Contrary to what the name might suggest, discrete event simulation (DES) isn't just for "widget" manufacturing. Indeed, it has particular value in the chemical industry. Yet, chemical makers have been slow to adopt this methodology. So, in this article, we'll look at what DES can do.
The chemical industry widely uses process simulation and inventory optimization tools, and clearly understands that modeling has a very important role in decision-making. However, there's a missing link between the focused analysis performed with these tools and the need for a clear comprehensive view of the entire supply chain and how factors like continuous operations, discrete processes, inventory planning and logistics affect specific key performance metrics. DES supplies this missing link by providing a method to quantify these interactions and their effect on the bottom line. Furthermore, it enables decision-makers to assess "what if" scenarios to explore the different optimal configurations that can be achieved or the possibilities of yet-to-be built plants and processes.
The Value of Simulation
Simulation consistently ranks as the most useful and powerful of mathematical-modeling approaches. This stems in part from its ability to handle high fidelity models of very complicated systems. Recent advances in computing power and the ease of use of "commercial-off-the-shelf" simulation software bolster its appeal. In addition, the flexibility provided by hybrid continuous/discrete simulation software has paved the way to an increase in viable applications, including for the chemical industry.
Most chemical processes are continuous, but discrete operations also are common at plants. Enabling processes such as procurement and logistics are discrete, as are packaging, raw material arrival and asset maintenance. So when modeling the chemical industry from end to end, it's important to include elements of both continuous and discrete change.
A chemical maker's supply chain must be responsive, reliable and flexible. Production processes often are campaigned to ensure nonstop operation. Premature halts — due to lack of raw materials or other supply chain failures — often can incur high costs. A missed shipment of energy or feedstock materials could cause a total plant shutdown, resulting in a loss of millions of dollars. To mitigate this risk, chemical companies often carry large inventories of raw and intermediate materials and finished goods. Compounding this, customers are implementing lean strategies that, in effect, push inventory demands back onto chemical makers.
Supply chains are complex systems to model due to their uncertain and highly variable nature. The integration and interrelation of suppliers, suppliers' suppliers, customers, etc., mean that something taking place at one company could greatly affect supply chain activities. Another complication is that supply chains are dynamic. Changes such as an enterprise leaving or another joining the chain are common. Therefore, decision-makers need a methodology that allows for timely and efficient updating to reflect such changes. In addition, supply chains span numerous physical locations — and necessary information must come from all these sites.
With decision support tools based on mathematical models, spreadsheets or process map methodologies, decision-makers must contend with lots of assumptions that hardly ever hold true. Using a number such as an average for systems with variation isn't the solution. That approach fosters significant inefficiencies like production backlogs and unbalanced capacities — and also reduces revenues due to lost sales (where production couldn't meet demand) and higher inventory and other costs. The bottom line is that not taking variability into account costs money.
The power of supply chain simulation lies in its ability to provide the essential level of realism and utility required for accurate modeling. That's why companies in many industries have adopted DES as the methodology of choice when tackling supply chain decision-making.