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ATD AS AN ADDED DIMENSION

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.

OPTIMIZATION

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.

SIMULATION

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:

Lean Manufacturing Articles and click on Series 11


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