Intended learning outcomes: Produce an overview on the technique (the algorithm) for order-oriented finite loading. Describe various priority rules as well as dealing with exceptions. Explain an example of order-oriented finite loading. Present an evaluation of the technique. Identify its limitations and typical areas of application.

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Depending on the technique that is used, order-oriented finite loading achieves maximum capacity utilization or ensures that as many orders as possible are executed on time with low levels of goods in process.

Overview: Orders are scheduled in their entirety, one after the other, in the time periods. If the period begins with an empty load, any orders that have already started are scheduled first, and only those operations that have not yet been carried out are considered.

Planning strategy: The objective is to find priority rules that will enable as many orders as possible to be completed. Special attention is given to those orders that cannot be scheduled, and whose start and completion dates must be modified as a result.

Technique: The planning horizon is once again divided into time periods. Individual orders (and all their operations) are scheduled in the order determined by the specified priority, without intervention by the planner. If the capacity limit for an operation is already exceeded, there are three possible responses: load the operation, defer it, or refuse the order. Once every order has been either planned or rejected, the planner handles the exceptions. The algorithm then attempts to plan rejected orders or those whose completion dates have been altered. Figure 14.3.2.1 illustrates the principle of the resulting algorithm.

Fig. 14.3.2.1       Technique (algorithm) for order-oriented finite loading.

The details of the individual steps of the algorithm are as follows:

Determine the orders to be scheduled and treat them according to priority; typical orders are, firstly, all orders already begun (we know what operation is waiting to be carried out next from the order progress data;[note 1408] all outstanding operations should be scheduled), and, secondly, all orders not yet begun whose start dates lie within an arbitrarily chosen time limit (this limit defines the anticipation horizon, which should ideally be smaller than or equal to the planning horizon; the start date should also be set or calculated using a scheduling method).

The possible priority rules are similar to those presented in Section 14.3.1, although here they apply to the entire order and not just to the individual operations:

• Proximity of the start date for the order (orders with fixed start dates can be loaded first)
• Proximity of the order due date (EDD, earliest due date)
• Ratio “remaining lead time for the order divided by the time still available for the order” (SLK, shortest slack time rule, » order urgency; see Section 13.3.6)
• Ratio “remaining lead time for the order divided by the number of remaining operations”
• (External) order priority
• Any combination of the above

Handle and load operations in order: All operations are loaded at the corresponding work centers for the time period in question, working forward, beginning with the earliest start date, or backward, beginning with the latest completion date. Inter­operation times are also considered, but queue times are not.

Deal with exceptions: If an operation falls within a time period during which the associated work center’s capacity is already fully utilized, the following three possibilities can be applied:

a.    Load without considering available capacity: This option is suitable for orders already begun or for relatively short operation times. Some general reserve capacity is thus kept free for the latter operations.

b.    Defer the operation until the next period with available capacity (defer with forward scheduling, move forward with backward scheduling).

c.    Unload the entire order, to give priority to other orders.

Deal with all exceptions that could not be handled earlier: If the steps described above have been carried out for all orders, the following contingencies requiring action may arise, depending upon which exception rule is applied:

a.    For every capacity that is overloaded in a particular time period, either provide more capacity or unload orders accordingly.

b.    (1) Backward scheduling: The resulting latest start date for an order lies before the earliest start date. Unload this order and then try again using forward scheduling, beginning with the earliest start date. (2) Forward or probable scheduling: The resulting earliest completion date for an order lies after its latest completion date. If the order due date is flexible, defer the order accordingly. Other­wise, it may be necessary to deliberately increase the fully utilized capacity to first unload the order.

c.    For every unloaded order: It may be possible to bring forward the start date. If the order due date is flexible, defer the order. If the fully utilized capacities are at least a bit (quantitatively) flexible, they may be increased accordingly.

The unloaded orders are then scheduled in another iteration of these steps of the algorithm. This technique could quite conceivably be applied interactively, that is, “order by order”: If an operation falls within a time period in which the capacity limit is already exceeded, the planner can immediately decide on the appropriate action.

Figure 14.3.2.2 shows the results of order-oriented finite loading after the first iteration, using exception rule (c). This example uses the same orders as in Figures 14.2.1.1 and 14.3.1.2, specifically P1,  . . . , P6, and the same work centers, namely, work center A and work center B. Priority was assigned in ascending order by order ID. Again, “preload” represents operations for orders that were loaded before orders P1,  . . . , P6.

Fig. 14.3.2.2       Example of order-oriented finite loading, exception rule (c): unloading.

Exception rule (b) would have produced results similar to those in Figure 14.3.1.2, that is, similar to operations-oriented finite loading. The more that exception rule (a) is applied or capacities are increased in the last step, the more infinite loading is obtained.

