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Planning and Scheduling
Part 1 of 4

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Lean Manufacturing Articles

Companies invest millions of dollars and critical re­sources to select and implement complex, fully inte­grated enterprise resource planning (ERP) systems that, among other things, are designed to provide manufac­turing schedules. Modern ERP systems are richly fea­tured to provide forecasting, master scheduling, material and shop scheduling, and production activity control functionality. Yet, with all those capabilities, manufac­turers still struggle to achieve manufacturing success. And the quest for results lies at the end of a long pro­cess of continuous process improvement towards the elusive goal of "Class A."

With all the time and money spent to generate sched­ules, corporate executives are often frustrated, and one of the most frequently asked questions in manufactur­ing firms is, "Why doesn't the shop follow the sched­ule?" I have struggled with this question most of my career, constantly seeking to improve the scheduling process, installing new systems and implementing a va­riety of wonderful operating and quality philosophies. I have achieved very good business success with Class-A ERP systems and manufacturing strategies aimed at being agile, lean, quick, and just-in-time. However, what I have learned more than anything is the wide variety of reasons manufacturing has at their disposal to describe why they should not or could not comply with the best schedule produced. My response was generally to find a better dispatch rule or establish a program to re-scrub the MRP data needed to develop a better schedule.

I have since come to realize that only a much higher level of detailed finite scheduling, using very powerful modeling capabilities with flexible scheduling and dis­patching logic, will provide consistently doable sched­ules. Advanced planning and scheduling (APS) engines have the power to provide a schedule that considers the linked dependencies of shop orders against the finite capabilities of resources to produce a feasible schedule. And, by instituting a manufacturing process that reviews the integrity of the daily schedule, that doable schedule can be "bought off' by manufacturing, rather than be­ing "thrown over the wall" from scheduling to the shop.


The key in getting compliance to a schedule is that it is doable. Not at a high level of capabilities lumped into weekly time-buckets at a workcenter level, but doable at a level that can consider all the constraints on manu­facturing at a man, machine, tool, and material level;

doable in terms of aligning with business, customer, and manufacturing objectives; doable with regard to the unique needs of each resource to perform effectively with regard to setup groups, product characteristics, and se­quence dependent efficiencies. If all these factors are considered, a schedule will make sense to our "users." A schedule that can take these factors into account has the potential to be acceptable to manufacturing.

This last notion of creating an acceptable schedule is one of the key features of a good APS engine that can lead to significant results in manufacturing. Even with all the capabilities of a powerful APS engine to create a finite schedule based on the factory that it models, the ability to change the model and simulate the results of a change are vital. If we are looking for schedules that we can expect to be followed, the process to get buy-in must be very receptive to input. What we have seen re­peatedly is that although APS will generate a high-qual­ity finite schedule, unique circumstances, product mix or data error can cause a schedule to be erroneous— maybe not entirely wrong, but not good enough to be credible. The most successful APS implementations must bring the APS scheduling process to the floor. Es­pecially in the initial phase of APS use, a review of the schedule is an essential part of getting manufacturing to accept that a schedule is feasible.

To Be Continued

For balance of this article, click on the below link:

Lean Manufacturing Articles and click on Series 14

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