Policy Constraints

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Overview of Policy Constraints

A policy constraint is an extremely common problem that can reduce throughput levels. A policy is a rule that dictates how a system is operated, such as a batch sizing rule that a crate must be filled with work-in-process before being moved to the next downstream work station. The trouble is that such a policy may keep materials from arriving at the downstream work station in a timely manner, which is starved of materials until the appropriate delivery crate is filled, and is then flooded with work when the crate arrives. Spotting the policy constraint in this example is relatively simple, because it results in downstream operations being alternatively flooded with materials or starved. This boom-famine cycle occurs because inventory builds up at an upstream workstation until a sufficient quantity has been completed to meet the policy guideline, triggering delivery of a large quantity to the downstream workstation.

Conversely, if an operation is continually starved of materials (but never flooded), then the constraint is likely to be caused by an upstream work center with inadequate capacity, rather than a policy.

Types of Policy Constraints

There are a number of other common policy constraints. When they are mentioned in the following paragraphs, the name of each policy constrain will be underlined for easy reference. For example, a negotiated break rule that allows all machine operators a half-hour break period is a constraint when this means that no one is operating the constrained resource during that half-hour period. In this case, the problem caused by the policy is obvious, but the solution may require painful labor negotiations to achieve. This problem arises for all types of work rules, which are frequently imposed by union agreements.

Another policy causing a constraint is the requirement to always have production runs that do not drop below a set minimum level. An excessively long production run creates too much inventory and also uses up valuable time at the constrained resource; thus, shorter production runs that only match immediate customer requirements are to be encouraged. This policy is usually engendered by a cost accounting analysis which points out that the cost of an expensive equipment setup can be reduced if spread over the cost of a great many units of production. However, since most work centers have excess capacity, the time required to make extra equipment setups for shorter production runs is actually free. This type of policy is discovered by investigating whether the scheduled amount of a production run matches demand, or if an excessive quantity has been scheduled by the production planner. Another form of evidence is the presence of economic lot sizing rules where the computer recommends a lot size, rather than using the amount of actual customer orders. A secondary investigative approach is to review recent additions of finished goods to the warehouse, and determine if they were added because of excess production.

Yet another policy constraint is overtime avoidance. Plant managers are frequently judged on their ability to keep employee overtime levels to a minimum in order to reduce labor expenses. However, when the incurrence of overtime can keep the constrained resource operational, the resulting increased throughput should easily outweigh any overtime costs. This constraint can be spotted by investigating the reasons for downtime at the constrained resource.

A policy that causes considerable trouble for the constrained resource is the concept of attaining production line balance. Under this concept, the best production process is one where there is just barely a sufficient level of production capacity in all work centers to complete the work listed in the production schedule. This approach assumes that costs can be stripped out of the production process by deliberately limiting capacity levels in all areas. The problem with it is that any production shortfall in any work center will almost certainly limit the production of the constrained resource, and so will reduce throughput. This policy is readily apparent in most cases, because it takes a great deal of deliberate effort to achieve product line balance. A form of indirect evidence of line balancing is when constraints appear to crop up in many places, and will seem to move around the production floor even during a single day.

Policy constraints can even arise in the capital budgeting area, where net present value is the benchmark standard used to calculate the need for new equipment. However, this evaluation technique may result in the rejection of a proposed purchase from which incremental sprint capacity is to be gained, which in turn reduces the amount of potential downtime at the constraint. The proposal may clearly result in more throughput, but the traditional analysis model would not accept it. This policy can be found by investigating rejected capital budget proposals or by reviewing the project acceptance criteria in the capital budgeting procedure.

Perhaps the worst constraint is the general belief that a company must run all of its resources at their maximum levels in order to gain the highest level of efficiency and therefore (supposedly) the highest level of profit. This is not precisely a policy constraint, since it is not always formally enunciated, but is more of a paradigm constraint, where everyone’s underlying view of the production process is that all resources are to be run “flat out.” In reality, only the constrained resource must be run at the highest level of efficiency, while many other resources should operate only when needed. This constraint is most easily spotted by checking for work center efficiency reports for areas that are not constrained resources.

Another bad paradigm constraint is an excessive focus on cost reduction. When managers spend all of their time determining how to squeeze the last nickel out of their operations expenses, it is easy to lose sight of the resulting drop in production capacity that occurs as those expenses are gradually eliminated. Unrestricted cost reduction can eliminate large amounts of capacity, until a company has essentially cut so many expenses that it has run itself out of business. This type of policy is readily apparent when the bonus plan and management reports focus more on expenses than throughput.

Local Optimization

Most of the policy constraints noted here share one bond – they are based on the concept of local optimization. Each one is designed to optimize a specific performance measurement, rather than the throughput of the entire system. For example, banning overtime will reduce labor costs, not paying delivery charges will cut freight costs, and long production runs will cut the average setup cost. However, in each case, they also reduce the total amount of throughput generated. Because of this common underlying problem, it is useful to analyze every production policy and determine if it is based on local optimization. If so, it is probably having a negative impact on throughput, or has the potential for doing so.

Changing a Policy Constraint

Since it takes no investment or new expenses to overturn a policy constraint, there can be a massive payback involved in the adjustment or elimination of selected policies. However, this is rarely a simple task. Employees consider many production policies to be “set in stone,” perhaps because of their training, but also simply due to long usage. This makes it extremely difficult to convince employees that long-cherished policies are causing production problems. Also, if not guarded against, these policies can recur over time, as new employees take over job functions without proper indoctrination in throughput accounting concepts.

Given the number of examples shown here, it is evident that a production process may be rife with policy constraints. Accordingly, you should devote a considerable amount of time to the investigation of all policies that could impact throughput.

Podcast

A discussion of throughput concepts is available on Episodes 43 through 47 of the Accounting Best Practices podcast.