Many of us are currently working from home and getting adjusted to this new way of working.
If you’re an employee working on the shop floor of a manufacturing facility, however, working from home is not an option.
Among the many hard decisions manufacturing leaders have had to make during the coronavirus pandemic is determining the right measures to minimize pandemic health risks for employees, while ensuring continuity of production processes. After all, most essential items we depend on are manufactured by people directing machines and technology.
How can companies reduce the risk of employees being exposed to each other during their manufacturing shifts?How can leaders adjust plans if one or more shift teams are on quarantine? What safety protocols should be taken if one employee has a confirmed COVID-19 infection.
Optimization is a technique that can help find the right answer to these questions and simulate different scenarios. Consider these two possible scenarios: Is it safer and more efficient to reduce the size of shift teams, or to move from a three shift to a two shift model? This is a great question for optimization.
The nature of optimization
Optimization is a form of mathematical analysis that aims to find the best solution from all the possible options by looking across a range of variables.
Optimization models typically require a clear objective, like maximizing profits, minimizing costs or minimizing contact between employees on the shop floor. These models also require a range of variables, such as production levels and resource allocations, and some constraints, for example, factory capacity or customer demand.
The optimization algorithm then runs through the decision variables and selects the best possible level for each to meet the objective while remaining within the defined constraints.
This process helps organizations to improve business decisions and in our current situation could help save lives. In the case of our particular example, the improved decision is to allocate resources most effectively while minimizing risk for infection.
Some traditional use cases for optimization have included successful decisions to:
- Allocate production to machines with different capacities, start-up costs and operating costs, so that the organization can meet production targets at minimum cost and maximum efficiency.
- Manage shipping from the factory to the warehouse, or the warehouse to final customers in a way that minimizes shipping costs but still meets demand.
- Schedule employee shifts or activities and manage the workforce in a way that will both meet business requirements like service levels and throughput time.
Workforce management optimization to maximize employee safety
It is likely that workforce optimization will become more and more important during and after the COVID-19 pandemic.
Let’s take a closer look at how optimization could help to increase safety at manufacturing plants.
In workforce optimization the goal can be to take into account both production efficiency and employee safety constraints. Variables could include demand, capacity (including worker distancing constraints) and variables such as lead times.
Here is an example for a problem statement in shop floor workforce optimization.
Given a sales forecast, initial stock levels per product and a capacity limit per factory/product group, build a production plan that takes the following into account:
- Ensure a distance of 6 feet between each employee.
- Avoid stock outs while minimizing contact between employees and product.
- Avoid overtime work.
- Respect minimum order quantities.
- Maximum one ramp up / ramp down per week.
- Maximize machine productive time.
- Respect skills per employee and consider possibility of training for additional skills to increase flexibility.
- Minimize change in production from week to week.
- Respect cleaning or setup times between products.
How to operationalize the optimization model
The problem statement supports optimizing workforce safety while respecting traditional production planning and scheduling optimization at the same time.
What’s most important is that by using this kind of optimization model manufacturers are able to meet their service levels while maximizing both employee safety and efficiency in production.
In a next step that problem statement needs to be translated into a mathematical optimization model.
Once the model is developed it can be deployed into the plant planning system in order to operationalize a daily optimized production plan for each month, week, day and shift.
This is one example of how analytics can help and enable manufacturing leaders to protect their employees and support a safe, productive workforce in the context of evolving COVID-19 challenges. This information could be especially useful as more states and regions begin to slowly open their economies back up over the next few weeks and months.
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