An effective supply chain is one with right coordination of logistics, production scheduling and product distribution. In pull driven multi-echelon supply chains, distribution plays the dominant part in minimizing the supply chain cost (inventory & transportation). Supply chain is based on the intricate balance between information (demand, order) exchange to the upstream nodes headed towards the manufacturing system and material (products) exchange to the downstream nodes towards customers. Product flow between the nodes depends on the order quantity (information) placed by the downstream node which in turn depends on the procurement rule practiced by the downstream nodes.

Minimizing the supply chain cost (inventory) in the multi-echelon supply chain network necessitates superior control law (procurement rule) to achieve minimum excess inventory and back order. Procurement rules that are in vogue have their advantages and disadvantages depending on the type of demand pattern, the competitive environment faced by the business, type of product (life time, unit price & establishment) and business strategy of the supply chain. Demand trend types are deterministic, stochastic or seasonal in nature. Products types depend on their life time, unit price and its status (new product or mature product) in the market. Some operational strategies pursued by the business are: (i) no back order at the cost of excess inventory. (ii) No excess inventory at the cost of back order. (iii) Minimum of both excess inventory and back order with different weighted importance depending on its product establishment in the market. Bull whip effect is an additional constraint to account in the performance assessment of multi-echelon network to avoid demand amplifications/variations.

Supply chains have different combinations of demand pattern, product type and business objectives. A suitable control law is necessary to operate the supply chain in a successful manner. Selecting, retuning and restructuring the existing control law or developing the new order policy for the existing combination of demand, product type & objectives is an important approach in benchmarking and troubleshooting of supply chain systems. This work will focus on troubleshooting the existing network in order to enhance its performance through retuning (parameter adjustment) and restructuring. Variance analysis and time series analysis will be employed in this task. Many simulation examples will be provided to illustrate the theoretical concepts.