A production planning and control definition has two elements:
Production planning includes:
Production control includes:
This definition deliberately includes activities that belong to other disciplines such as sales and marketing, as well as raw material procurement. The reason is that effective production planning and control doesn't happen in a vacuum, but is part of an integrated approach that considers all production planning and control responsibilities; it's not just a part of the business.
One of the first steps in production planning and control is determining demand. Usually performed by sales and marketing, demand planning is crucial in that it determines the required production capacity and raw material requirements. While erroneous demand forecasts undermine profitability, it's surprising how many executives still rely on gut feel when deciding future demand. Perhaps it’s because they don't have the right tools to analyze sales information and arrive at data-driven decisions.
The next step is to consider the available production capacity, especially in terms of:
This information helps determine whether there's sufficient capacity to meet demand. The ideal is to match capacity to demand, which is relatively simple if demand is steady. Unfortunately, this is rarely the case, with peak demand occurring during well-defined periods such as over Christmas or such as planting and reaping times in the case of agricultural equipment manufacturers. In most instances, management needs to consider demand shaping to optimize supply and demand.
Once the overall production plan is settled, it's time to consider raw materials procurement. The starting point is the bill of materials (BOM). It's the procurement department's responsibility to procure items required for each build so items arrive in time to meet production requirements. This is sometimes a balancing act, as some items are unique or may have long lead times, whereas others are off-the-shelf items.
Most large organizations use Materials Resource Planning (MRP) to determine production sequences and detailed material requirements to satisfy the build. MRP solutions also have the ability to determine the complex sequence of operations to produce each item; this is also known as routing.
Besides MRP, other production planning tools for improving production efficiency exist such as Kanban, Just in Time (JIT), Optimized Production Technology (OPT) and Flexible Manufacturing Systems (FMS). Each has its strengths and specific objectives. Some, such as Kanban, are largely manual systems, while others use sophisticated algorithms to manage production scheduling.
While the overall concept of production planning and control is simple, in reality it's a fiendishly complex and difficult-to-control process. Late delivery of critical components can stop production lines, as can quality control issues, unplanned downtime and labor shortages.
Production management needs support, as they often have to make decisions on the run with little time to consider alternatives. Unfortunately, conventional planning tools are relatively inflexible and, as they generally update overnight, can't provide immediate answers. This means many managers turn to spreadsheets to determine the best solution, a process that can be cumbersome and prone to errors. Often, the only practical answer appears to be costly measures such as unplanned overtime to meet production schedules.
While these measures may work, their impact on operating costs is significant. What if production managers could quickly consider alternative scenarios and discover optimal solutions that reduce overall costs as well as answer other production planning and control questions?
As fanciful as this may seem, this is exactly what many large organizations are doing through leveraging prescriptive analytics to find data-driven answers to production challenges. For example, Unilever achieved radical savings in capacity planning and production allocation through using prescriptive analytics for modeling their production lines.
An optimization model uses mathematical techniques such as linear, mixed integer, nonlinear and constraint-based programming to determine the best answer to a complex scenario. Using a modeling language or platform, it's possible to prepare a digital model to accurately reflect how your production line and business functions. Solver software then allows you to evaluate different scenarios and determine the optimal values for objective functions such as profit, operating cost or ROI.
Because the model accurately reflects the organization, it's possible to run different scenarios to determine which of several alternative actions offer the best result. Here are three production planning and control examples.
Optimization modeling allows managers to move away from error-prone spreadsheets and discover the real secrets to boosting production planning. This includes the ability to run scenarios to find the best way to optimize production while minimizing operating costs.
Optimization modeling allows you to dynamically determine optimal stock levels for any situation, freeing you from the tyranny of static solutions based on the day's stock as well as the limitations of ABC stock analysis. It helps you optimize supply chain management and answer important supply chain management questions.
By modeling the entire organization, it's possible to integrate and optimize supply and demand planning simultaneously. This holistic view of the organization opens up numerous possibilities that would never be apparent when functions operate in silos. In this way, changes in demand are reflected in supply production requirements so that changes made by one functional manager are immediately apparent throughout.
Prescriptive analytics takes the guesswork out of production planning and control decisions. While it doesn't replace the functions performed by ERP and MRP solutions, it allows management to use the data derived from these systems to optimize manufacturing decisions and to balance complex trade-offs. Added to that is the visibility into how the manufacturing functions together with an ability to update models to reflect new production lines and facilities.