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As the business world goes global, many multinational manufacturers are looking to tap into diverse local markets by switching from single-site to multi-site operations. However, the complexities of managing such continent-spanning organizations can cause ‘hiccups’ in the supply chain and affect a company’s bottom line.
Rajagopalan Srinivasan and Arief Adhitya at the A*STAR Institute of Chemical and Engineering Sciences have now developed a computer model that simulates the complex dynamics of a multi-site supply chain. The model could help companies predict how their business decisions may affect profitability. “Traditional supply-chain models were designed to optimize the management of single components,” explains Srinivasan, who led the research. “Our model looks at the integrated network in its entirety and takes into account the interactions between multiple components.”
In order to emulate real-world conditions, the researchers incorporated a wide range of parameters—from order acceptance and assignment to raw material procurement—into their model. They could then evaluate the model by assessing what impacts different business policies might have on the overall performance of a supply chain.
The researchers were able to help a lubricant additive company, which turns bulk chemicals into specially tailored products for improving the performance of lubricants, identify problems within their scheduling department. They used the model to predict a number of different scenarios, from different ways to schedule jobs between different plants to the impacts of a disruption in production.
The model showed that when a company uses a cheap but unreliable logistics firm to deliver its raw materials, it would need to keep a larger inventory of raw materials. However, when a company uses a more reliable logistics firm to deliver its raw materials, it would increase plant productivity, customer satisfaction and overall profitability. In other words, despite a higher cost, a company should aim for higher on-time performance and avoid higher inventory levels. “The model produced some very surprising results,” says Srinivasan. “Though the model, we learned something that had not been evident to large enterprises, nor considered much in the research literature.”
Srinivasan believes that there is still scope to expand the utility of the model by incorporating extra parameters. “We plan to incorporate sustainability into our model so that when companies want to go green, they can make a holistic decision based on their entire supply chain, rather than just green products.”
The A*STAR-affiliated researchers contributing to this research are from the Institute of Chemical and Engineering Sciences.
