; Division Winner
You have most likely seen or heard of the story behind the movie “Moneyball”. In 2002 the Oakland Athletics had a very limited budget to “carry” their team roster through the season, and they still had to compete with topnotch teams in their league. Some of the teams they had to compete with, like the Yankees, spent up to four times (4x) as much as they did on their “inventory” of ballplayers (i.e. “products” in baseball). The A’s turned away from traditional thinking on how to allocate their budget to field a team, which meant relying on the gut feel of managers and buying the highest priced players. Instead, they started to rely on “Sabermetrics”, the use of statistical analysis to determine the most cost-efficient baseball players based on the measure of in-game activity/history. Hence, based on mathematical models, the A’s figured out how to best optimize the team at every position on the field. The result was that Oakland won 103 games in 2002, made it to the playoffs, and tied with the Yankees for most wins that season. Again, Yankees spent more than three times (3x) of what Oakland paid for its team, in the same year.
Coming back to the supply chain world, in the same manner, that Sabermetrics can help optimize the baseball players on a team, Multi-Echelon Inventory Optimization (MEIO) can optimize your inventory that is deployed throughout your supply chain, in order to achieve target customer service levels, and maximize profit. There are obvious parallels in taking the Moneyball philosophy to the optimization of inventories and improving the efficiency of the supply chain. Instead of the General Manager in baseball using statistics to determine the best players to have on a baseball team, the Supply Chain Manager can use statistics and mathematical models in an MEIO system in order come up with the highest profitable scenarios and maximizing on-time delivery performance. By examining these scenarios, the Supply Chain Manager can decide how to right-size the inventory levels at different locations, and achieve targeted customer service levels, at the highest profit margins.
Of course, instead of baseball metrics (e.g. RBI’s, on base%, ERA, salary), there are statistical supply chain metrics (e.g. Demand variability, supply variability, BOM, Inventory value, etc.) that can be used to objectively calculate the value of each unit of inventory that you plan to place at a given “position” in your supply chain (e.g. Raw Materials, WIP, Finished Goods, etc.). This would make it possible to optimize inventory deployment for meeting certain customer service objectives and squeeze the most profit out of your supply chain, while not exceeding the budget allocated for working capital.
The Oakland A’s are back in the playoffs again this year, with a budget that is one-third of the Bronx Bombers. Not surprisingly, the use of statistics (i.e. the right system) is helping them get the most out of their small budget. Adexa has the equivalent of Moneyball’s Sabermetrics for your Supply Chain, it’s called the Inventory Planning Optimizer to ensure each dollar of inventory is spent in the best possible way.