Using Predictive Simulation to De-Risk Decision Making in Supply Chain Operations

Bryan Jackisch is the newest addition to Lanner’s consulting team, focusing on the LNG industry. His previous experience was in Oil & Gas, Supply Chain and Aviation. In this blog he shares an example of how he’s used simulation to analyze risk to support better, more confident decision making.

I was fortunate to lead a simulation project at an Oil and Gas company that helped management understand the risk behind a proposed change to their multi-million dollar supply chain operation. The management responsible for the supply planning of a petroleum product had long suspected that there was unneeded extra capacity at one of their European terminals. The tanks cost hundreds of thousands of dollars a year to rent, but no analysis could convince management that removing the tank wouldn't result in missed orders to customers.

To investigate the question of whether a tank could be removed, I tried multiple approaches: Optimization, spreadsheet analysis, and simulation. The simulation approach ended up being the most practical and convincing analysis to answer the question. The model was fairly simple: a historic customer demand profile pulled the product from the tank; when the tanks got low a reorder was placed, then the replenishment order was shipped from the refinery. A full year was then simulated and different scenarios were tested.

First, the model was run in its current state with six tanks at the terminal, and the model showed that customer on-time fulfillment was nearly perfect. Next, one tank was removed, reducing the inventory capacity at the terminal. Once again, the model showed that the fulfillment was nearly perfect, implying that the tank could be removed with no significant impact to customer deliveries. To further support this finding, the model was re-run with customer demand increased by increments of 10%. The output showed the reduced tankage scenario would have no problem handling up to a 50% increase in demand with the tank removed, a situation that was highly unlikely.

The findings were presented to senior management and they were convinced that the tank could safely be removed. From their perspective, they didn't want to sign off on any change that would negatively impact customer satisfaction. The simulation model assured them that the change could be made without long-term, unintended consequences. Within the quarter the tank was returned to the terminal for a savings of over $300k annually and the supply chain continued to run as anticipated.

Predictive simulation helps both analysts and decision-makers understand their business processes, data and how they affect one another. In my new role with Lanner I look forward to helping companies uncover new insights about their processes to help them achieve better, more confident decision making. 

Contact us today to discuss your challenges and opportunities, and how we can help you achieve better, more confident decision-making.