How are pharma companies coping in this volatile environment?
Conflicting objectives can cause issues, such as the need to maximize resource usage to keep production costs down, while simultaneously striving for a high customer service rate.
Multi-objective decision-making is complex and often difficult for the human mind to deal with. Without a systematic approach, humans tend to base their decisions on only one of the multiple objectives. Herbert A. Simon, the American political scientist who received the Nobel Prize for economics science in 1978, described this in his decision-making theory that explains bounded rationality and how people make decisions within certain limitations.
When humans leave out important objectives, they are likely to accidentally exclude the optimal solutions that the industry urgently needs in the current environment.
So, what are companies to do?
The focus appears to be simple to set: make more for less and optimize at all costs.
Successful planning and scheduling tend to be complex and include many different aspects. Numerous factors, including market knowledge and personal experience, affect the planner’s decisions. As the schedule grows larger, these decisions become more error-prone and difficult to comprehend. Guidelines may not even lead to a near-optimal solution.
In addition to the more complex situations, the planner also must assign straightforward tasks to the plan, which can be both tedious and time-consuming.
The industry is actively looking at solutions for solving this scheduling problem, without disrupting the manufacturing process. Today, most pharma companies’ schedules are still created and maintained manually by the planner, using less-than-ideal software solutions, often spreadsheet-based.
Problems are usually the predictable result.
Complicated spreadsheets are notoriously difficult to debug and maintain, so users tend to tread very carefully in order not to break something that is working well enough. Version control and lack of history often result in companies relying on a single master plan, without any obvious or easy ways to perform what-if-analysis, or algorithms to suggest improvements to the plans.
Hence the manufacturing plans are usually sub-optimal, difficult to maintain, and hinder necessary responses to changes in the environment.