ATD adds a new dimension to the planning process. It answers the dilemma we had for many years: should we reschedule or not? As long as the requirements of the master schedule translate to a supportable detail schedule, there is no problem. Once we start to have schedule conflicts we cannot resolve, especially for a large, complex assembly (like an airplane), we have a dilemma. If we reschedule to a feasible date, we may have a "creeping schedule" phenomenon, for two reasons:
• With a new schedule we lose priority for allocation. Parts may be reallocated to other requirements with an earlier need date that did not change. As a result, a small reschedule may end up with a much larger actual delay.
• The MRP schedule is inherently critical. There is always a statistical probability that another problem will pop up. At each reschedule, all suppliers get a new target schedule, so the distribution of the actual results will happen around the new date, again and again. If we don't reschedule, we keep the target stable and the priority the same, but the schedule becomes only a priority driver, and there is no visibility of the real plan. The ATD process adds another set of schedules to the planning process, enabling us to retain both: what we want to happen, and what will happen. This will stabilize the schedule yet provide visibility as to what the real plan is.
The theory and methodology of optimization exists for several decades. However, optimization is a computation-intensive process, which requires processing power that has not been available until recently.
Why do we need optimization, and when can we use it?
Optimization is relevant in cases where we have a choice between alternatives. If we need 10 pieces of a part and we have 10, we don't need to optimize. The same is true when we have none. However, suppose we have 6 pieces out of the 10 we need, and they may be used in several assemblies. Now we must make a decision. MRP is a deterministic, rule-based process. It follows that process whether it yields optimal results or not. An optimization engine will attempt to improve on this by finding the best solution out of many potential combinations, following no predefined "rule." Another example: When we have one supplier and one destination for our products there is no choice. However, when we have many suppliers, several distribution centers, and many final destinations (retail outlets) we have many alternatives, and no one rule can find the best solution. Optimization sounds complicated, and sometimes it can be a scary process. It is not supported by intuition, and actually in many cases intuition will lead us away from the optimal results. So how can we explain it? In a very nonscientific description, we may say that optimization is a systemic process of checking many, many combinations of options in order to find the best one to achieve a specific objective. It is done through a mathematical model of the process, depicting the elements that effect the result as variables, and using sophisticated methods to move in a direction that improves the results. The process of optimization begins by creating the model, verifying its validity, identifying the optimization objective as a mathematical function, and then running it to find the optimal solution. Thankfully, the user does not need to fully understand the math of the whole process. The results will speak for themselves.
In most cases optimization is a balancing act, where we try to find an optimal balance between several elements (e.g., customer service versus cost). Optimization takes time. The users have to define the time they are willing to spend on the optimization. The longer time we allow for optimization, better results can be expected. We still need to assess the cost effectiveness in terms of time or cost of the additional benefit. If we describe optimization as travel in an unknown terrain, where we try to find the highest peak or deepest valley, the more time we spend looking for it, the better chance we have of finding it. However, when we get to a very modest slope, the added benefit will not be worth the effort of continuing the search. Optimization process may lock on a peak lower than the tallest one (i.e., suboptimization). The ability to avoid suboptimization is a difference between a better process and others. Choosing the right optimizer is definitely the right time for some expert consulting. Examples and opportunities
for optimization are ample: from transportation routing, to material utilization; from strategic decisions on how many plants, distribution centers we should have (and where they should be located) to detailed scheduling of a workstation on the shop floor.
Another characteristic of APS is its ability to simulate quickly and with flexibility. The ERP process assumes everything is known: requirements, resources, lead times, and capacities. It processes them into a plan of execution. Simulation lets us try different scenarios, make contingency plans, offline, without changes to the plan. It prevents others from taking actions like generation of orders. The ability to separate the formal plan, which everyone in the enterprise is committed to, from other, noncommittal attempts of checking and testing other options goes way beyond the classical question of "what if?" ATP and optimization are forms of simulation. Collaborative forecast is a form of simulation. The ability to simulate became practical and affordable with the development of memory-resident processing technology. It allows us to quickly duplicate a situation, make assumptions, test the results and assess them, providing us with a real-time decision support. It also allows us to build our data gradually, and reap some early benefits before it is complete or perfect.
To Be Continued
For balance of this article, click on the below link:
Need help in bringing this training to your company, may I
suggest that you forward this Web page to your leader. If you do,
we'll send you our Power-Point presentation, "7-Rules for Surviving in an Entirely New Economy."
To open the
"Forward to" form:
To stay current on Lean Management Basics and
Best Practices, subscribe to our weekly MBBP Bulletin... and we'll send you
presentation, "Introduction to Kaizen Based Lean Manufacturing™." All at no cost of course.
personal information will never
be disclosed to any third party.
what one of our 13,000 plus subscribers
wrote about the MBBP Newsletter:
"Great manufacturing articles. Thanks for the insights. I often share portions of your articles
with my staff and they too enjoy them and fine aspects where they can
integrate points into their individual areas of responsibilities. Thanks
Kerry B. Stephenson. President. KALCO
to Basics" Training for anyone ... anywhere ... anytime
6003 Dassia Way, Oceanside, CA 92056
West Coast: 760-945-5596