The following prerequisites must be met to use this planning technique:

• Capacities and loads must be sufficiently reliable, that is, the planning data and reported work progress must “tally.” Errors can accumulate very rapidly in the calculated dates if this is not the case.
• Due dates must be sufficiently flexible — especially for exception rule (b). The order completion date results randomly on the basis of the existing utilization of production capacity. Lead times can sometimes be much longer than normal.
• Exception rules (a) and (c) are suitable for order due dates that are relatively inflexible. For these, however, the capacities must have some flexibility; other­wise, the administrative effort needed to regularly change dates would become unmanageable or so imprecise that capacities would be poorly utilized.

This creates the following limitations:

• The farther we plan into the future, the smaller our chances that the planning forecasts will prove correct. For this reason, the technique is only sufficiently exact for short planning horizons, and it must be repeated at regular intervals.
• In long-term planning, the technique calculates a permissible plan, in the full knowledge that it will change in the short term. Regular and efficient replanning is thus needed as the term becomes shorter.
• In short-term planning, for exception rule (b), any scheduled operations must once again be completed during this period. The technique does not allow local, reactive replanning. Exception rules (a) and (c) do, however, allow some potential degrees of freedom for reaction if capacity is not fully utilized.
• Exception rule (b) leads to the best possible utilization of capacity. As with operations-oriented finite loading, long queues may arise. Goods in process then tie up capital and even hold up the entire production plant. Choosing a “neutral” priority rule will distribute the delay more or less evenly among all the orders.
• Exception rule (c) loads production only with the orders that it is capable of processing. It thus results in lower levels of work-in-process and shorter lead times. Successfully planned orders are completed on time. Exception rule (c) essentially uses the model of the queue presented in Section 13.2.1, that is, the reservoir or open funnel model. If the funnel does not overflow, the production plant will not be held up. Thus, if further processing of an order is delayed excessively (e.g., over at least one time period), it should be rejected, rather than loaded.
• With inflexible capacities, on the other hand, exception rule (c) leads to lower utilization of capacity as soon as completion dates have to be deferred. This is because the load that would have been caused by operations earlier along the time axis is now missing. If there are no other orders, the capacity is wasted. Deferred orders will have long delays, and it may even become impossible to accept new orders.
• If the time between the earliest start date and the latest completion date is longer than the required lead time, then a start date and an end date that falls between these two extremes may be more suitable for the overall mix of orders. It is worth considering the load-oriented order release and capacity-oriented materials management (Corma) techniques outlined in Sections 15.1.2 and 15.1.3. Load-oriented order release, in particular, can actually be regarded as a generalization of order-oriented finite loading with exception rule (c).
• Interactive planning, that is, order by order, is only efficient if relatively little effort is needed to load an order compared to its value added. In addition, we need continuous knowledge of the total load on the work center resulting from previous orders, so that a very fast database is required. We also have to keep load totals for each time period. To create sufficiently simple and rapid algorithms, the length of the time periods for each work center and along the time axis must then be defined as fixed.

Typical areas of application are as follows:

• As with operations-oriented finite loading, exception rule (b) is suitab­le for batch production over a long period or in a monopoly situa­tion or seller’s market. Typical industries here are chemical and food processing industries and niche capital goods markets.
• Exception rules (a) and (c) are suitable for many discrete manu­facturing industries, wherever there is the minimum required level of (quantitatively) flexible capacity. This is more often the case than we might at first suppose, even in short-term planning.
• For short-term planning and control. For this planning range, the technique provides, firstly, with exception rule (b), an actual work program for the next few days, and, secondly, with exception rules (a) and (c), an acceptable work program that also allows a degree of situational planning. The horizontal bar chart provides a rapid overview of all work centers and all orders, as it requires little space. It corresponds to the familiar planning board in production control. Individual orders can often be replanned very efficiently — in the case of the electronic control board (Leitstand), through the click of the mouse.
• For long-term planning of few orders with high value-added and regular planning and replanning. For replanning individual orders, the advantages are again the clear display and ease of manipulation mentioned above.

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Course section 14.3: Subsections and their intended learning outcomes

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  14.3 Finite Loading

  Intended learning outcomes: Explain operations-oriented, order-oriented, and constraint-oriented finite loading.

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  14.3.1 Operations-Oriented Finite Loading, or Operations Sequencing

  Intended learning outcomes: Produce an overview on the technique (the algorithm) for operations-oriented finite loading, also called operations sequencing. Explain an example of operations-oriented finite loading. Describe various priority rules. Present an evaluation of the technique. Identify its limitations and typical areas of application.

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  14.3.2 Order-Oriented Finite Loading

  Intended learning outcomes: Produce an overview on the technique (the algorithm) for order-oriented finite loading. Describe various priority rules as well as dealing with exceptions. Explain an example of order-oriented finite loading. Present an evaluation of the technique. Identify its limitations and typical areas of application.

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  14.3.3 Constraint-Oriented Finite Loading

  Intended learning outcomes: Identify bottleneck capacities and the drum-buffer-rope technique. Describe the drum, the buffer, and the rope. Present an evaluation of the technique. Identify its limitations and typical areas of application.