Abstract
The complexity of providing timely and cost-effective distribution of finished goods from industrial facilities to customers makes effective operational coordination difficult, yet effectiveness is crucial for maintaining customer service levels and sustaining a business. Logistics planning becomes increasingly complex with growing numbers of customers, varied geographical locations, the uncertainty of future orders, and sometimes extreme competitive pressure to reduce inventory costs. Linear optimization methods become cumbersome or intractable due to the large number of variables and nonlinear dependencies involved. Here, we develop a complex systems approach to optimizing logistics networks based upon dimensional reduction methods and apply our approach to a case study of a manufacturing company. In order to characterize the complexity in customer behavior, we define a “customer space” in which individual customer behavior is described by only the two most relevant dimensions: the distance to production facilities over current transportation routes and the customer’s demand frequency. These dimensions provide essential insight into the domain of effective strategies for customers. We then identify the optimal delivery strategy for each customer by constructing a detailed model of costs of transportation and temporary storage in a set of specified external warehouses. In addition, using customer logistics and thek-means algorithm, we propose additional warehouse locations. For the case study, our method forecasts 10.5% savings on yearly transportation costs and an additional 4.6% savings with three new warehouses.
Highlights
Logistics is widely recognized as the most complex among business processes. e challenge of coordinating with multiple suppliers for raw materials and partially finished goods, and the challenge of delivering next-stage finished goods to customers, all in the correct amounts in a timely fashion and in coordination with production processes, despite uncertainty due to independent decision-making of customers, is daunting. ese coordination processes are challenging because of the need to optimize costs and maximize customer satisfaction
E issue of complexity in supply chains has been explored from a number of perspectives [7,8,9,10,11,12]. e literature on these problems, generally divided into the locationrouting problem (LRP) [13,14,15] and the warehouse location problem (WLP) [16,17,18], provides a range of proposed solutions
We tested our model on a dataset from a medium-sized manufacturing company with more than fifteen years of customer orders. e company and its customers are located primarily in the US. e logistics network has about 15 production facilities and more than 30 external warehouses and serves more than 2000 customers. e majority of the customers have not ordered more than 10 times, and due to the lack of data on these customers’ ordering patterns, the direct shipment method is always chosen by the company. erefore, we excluded customers with 10 or fewer orders from our analysis
Summary
Logistics is widely recognized as the most complex among business processes. e challenge of coordinating with multiple suppliers for raw materials and partially finished goods, and the challenge of delivering next-stage finished goods to customers, all in the correct amounts in a timely fashion and in coordination with production processes, despite uncertainty due to independent decision-making of customers, is daunting. ese coordination processes are challenging because of the need to optimize costs and maximize customer satisfaction. While mathematical models should be helpful to explore the space of possible strategies and propose optimal solutions when operations become complex [5], solving such models becomes more difficult as the number of strategies and considered variables increases [6]. E literature on these problems, generally divided into the locationrouting problem (LRP) [13,14,15] and the warehouse location problem (WLP) [16,17,18], provides a range of proposed solutions. For both the LRP and the WLP, there is an objective function to be minimized. Each approach involves imposing constraints on the objective function to make the optimization more reliable, which makes the solution harder and more time-consuming for large complex systems. erefore, the LRP and WLP together remain open problems in the field of supply chain management, requiring further improvements in analytical methods
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