GlaxoSmithKline, also known as GSK, is one of the largest pharmaceutical companies in the world. The British multinational company has a complex supply chain that consists of numerous suppliers, factories, warehouses, and customers around the world. GSK offers a large range of pharmaceuticals, vaccines, and consumer health care products. Their demand fluctuations are a major challenge for production planning and logistics. Production line design is an important factor in GSK’s success.
GSK’s success comes, in part, from their dedication to innovation and continuous improvement. They strive to improve quality and reduce costs by applying economies of scale and optimising resources. GSK’s dedication to innovation and improvement is exemplified in the redesign of their facility in Parma, Italy, and the capabilities developed during the project. They achieved a 20% reduction in capital expenditure.
How to better plan a production line
Product forecasting and demand fluctuation are common challenges in business. For GSK, meeting these challenges, considering their broad and complex product mix, meant addressing issues with bottlenecks and scheduling. From previous experience with simulation modelling, GSK knew adopting an approach that captured the dynamics of the system would help them find efficiencies. Furthermore, for later stages in the project, they planned to further enhance results using deep reinforcement learning. (Learn how simulation complements AI)
GSK, Decision Lab, and SimulAi joined forces on the first stage of this project. This stage included an AnyLogic model that was developed to inform the design of the production line for GSK’s Parma Factory, Italy. The model was created so that after the design stage it could be used for analysing what-if scenarios.
The logic of the model was developed considering specific production rules, such as deadlines and product priorities. Moreover, resources available, such as operators or machines, and parameters, such as changeover and cleaning times, were taken into consideration. The simulation gave GSK a clear view of key operational interests:
– Process bottlenecks
– Production scheduling
– Demand coverage
For the what-if scenarios, the team explored parameters such as the number of machines, shifts, overtime, and product demand, to understand how processes reacted under stress. They wanted data on four key points:
– Optimal product mix
– Optimal production scheduling
– Maximum demand peaks
– Time needed to adapt to changes in demand
The opportunity to change parameter values helped GSK develop a better understanding of several areas:
– Process bottlenecks
– Resource efficiency
– Product mix optimisation
– Production scheduling optimisation
Production line testing
GSK sought to develop a model for configuring easily and flexibly the attributes of model entities that represent factory assets. The ability to configure the model in this way allows the definition of any number of attributes for any number of assets. To achieve this level of flexibility within a simulation and combine it with a simple user interface required careful development.
For GSK, the challenge was developing a model that captured all the required complexity while being fast enough and flexible enough to answer the business questions, allowing a wide exploration and optimisation of the problem space.
The resulting operational model enabled GSK to test the future production line and investigate its performance boundaries based on simulated constraints. Importantly, the model helped understand how the performance boundaries and constraints affect the factory’s ability to fulfil demand as well as their impact on production capacity and operating costs.
GSK’s engineers and management answered key questions using manufacturing simulation: how many machines they should buy, what technology the machines should use, and what their operational regime should be.
Learn more in this white paper about Material Handling Simulation.
Reduced capital investment costs
In their development of the model, GSK did not want to develop a black box with no visibility of simulation logic and for which outputs could not easily be interpreted. Consequently, to better support a transparent decision-making process, they created a user-friendly interface that shows products progressing through production lines. For each stage of the process, it is possible to inspect the embedded business logic.
As well as informing the design phases of a new facility, the simulation model provides continued value to a project. In later equipment selection phases and beyond, targeted scenario analysis can inform procurement and facility design decisions. The model also extends to include people and material flows, helping inform operational layout choices and more.
For GSK, the main benefits of the simulation model are:
– Increased confidence in the amount of equipment required for the new production line
– 20% reduction in overall capital investment costs
– Rapid business scenario analysis
– Faster decision making – stakeholders understand, and trust, analysis based on the modelling – smoothing stage-gate approval of concepts and allowing faster progression through project phases.
Using AnyLogic for production line design helped find the 20% capital investment saving because simulation modelling with the platform can provide far greater insight and capability than Excel and similar alternatives while maintaining a favourable cost of entry. Key for GSK, the development platform, allows for standalone model deployment and has a broad and strong ecosystem of support.
Read the AnyLogic GSK case study.