Suboptimization definition
/What is Suboptimization?
Suboptimization is a reduced level of output that results from an inefficient or ineffective process or system. Suboptimization can also arise from a focus on optimizing a unit of a business rather than the results of the entire business. Avoiding suboptimization is a key goal of throughput analysis, which seeks to manage the key constraint within a business.
Examples of Suboptimization
Here are several examples of suboptimization within a business setting:
Excessive control of office supplies. A company's controller locks down the office supplies cabinet in order to eliminate the theft of supplies. However, it takes so long for employees to have the cabinet unlocked whenever they need supplies that the overall effect on the firm is that employee resources are being wasted, even though the office supplies expense declines.
Purchasing cheap raw materials. A purchasing department decides to buy cheaper raw materials to reduce costs and improve its departmental efficiency. However, this decision leads to lower-quality components, which causes more rejections in the production process, higher return rates from customers that are experiencing product failures, and decreased sales. The net effect for the company is a reduction in its profits, even though the purchasing department is able to report cost savings.
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Suboptimization FAQs
Is suboptimization related to the theory of constraints?
Suboptimization is related to the theory of constraints because both focus on how decisions in one part of a system affect the whole. The theory of constraints emphasizes improving the system’s bottleneck rather than maximizing output in every department. Suboptimization occurs when non-bottleneck areas pursue local goals that do not support the system-wide objective.
How can batch-size decisions create suboptimization in production and distribution?
Large batches may reduce setup costs within one department but create excess inventory, longer lead times, congestion, storage expense, and delayed detection of defects. Small batches may increase handling and transportation costs. Optimal batch sizes should support overall throughput, constraint utilization, customer demand, distribution capacity, and total system performance.