MEM helps Sematech member companies make two- to five-year corporate planning decisions for all critical facets of manufacturing:
The way MEM helps make decisions is by finding the best answers to Eight Questions.
Q1 Meet Demand - Minimum Cost
Which products should we manufacture in which facilities to meet demand at minimum cost, without violating capacity?
Q2 Relax Demand - Minimum Cost
If we cannot meet demand with current capacity, which products should we manufacture in which facilities to make the most money, without violating capacity?
Q3 Meet Demand - Add Resources
If we cannot meet demand with current resources, what resources can we add (and when) at the least cost to meet demand, without building new facilities.
Q4 Meet Demand - Add/Expand Facilities
If we cannot meet demand with current four-walls capacity, what new facilities can we add at least cost to meet demand?
Q5 Relax Demand - Add/Expand Facilities
Allowing for expansion and addition of facilities, how can we maximize profit if demand does not have to be met?
Q6 Meet Demand - Minimize Cycle Time
How can we meet demand by minimizing cycle time when facilities and resources are fixed?
Q7 Add Resource - Minimize Cycle Time
With a limited amount of money to spend on resources, how can we add resources to meet demand and minimize cycle time?
Q8 Add/Expand facilities - Minimize Cycle Time
With a limited amount of money to spend on resources and facilities, where and when should we add resources and facilities to meet demand and minimize cycle time? (Ingalls, 1994)
How did we solve these problems? It was a mixed- integer program with different objective functions for each question. The formulas were non-trival, especially considering the multi-period, multi-product, multi-facility, multi-resource nature of the problem. However, it was achievable and its use in industry today shows the benefit it has for the semiconductor industry.
I hope that you are thinking to yourself, "What more could someone want?" When we completed the MEM project, the team members thought the same thing. However, there are a couple of key business issues that this type of supply chain analysis does not handle.
First and foremost is "demand variance" or "forecast error". Due to my experience at Compaq, I have become convinced that no one variable effects the movement of material through a supply chain like demand forecast. Unless you are blessed enough to know your demand for several months or years in advance (like a government contract), the demand forecast is always changing, and sometimes drastically. What does the supply chain do in response? It starts moving material. If the demand forecast is up, the chain tries to produce more product in order to fill inventories up to their proper levels. This can mean overtime expenses, expediting charges, and other charges. If the demand forecast is down, then manufacturing sites go idle, materials already in inventory go obsolete, and costs already in the chain have to be absorbed. Optimization has no way of handling this problem. There have been some attempts, including robust optimization. However, robust optimization has not proven itself to be commercially viable.
The second key business issue is related to making Wall Street happy. The process has several steps, including:
If this is your world, and your job is to be sure that you hit the earnings target, what are you worried about? You are worried about hitting the target. Exceeding it does not buy you much and missing it is catastrophic. If that is your environment, you manage the downside risks of the business. How does optimization help you with this? Frankly, it does not. The reason is that the plan that an optimization gives you may be a good one, but it is wrong. The assumptions that go into the model will not play themselves out over time. The demand will be different, the cost of materials will be different, the supply of key material will be different, everything will be different. In essence, you have optimized a problem that will never exist in reality. And because of the nature of optimization, the optimal answer can change dramatically if there is a slight change in the inputs. A manager must know that a plan is robust, meaning that the variance in the business will not drastically effect the overall answer.
There are three areas where optimization and simulation compete -- scheduling, tactical planning, and strategic planning. These three areas have different advantages and disadvantages when it comes to using simulation.
Scheduling is typically a short time horizon with a limited scope, possibly one plant, at part number level. Resources are typically known and fixed. The demand either fixed, or known to an extent that it could be considered "firm". Variance, though critical, can usually be dealt with on an exception basis. For scheduling applications that can be modeled with optimization techniques, optimization is clearly the better choice. In this case, simulation should be used when optimization cannot be used.
In tactical planning, time horizons are longer, perhaps up to several months in length. The scope is at least regional, and perhaps corporate-wide. The tactical plan is either developed at the part number level or an aggregate level, such as a family of products. Some resources, such as the location of manufacturing facilities, are fixed. Others, including what products are produced in which facilities, could be changed, but that would happen toward the end of the tactical planning horizon. Some capital could be bought and deployed toward the end of the tactical planning horizon. Most other resources are open to adjustment. Certainly, most materials have short enough lead times so that they are ordered during the tactical planning horizon. Labor can be hired, transportation can be procured, etc. But depending on your industry, the demand forecast could be simply a best guess. If the demand forecast is a guess, and you want to be sure that the supply chain will meet the demand without risking high amounts of obsolesence, then simulation is the best choice. If the demand forecast is firm in this time horizon, perhaps an optimization would be the best.
In strategic planning, time horizons are even longer, up to several years in length. The scope corporate-wide. The strategic plan is developed at an aggregate level, perhaps at the level of product divisions or product families. Basically, there are no fixed resources. Manufacturing sites can be opened or closed, any capital can be procured, product deployments are completely open, etc. The demand forecast is certainly a guess at this point. However, decisions with some of the largest costs to a manufacturing operation must be addressed in this time horizon. Primary among these decisions is manufacturing and inventory site locations, which includes the size of the facility, and the basic logistics infrastructure (if it is not already in place). Based on site location, future costs such as labor, taxes and tariffs are set. This is a point where optimization and simulation can both play a role. Because of the level of abstraction at the strategic level, an optimization can be used to help decide the location of new facilities and the closing of others. Based on the output of the optimization, a supply chain simulation can then be used to be sure the supply chain deliver product as expected. The simulation would help set inventory policies based on demand variability and demand risk. The simulation can also give a more realistic capital purchase plan, labor requirement, and a better overall cost estimate.