Case study: Optimizing production network in the paper industry

A leading manufacturer of tissue papers commissioned COSMO CONSULT to develop a solution for simultaneously optimizing production, warehousing and transportation planning within a Europe-wide production network. This has significantly reduced the total costs in the supply chain. In addition, the company is now able to react quickly and flexibly to sudden market changes.

• Reducing the total cost by simultaneously optimizing the entire supply chain
• Providing simple simulation of scenarios and parameters (e.g. new machine, production relocation
• Comparing different variants and planning procedures
• Integrating external data sources such as Google Maps

A leading manufacturer of tissue papers was manually planning the allocation of resources for various products. Due to the many restrictions and dependencies, this required a lot of experience and intuition. To reduce the cost and become less dependent on planning experts, the company commissioned COSMO CONSULT to develop a solution that assists decision-makers and automatically generates production and transportation plans. At the same time, the solution should easily calculate alternative scenarios, such as investment decisions.

After a joint workshop, the project team first developed a software architecture that largely consists of modules for data processing and optimization. It also consists of a graphical user interface which ensures that the optimization and data management functions can easily and efficiently be put to use. To fully map the optimization problem, a mathematical model was created, on the basis of which a C ++ solution algorithm was developed that uses the generic solution algorithms of IBM CPLEX solver for subproblems.

Because of the complexity of the task, the team chose an agile project methodology with regular feedback and an iterative development process. Thus, it was possible to achieve a high level of satisfaction right from the start. During the optimization process, the typical long operating time of large mixed integer problems (MIPs) posed a particular challenge. By decomposing the problem and separating the constraints, the project team managed to reduce the operating time by 5-10 minutes per optimization run.

The simultaneous optimization of all relevant types of costs has significantly reduced the total costs in the supply chain. The experience and intuition of the employees were thus transformed into digital knowledge. Today, the company is far less dependent on the knowledge of individual employees. Faster operating times and more flexible parameterization also ensured shorter planning cycles and prompt reactions to changes.