Healthcare Company Identifies Strategies for Inventory Reduction while Maintaining High Service Levels
For this global healthcare company, getting its products to the right place at the right time is no small endeavor. The company’s distribution network comprises three different levels: production facilities, regional distribution centers, and customer-specific distribution centers. Inventory is tracked at all three levels and while levels; in-transit inventory is also tracked between locations.
The company’s legacy inventory optimization tool could not account for their multiple layers of distribution and only had a limited ability to handle different inventory management methods. LLamasoft was brought in to help the company develop a repeatable approach to optimizing safety and cycle stock levels and decreasing the amount of inventory on-hand considering multi-echelon interdependencies across the supply chain. However, they had one caveat: Given the life-saving nature of their products, they did not want to compromise service levels.
The LLamasoft team used Data Guru to take historical data and develop a three-month forecast that could be used for network optimization and inventory optimization in Supply Chain Guru to calculate baseline cycle stock, in-transit inventory, and safety stock levels. Then they used Supply Chain Guru’s integrated simulation engine to evaluate multiple variables including alternate stocking strategies, stocking levels, and replenishment frequencies. Although the company has no immediate plans to alter its distribution network, they also asked LLamasoft to run simulations to determine the impact of reducing the number of distribution centers from 40 to as few as eight.
The team also ran scenarios that included varying levels of service to determine the investment needed to reach beyond their targeted level of 98 percent. As expected, the data showed a hockey stick pattern, with higher service levels requiring disproportionately higher levels of investment, especially for items with a greater degree of demand variability (see image below).
The results of the optimization and simulation revealed several ways the company could lower overall inventory levels, with resulting decreases ranging from 10.5 to 17.9 percent. This gave the team the insight they needed to consider the various tradeoffs and select an approach that would allow them to significantly reduce inventory, while delivering services levels very close to the 98 percent targets even for products with a high degree of demand variability.