![]() ![]() The simulation model received the data from a cloud database. Usually, in the normal process, RPM eco would use it week by week to get the best predictions for the customers. They used mostly statecharts for modeling the processes. ![]() To address these challenges, SimWell developers built an agent-based simulation model to manage the prioritization of routes. Previously, RPM eco had static routes that were problematic due to seasonality. Simulation of routes for optimizing the travel distance between customers They could predict the available weight for each customer at a certain point in time. They were able to anticipate if the client’s consumption behavior required them to change from one method to another.Īfter SimWell implemented these tools, RPM eco was able to increase the service level and reduce the margin of error between the predicted and pickup weights. They combined the predictive tool with a supervised learning model to manage client variability. The engineers used two methods: ARIMA (autoregressive integrated moving average) and Croston, mostly used for intermittent demand. Predictive algorithms were based on historical data and seasonality. SimWell engineers created a predictive tool to anticipate the weight available in each of the client’s facilities. ![]() This end-to-end solution started with prediction. Prediction of the available weight for each customer In the picture below, OSCAL is illustrated. It consisted of prediction, simulation, and a mobile app. For this purpose, SimWell developed their own end-to-end solution called OSCAL. SimWell helped RPM eco optimize a reverse logistics supply chain. The business purpose was to maximize the material weight collected by trucks. RPM eco also wanted to build dynamic routes according to the needs of their customers and be able to easily modify these routes and keep track of them. RPM eco planned to get to their customers right before they called to maximize the weight picked up and have a high service level for their customers. Also, they intended to increase the drivers’ willingness to operate the routes.Īs for managers, it was necessary to increase customer service levels and employee satisfaction. They would like to have more kilograms per kilometer done, much fewer empty pickups, and an easy-to-use mobile app.Ĭoordinators wanted more visibility on the ongoing routes and their drivers. RPM eco had three main stakeholders: drivers, coordinators, and managers.ĭrivers were paid by the amount of weight they brought back to the collection center. Only two employees managed all the service calls and operations, such as building these routes. This could be an issue if there was seasonality in consumption. This meant that they would be serviced once a week, once every two weeks, or once a month.Īnother problem was that all of those customers were on a static route. One of the main issues was that all those clients were on a fixed time interval. RPM eco drivers picked up the recyclables from their customers and brought them back to the collection center. They help trailblazing business leaders make confident decisions with the use of simulation modeling. SimWell is a consulting company with a global team of engineers and simulation consultants dedicated to simulation, optimization, and digital twins. The company has thousands of customers all across Canada. 32 // src/Command/CreateUserCommand.RPM eco offers an integrated management system for recycling of contaminated plastic hydrocarbon containers, as well as pesticide and fertilizer containers.
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