Pharmaceutical industry capacity simulation model
This organisation produced over pharmaceutical products has a need to improve the level of tank utilisation to ensure that all facility resources are maximised as part of the capacity planning process.
We completed a recent supply chain assessment "Pharmaceutical industry capacity simulation model" for the entire operation to better understand how inventory, information and data moved throughout the business and its influence on the decision making process. This was achieved by constructing a simulation model of the operation, starting with forecast demand and watching it flow through the ERP system to trigger workorder and item requirements for capacity planning.
This approach allowed all stakeholders to visualise the entire manufacturing facility, observe the flow of inventory to guage which processes were delayed, which were performing as expected and bottlenecks caused by delays in information flow. The entire simulation was extremely interactive allowing the users to conduct a number of what of scenarios to better understand the impact on vessel capacity.
- Current levels of tank utilisation were very poor, some tanks not even used for days.
- Work order management and release processes were not standard protocols
- Regulatory constraints around document and process compliance created delays for planning.
- Resources were not utilised efficiently throughout the production and warehouse operation
- The cost of goods for manufactured items was not clear and accurate for financial planning
- New As-Is workflow model was created in a dynamic simulation model
- New processes and procedures implemented for work order management to production.
- 3D simulation model provided ability to run scenarios and alter product cycles to tanks
- Tank utilisation levels increased by 30% with volume spread more evenly to all tanks.
- Resources were now focused on supporting required materials to support forecasts.
Our project: Pharmaceutical industry capacity simulation model delivered a quality outcome for our client as its being used to improve other aspects of their operations that were previously extremely difficult to problem solve due to the challenges of using traditional data analysis models that were complex in their development, construction, interpretation and solution implementation.