APO:The New Savior?
With newer technologies of data processing, people found a “technology” solution to the MRP II problem. A good algorithm of optimization, which can do a simultaneous check against multiple constraints, can provide answers faster than the sequential and batch process approach of MRP II technologies could. The key selling point was the best of both worlds -optimal usage of capacity and other limiting conditions (material or tools) along with high on-time delivery. The capability to do fast on-line processing also made “what-if” analysis and rescheduling much easier than MRPII did. “On paper”, one had found out the way to avoid overloads, right in the planning phase when order due dates were being set. It seemed as if everyone had finally got the elusive silver bullet!
However most of the implementations of APO failed to give the desired results – overloads happened in execution. The feature of frequent rescheduling actually did not help much. The plants which tried to do so had to stop it immediately because frequent rescheduling amplified a small uncertainty into chaos in the shop floor. In some cases, the schedules churned out by APO tools did not make intuitive sense to shop floor managers so they did not follow it.
Faulty Assumption of APO systems – A perfect World!
APO was built in a lab without considering the “real world”. The practical world has two problems, which make it difficult to define capacity accurately at any point of time.
* Variability is a way of life – there is no perfect plant without breakdowns, rejections, absenteeism, and even changing demand requests.
* Changing Product Mix (capacity available can change based on product mix loaded on the plant at a point of time)
The combined effect of these two factors makes it difficult to precisely define capacity. Product mix changes impact capacity and so doe’s variability in terms of worker skill, machine conditions and many other factors. It is nearly impossible to consider all possible factors that can accurately define capacity at a point of time.
Because of the above conditions, it is difficult for a computer schedule to match the intuition of a plant manager. A plant manager will always have more information (not considered by the computer) to arrive at a “better” decision. For example a dyeing department would want a specific color sequence to maximize its output but the subsequent spinning department would want a conflicting sequence based on its need to produce a desired sequence of “count” of the yarn. In the real world, the schedule given by a computer, which has globally considered both, may not be acceptable to either work center manager. On a specific day, the dyeing manager may want to avoid making the difficult shade of color (as per the schedule generated by the computer), because the most experienced person who can do the color mixing without rejections for the “difficult” shade is absent. At the same time, the spinning manager might want to avoid taking up a particular schedule because it is not productive to produce the specific yarn after breakdown maintenance. Such numerous considerations cannot be incorporated in the capacity definition because at times such conditions are also not “rigid” enough to be followed. Considering all such numerous conditions as rigid can de-rate the overall capacity of the plant.
The other problem originated from the very stated advantage of APO algorithms they can optimize under environment of multiple constraints. In an environment of dependencies (the way one schedules a particular work center has an indirect impact on subsequent work center), it is mathematically impossible to maximize the usage of multiple constraints to the full capacity. At the same time, when there is variability in the system, namely shortages, rejections, breakdowns etc, there is a need to leave aside adequate buffers in all identified constraint to maintain stability in the system. Without adequate buffers, one would be forced to reschedule very frequently with even minor variation. When multiple work centers are rescheduled on every variation, de-synchronization sets in feeding departments making the plant chaotic. The waiting time can amplify many times over multiple work centers. Alternatively, if one wants to have a stable schedule from an APO in an environment of multiple constraints and seemingly conflicting objectives, the level of buffers required at multiple places would make the plant stable but reduce the output significantly.
APO investment ultimately resulted in lot of efforts and resources without any meaningful outcome. The plant performance in terms of on-time performance remained at the same level.
The Theory of Constraints Approach
The way to maintain reliability is to have stability in the due date scheduled, variability should not force change in schedules. This required one to keep aside protective capacity while scheduling, which means, practically, there should be only one constraint in the plant. Having many constraints will force one to keep buffers in many places with low overall output of the plant. So with a capacity buffer on the single constraint resource, one could get a schedule that stays stable without trying to have buffers in many resources. This approach can ensure one has the maximum output from the plant as a whole while maintaining a stability of due dates.
This means that other limiting conditions (or constraints) have to be removed. This may seem like an impractical idea due to potential investment required. However, in most plants, various visible “multiple constraint” problems are more of a symptom than a real problem. An environment of very high WIP in a plant can create temporary bottlenecks in many work centers. At the same time, it could also lead to cases of artificial material shortages due to diversion of common material used across orders. When there is lot of WIP, and every work center is driven by utilization/efficiency, each work center “cherry picks” components across orders. Also, the consideration for “cherry picking” is different for different machines. As a result production lead-time goes up and order reliability becomes extremely poor. This unreliability creates urgencies/fire-fighting in the plant due to late orders. Urgencies create additional set-ups creating multiple bottlenecks leading to low plant output.
As a first step towards removing the symptom of multiple constraint and associated chaos is to reduce the WIP, and forcefully maintain the WIP at a constant low level. With low WIP, the opportunity to “cherry pick” orders is limited as there are few orders on the shop floor. However, very low WIP can lead to starvation and low output. The way to check if one has excessive WIP is to compare the touch time of an order with the total production lead-time. If the touch time is less than 10% of the lead-time, and if there is day-to-day fire fighting with frequent requests for expediting, the WIP is definitely high. In such an environment, halving the WIP does not lead to starvation. Reduced WIP along with a priority system focused on order completions prevents wastage of capacity and the output of the plant goes up. At the same time, with reduced WIP, the real constraint is revealed. As part of the solution, a constant reduced WIP is maintained before the constraint resource. All other resources subordinate to ensure there is no starvation at the constraint resource. The material release to the plant is based on the WIP maintained. So, if the output of the constraint resource falls (due to uncertainty or product mix changes), further material release is stopped to maintain the WIP. Similarly, if the output of the constraint resource increases (due to no murphy or favorable product mix), material release is increased so that there is no starvation at the constraint. In other words, WIP is maintained. This mechanism of “pull system” of using constant WIP ensures maximum utilization of the constraint resource without the need to precisely define capacity in the planning phase. At the same time, one could leave behind a buffer capacity in planning (while quoting due dates) without any fear of losing it in execution. This would also ensure that the reliability of orders goes up.
Guidelines for creating a highly reliable plant
1) Ensure constraint stays at one place in planning and execution
2)Use buffer in capacity loading while quoting dates
3) Use pull based material release on a constant WIP system to ensure utilization, rather than depending on precise capacity definition during planning
4) Define a clear priority system for orders
5) Follow daily perpetual demand planning rather than a “bucket” based planning
In a plant where reliability (i.e. order due date performance based on initially committed dates) is extremely high, there is no need to follow the concept of a planning horizon or the bucket system of planning. One can follow a system of daily perpetual planning of new orders, without following the planning bucket (or horizon) system for rescheduling based on observation of past period performance. The concept of daily perpetual planning also ensures high utilization throughout the month.
In some manufacturing plants, there can be cases of interactive constraints due to drastic changes in product mix. The only way to solve this problems to ensure that orders are throttled in planning as well as releases to ensure constraint stays at one single place both in planning and execution. In the long run, it pays to elevate such temporary bottlenecks to ensure a stable plant.
The execution based pull system of Theory of Constraint takes away the need to be “perfect” in planning. The silver bullet in manufacturing systems lies in the approach of “good enough” planning (schedule with capacity buffers) coupled with a perfect execution by the way of controlling Wipe very day.
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