Determining the quality of a plan depends on how we measure its success. Only a plan with clearly defined objectives can easily be evaluated afterwards. And only a plan that is thoroughly analyzed afterwards can be redefined for the next planning process. This process usually happens in an asynchronous mode where the generation and the execution of a plan are separated — not only in time, but also with respect to the people and information systems involved. This separation of planning and execution is the reason for many common production problems.

However, a new generation of manufacturing systems called advanced planning and scheduling systems (APS) has emerged that are designed to overcome this shortcoming.


Advanced Planning and Scheduling
The term advanced planning and scheduling systems (APS) has just recently been generated, and the products represented in this category are quite diverse. Typically, APS systems are categorized in one of two categories: planning- centric and scheduling-centric systems. Planning-centric systems focus on longer-term tactical objectives (e.g., master scheduling, demand management, distribution scheduling and optimization of procurement). Planning for global or multi-plant companies also means determining the best potential production site in a global network of manufacturing locations. In every case, planning defines certain business objectives. Analysis then determines the constraints that might affect the accomplishment of these objectives.

It is important to note that constraints play quite a different role in long-term planning than in short-term scheduling. The longer the planning horizon the less specific and the more flexible the constraints. If there is enough time, practically all constraints can be relaxed. After all, you can build a new plant, move to another country and buy a supplier or a competitor if you have enough time. If the planning horizon gets shorter, however, constraints become more firm and cannot easily be adjusted to better serve the objective.

Scheduling-centric systems usually focus more on tactical objectives. A schedule is generated for shop floor production, short-term material deliveries and immediate shipments. Constraints for short-term scheduling are quite real. Finite capacity of machines, personnel and tools capacity are often a given and allow only limited changes. Material availability at the right time might still be a constraint that can be influenced to some extent (e.g., Just-in-Time delivery). But often material and component availability are also a given and not adjustable in the short term.

Schedule-centric APS systems are designed to deal with these finite constraints. Taking a required production output as the objective and putting it into the context of the limitation through constraints to determine a feasible solution is the challenging task of a scheduling system. The time horizon for scheduling systems is usually short and schedules have to be adjusted or regenerated frequently.

Although both categories of systems deal with different time horizons and different constraints, the result of both systems remain intentions. It is important to note that no matter how short the time horizon of a scheduling system, its output is still a suggestion for actual production and not the result of production. Not until the plans and the schedules are executed do we have actual data. It is at this final step when reality kicks in, when products are actually manufactured, that gets surprisingly little attention in the APS discussion. Obviously, there have been systems available for decades to collect information on the actual production process, and the collected information has been stored in historic data tables for future evaluation of production standards — an evaluation that 80 percent of all customers never actually perform. But this actual production data, this shop floor reality, finds very little attention with respect to its impact on scheduling and planning. Is reality not important?

Does Reality Matter?
There is the argument that if the plan is good you do not need to be worried about the execution. The better your long-term plan — so goes a common argument — the less important it is to pay attention to short-term scheduling and execution. So if the plan is good enough, does reality matter? Did not the plan prepare for reality well enough so that we do not expect to receive much benefit from spending a lot of time and energy to schedule and execute?

No doubt the better the plan, the better the execution. But without the feedback from actual production, planning and scheduling miss the critical reality check. It is the actual production information that validates the plan and allows for correcting it. This information includes actual production progress as well as peripheral information that might affect the production progress. Production progress information reports quantities completed, time completed, quantities that need to be reworked, etc. Events that might affect the production progress relate to equipment status, personnel available, material availability and other resource and constraint-related issues. Both categories of information will affect the execution of the plan.


How Does Reality Impact My Plan?
To make an APS system successful it is important to feed this information back on a continuous basis and update our schedules and plans accordingly. Without the feedback, we will not know what the quality of the plan or the schedule was until we have the final production results (e.g., percent shipments on time). If the results are not satisfactory, we will accept that the plan has to be revised for the next production run, but we will not know exactly what needs to be changed. Nor will we be allowed the opportunity to adjust the schedule in order to improve the immediate results. Since we deal with asynchronous information — results of the execution of the plan will be evaluated long after the plan has been executed — we will not know where and when a problem started to occur. Where did we deviate from the plan so that we did not get the results planned? Or did the plan have some wrong assumptions that made it impossible to implement? We will change our plan for the next time and afterwards we will measure asynchronous results again to determine whether our changes were right.

There is a much better, however not necessarily easier way, to deal with this problem. If the feedback occurs as frequently as possible, ideally in real time, and if the feedback is allowed to be compared with the plan, it can be used to correct the plan any time a deviation occurs. Operating in this mode allows us to capture exactly where the plan was wrong and allows us to take corrective action before the deviation affects the final result. This seems to be easy and logical, so why has it not been implemented in classical manufacturing systems and not even in almost all APS systems? The answer is: It is not so easy to deal with reality.


System Design for a Real-time APS System
Most APS systems use highly complex logic to determine the best possible plans and schedules in an environment of conflicting constraints. To do this, APS systems are required to constantly balance conflicting constraints based on predefined business objectives. A typical example is the conflict between inventory levels and on-time delivery. (How much inventory am I prepared to keep in order to have a high customer service level? What is the optimum for my business?) In addition, APS systems often have to consider complex production rules and a multitude of production constraints. Often, long-running optimization algorithms with multiple iterations are required to generate an "optimal" plan or schedule. But who wants to alter a schedule that has been so complex to generate? And what is an appropriate systems solution for that problem?

There are a number of important prerequisites for a successful feedback loop to APS systems. First, APS systems have to have different approaches for long- and short-term scheduling. Sophisticated long-running optimization is useful for long-term production planning but has little use in short-term production scheduling. Good APS systems offer high scheduling speed for short-term scheduling.

Second, it is important that the APS systems allow the user to clearly define business objectives and let these business objectives drive the generation of the plan or schedule.

A third important issue is the capability of APS systems to import the real-time production information and make this information available to the user in the most comprehensive way. This includes not only displaying the updated information in a timely manner, but doing it in such a way that the user immediately knows the impact on the current schedule and the impact on the business objectives. A dramatic change of the current schedule can still be irrelevant for the business objective (e.g., getting all orders completed on time).

Finally, the last and most important issue is to allow for easy correction of the plan or schedule based on the new information. To be able to do this the user should have an easy way to choose between several corrective actions and be able to evaluate different alternatives based on their value in light of business objectives. The APS systems should make intelligent suggestions and allow the use of a technology called incremental scheduling.

Incremental scheduling allows the user to adjust only parts of the overall schedule in a highly interactive way (drag-and-drop or automatic regeneration of parts of the schedule rather than the whole schedule) and show, for every action taken by the user, the impact related to this particular action. This interactive capability of an APS product is essential to accomplish the critical feedback loop.


Benefits
APS products offer to bridge the gap between planning and execution. To bridge this gap seems to result in dramatic benefits for manufacturing companies. However, it is important that the APS products allow continuous feedback and frequent update of the plans and schedules based on the real production information. The frequency of the update depends on the level the APS system is used at, but it has to have a continuous mechanism to compare the objective with the results. APS systems often offer payback times in a matter of weeks after implementation. The improvement of resource utilization, customer service levels and operating costs can be dramatic. Manufacturing companies are beginning to recognize the impact and, as a result, the market for APS systems is growing by about 50 percent every year, with company success stories abounding in almost all industries.

Copyright © 2020 by APICS — The Educational Society for Resource Management. All rights reserved.

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