In my last post, we began our discussion on the various types of constraints that exist within many manufacturing facilities. We also presented the five primary types of constraints that exist within any management system:
- Physical/resource constraints
- Market constraints
- Policy constraints
- Dummy constraints
- Material constraints
We ended the post with a discussion of the basics of physical/resource constraints and how to manage them.
In today’s post, we will continue our discussion on the remaining four types of constraints and how to deal with them.
The basic definition of a market constraint is one that exists where the market demand is less than the output capacity of each resource thus preventing the organization from achieving its stated goal. The figure below is an example of a market constraint. In this example, the demand is 25 parts per day and as can be seen, all four steps in this process are greater than the demand placed on it (i.e. demand is 25 parts/day).
There is one simple test we can use to validate whether or not a market constraint is real. If market demand were to increase, will it increase system throughput? If the answer is yes, then the market is truly a constraint. The fact is, because of global competition, most manufacturing organizations today face the situation where the market is the constraint. So what is the best way to manage this type of constraint?
In previous posts, I have written extensively about something referred to as a mafia offer. A mafia offer is an offer the customer cannot refuse. Simply put, the mafia offer is constructed by focusing your efforts on strengthening your company’s competitive edge factors. Factors like quoted lead times, superior quality, on-time deliveries complete with contract penalties for being late, etc. are classic examples of competitive edge factors.
In my last post, we covered the basics of physical/resource constraints, which are quite common in many manufacturing facilities. It’s relatively easy to locate these types of constraints because they are typically found where backlogs of work-in-process (WIP) are high. They can even be calculated based upon the demand requirements compared to the capacity of the supposed constraint. In reality, however, the most common constraints to improved performance are not physical at all. Many times the physical constraint is the direct result of an existing policy.
Policy constraints are defined as any policy adopted by a company that limits the performance of the organization with respect to achieving its stated goal. Typical examples of policy constraints include the following:
- The use of “local” performance measures such as manpower efficiency or equipment utilization in non-constraints, driving up the WIP inventory level
- A policy of running large batches of product in order to achieve a lower cost-per-unit and/or higher resource efficiency can result in higher levels of WIP, increased lead times, late deliveries, worsening customer service, continual rescheduling, changing priorities and increased overtime
- The policy of no overtime, even on the constraint process, reducing the company’s ability to improve throughput and profits
- Across-the-board manpower cuts that reduce the organization’s ability to satisfy market demand
- Using a fixed profit margin approach to set the selling price of products, which many times leads to lost throughput.
In order to determine if you have a policy constraint in place, run a simulation study to determine what would happen to throughput if the specific policy were not in place. If throughput would increase, then you have a policy constraint, which must be removed.
A dummy constraint is one that causes throughput to be constrained because of a resource whose cost is minimal. Typical examples of dummy constraints are a shortage of phone lines, fax machines, etc. that inhibit the smooth flow of product through a process. Dummy constraints are relatively inexpensive resources compared to the potential for the increased throughput that would be realized if they were removed. The simple test to determine if you have dummy constraints is to check to see if throughput would increase if the dummy constraint did not exist. Typically, throughput increases more than cover the cost of dummy constraints.
One example of a dummy constraint is what happens during employee breaks and lunches. Typically, all employees go on breaks at the same time, which results in the system constraint being idle. When the constraint is idle, valuable time is lost with respect to throughput enhancement. A simple way to remove this constraint, is to stagger lunches and breaks so not everyone is “idle” at the same time. Without spending any money at all, this dummy constraint could be resolved. One could argue that this example is a policy constraint, but in my opinion, it falls into the dummy constraint category. Essentially dummy constraints are what we refer to as no-brainers in terms of resolution. That is, spending little or no money results in potentially huge gains in throughput and profit margins.
It should be obvious that if you don’t have the input material needed to produce parts, your manufacturing process will shut down. As a result, numerous material control systems have been developed, most of which are based on forecasts. The only problem—much of the time the forecasts have been wrong. As a result, many times manufacturing facilities will over-order materials to avoid material shortages. The funny thing is companies still have stock-outs of parts requiring them to expedite materials or component parts to satisfy their needs.
A material constraint happens when the availability of material is less than the amount required to maintain the flow through the operation to satisfy market demand for the manufactured finished product. For example, if the demand for your product requires 100 units of a raw material per week, but you can only purchase 75 units from your supplier, then this material would be considered a material constraint. When you have shortages, you miss your throughput goal.
Another example is purchasing a lot of raw material and discovering that it is defective somewhere within the manufacturing process. The process will be forced to shut down until a new lot of raw material can be obtained. Depending upon how many in-process units were impacted by the defective material, the results could be very serious since all of these in-process units will typically be scrapped. Material shortages may also be the result of poor scheduling of the flow of product or even sudden surges in demand from your customers. Finally, material shortages might be the result of “stealing.” This occurs when material designated for one order is diverted to a different order to satisfy demand for that product.
One way to avoid or reduce the frequency of material constraints is the implementation of the Theory of Constraint (TOC) replenishment solution, which I have written about in previous posts. The basis for this material procurement solution is to replenish what’s been used on a frequent basis, rather than trying to forecast and then purchase what the forecast tells you is needed. The TOC replenishment solution results in much less on-hand inventory (i.e. 40-50 percent) while virtually eliminating stock-outs.
In my next post, we will begin a new discussion related to continuous improvement as it applies to the tools you can use to identify the constraint(s) in your system. As always, if you have any questions or comments about any of my posts, leave a message and I will respond.
Until next time.
 L. Srikanth and Michael Umble, Synchronous Management—Profit-Based Manufacturing for the 21st Century, Volume One, 1997, The Spectrum Publishing Company, Wallingford, CT
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