(385g) Multi-Site Specialty Chemicals Enterprise Decision Support through Simulation-Optimization

In today's competitive business environment, the ability of companies to reach out globally to different markets opens up vast business opportunities to be seized. The shift from a single-site manufacturing facility to a multi-site enterprise enables the flexibility of producing many products, focus on specialization activities, be close to low raw material cost sources, as well as get in close proximity to its targeted market. The supply chain (SC) of such a multi-site enterprise spans across continents, involves numerous entities with different interests, and has to contend with various uncertainties. The use of SC simulation models, which could capture the behavior of these entities, their interactions, the resulting dynamics, and the various uncertainties, when coupled with optimization techniques allows the evaluation of the set of decision-making parameters that would best bring out the performance of the SC in terms of profit margin and customer satisfaction. In this paper, we present the simulation-optimization based decision support for a multi-site specialty chemicals enterprise.

A typical multi-site specialty chemicals SC comprises raw material suppliers, 3rd party logistics (3PL) providers, shippers, the manufacturing plants, and numerous customers. Each plant has its own functional departments performing the different SC functions, i.e. scheduling, operations, packaging, storage, procurement, and logistics. The company would also have a sales department (typically centralized) which interacts with customers. Materials flow from suppliers to the plants and from the plants to customers.

Typically, three different cycles of activities constitute the SC operation: enterprise-level coordination, plant operation, and inventory management. Under the enterprise-level coordination cycle, order information flows from customers to the global sales department. The sales department then communicates with the plants to decide which plant is best suited to take on the job order based on the assignment policy adopted by the sales department. During plant operation, once an order has been received, the scheduler will then insert the assigned job to its production schedule. Checking of raw materials' availability with storage is done before releasing a job for production to ensure that it can be manufactured without running out of raw materials. The manufactured product is then sent for packaging and subsequently sent out to the customers through logistics. The next job in the schedule follows and the cycle is repeated. Lastly, the inventory management at each facility aims to ensure raw material availability for processing jobs.

A simulation model of such a multi-site SC has been developed in Matlab/Simulink. Various studies have been performed to demonstrate that the performance of this supply chain (in terms of profit margin and customer satisfaction index) is heavily dependent on the individual actions of the various entities. Therefore an optimization of the integrated system is necessary. The solution space of optimization problems related to a multi-site enterprise is usually large, and an efficient search of different parts of the solution space can be achieved through a simulation-optimization scheme. The optimization module chosen for this work is an elitist genetic algorithm (GA).

Three case studies will be presented in this paper. In the first case study, the plant assignment strategy ? which order is to be produced by each plant ? is studied. In the second case study, the trade-off between storage and production capacities is evaluated. In the last case study, a study on optimal procurement policy parameters will be discussed. These case studies illustrate how the simulation-optimization scheme provides decision-making support in numerous supply chain problems.