SCHEDULES: FUNDAMENTALS, EXAMPLES, AND IMPLEMENTATION
2. FUNDAMENTALS OF MASTER PRODUCTION SCHEDULING
MPS is at the interface between strategic planning and tactical planning in an integrated production planning and control system. As such, it is a key decision-making activity, in which strategic goals from business planning are translated into an anticipated statement of production, from which all other schedules at lower levels are derived. The MPS function is an essential part of the production management architecture, and, as such, it should be given high priority when developing an integrated manufacturing system (Higgins & Browne, 1992).
According to Slack et al (2001), the master production schedule is the most important planning and control schedule in a business, and it forms the main input to materials requirements planning (see Figure 7-1). It contains a statement of the volume and timing of the end products to be made; this schedule drives the whole operation in terms of what is assembled, what is manufactured, and what is bought. It is the basis of planning the utilization of labor and equipment and it determines the provisioning of materials and cash.
The master production schedule also provides the information to the sales function on what can be promised to customers and when delivery can be made. Therefore, sales function can load known sales orders against the MPS and keep track of what is available to promise - ATP (Slack et al, 2001).
Customer orders
Bill of materials
Purchase orders
w
^ W
Master production
T
^
^
Material requirements planning (MRP I)
^ T
Materials plans
^
^
Demand forecast
Inventory records
Work orders
Figure 7-1. The master production schedule in the MRP I schematic (Slack et al, 2001).
The American Production and Inventory Control Society (APICS) defines master production schedule as:
1) The anticipated build schedule for those items assigned to the master scheduler. The master scheduler maintains this schedule, and in turn, it becomes a set of planning numbers that drives material requirements planning. It represents what the company plans to produce expressed in specific configurations, quantities, and dates.
The master production schedule is not a sales forecast that represents a statement of demand. The master production schedule must take into account the forecast, the production plan, and other important considerations such as backlog, availability of material, availability of capacity, and management policies and goals. 2) The master schedule is a presentation of demand, forecast, backlog, the MPS, the projected on-hand inventory, and the available-to-promise quantity. (Cox III &
BlackstoneJr., 1998)
From the production, sales and/or operations plans, which consider products organized in families or product lines and a long time horizon, the MPS transforms general information into detailed, disaggregating such plans into detailed programs, individually defined for each end product, usually written in weekly and/or monthly time periods. In other words, tactical production planning processes a decomposition of the goals established by the aggregate planning (Femandes et al., 2000). In case of manufacturing, tactical planning disaggregates groups of resources into machines or production lines, years into months and months into weeks, if applicable.
In general, manufacturing enterprises must have these objectives in mind:
maximize customer service and resource utilization and minimize inventory
levels. Ideally, this means operating the plant on levels next to production available capacity during all the time, with inventory levels next to zero, and maximum service level. This would imply that when a customer places an order, that product would, at that moment, be leaving the production line towards the dispatching area. The challenge is to plan production to operate it in a comfortable steady pace, building minimum inventory, and taking into consideration costs caused by changing production rates and carrying inventory (Bonomi & Lutton, 1984). But one knows that these are conflicting objective measures. If one tries to minimize inventory level, for instance, not having enough products to meet unexpected orders may result in degradation of service levels. The contrary is true; having inventory is acceptable in order to meet customer demand, however too much of it will increase costs. Production planning, MPS especially, must take all these matters into consideration and also that production is generally a multi-task procedure (different operations), distributed in a multi-period discrete horizon.
The objective of the planning process is to plan all production activities necessary to meet demand forecasts and, secondly, to meet immediate requirements and promised orders. The production planning form most used seems to be hierarchical (see Figure 7-2), proposed by Vollmann et al.
(1992), although similar hierarchies have also been presented by others (see Figure 7-3). Following Vollmann et al. (1992), initially an aggregate plan is established, considering aggregation of end-items into classes of families and covering a long term horizon. Decreasing the planning horizon and considering end-items (or stock keeping units - SKUs), the production planning becomes what is known as a master production schedule. The following hierarchical level comprises the materials requirements planning (MRP or MRP I), which will define when and how much should be ordered, mainly in terms of raw materials, components and, if appropriate, sub- assemblies. The last hierarchical level relates to scheduling tasks and operations needed to accomplish the master plan - it is called production scheduling. The MPS, therefore, is the crucial input information for production scheduling, and this chapter focuses on it.
Cavalcanti & Moraes (1998) show an approach to the master production scheduling process with the intent to cover a gap existing in scientific publications in the field, which only superficially consider the real complexity involved in such a process. In this sense, it is easily seen that the literature, in order to introduce the actual MPS process complexity to the novice, ends up making too many simplifications that hide the real difficulty inherent in the MPS creation. Some of these simplifications are:
• Not considering that production capacity (work centers, production lines or cell, machines, workers and tools) is limited;
• Often, a changeover time will incur every time a new product is to be made at a production line or work center. These changeover or setup times usually demand a non-negligible time, which varies from product to product and their production sequence. Sequence dependent setup times are then to be considered since a different production sequence can yield dramatic savings in the use of limited resources. In such cases, the MPS process should consider a changeover matrix.
• Avoidance of routing flexibility, that is, there is not only one production resource that can produce the product. This routing flexibility increases the complexity in the MPS process.
Even authors of renowned books in this area simplify the explanation of how complex the MPS process really is (VoUmann et al, 1992; Slack et al., 2001; and Gaither & Frazier, 2002).
The MPS constitutes one of the modules part of the production planning and control structure. There are not, however, a commonly adopted form for this structure. It is the result of several factors like promised delivery dates from suppliers, production capacity, strategies and objectives (e.g., minimum inventory levels), and considers information exchange between departments, such as between manufacturing and marketing - for the production and sales forecasting.
Master production scheduling becomes a very complex problem as the number of products, number of periods, and number of resources (production lines assembly lines, machines, production cells) increase. In fact, Garey & Johnson (1979) proved that production planning problems are NP-hard. Yet, setup times and overtime can make this problem even more complex. Moreover, as seen previously, production planning problems usually involve conflicting objectives, like minimizing inventory and maximizing service levels. Because of all this, use of heuristics or meta- heuristics is suggested for the resolution of these types of problems. Since absolute optimal solution finding might be extremely time consuming, a good, perhaps close to optimal, in reasonable computer time is preferred.
Several artificial intelligence meta-heuristics have been applied to optimization, among them, genetic algorithms, tabu search, ant colony, beam search and simulated annealing. Some of these techniques are explained in the following sections.