by Merlin Hernandez     

         As small businesses seek to become more effective, they might take a page from the playbook of larger organizations and use business statistics as a vital key to competitive manufacturing processes. Process controls can influence cost savings, product quality, and customer satisfaction by establishing upper and lower limits to defects so that production and delivery schedules work as anticipated.  A Statistical Process Control (SPC) system uses process data to describe optimal manufacturing process to execute a prototype within the context of its environment – the goal is to identify defect points and levels in order to intervene before tolerance violators occur.

The system uses random sampling to detect whether process output is within the pre-selected range for quality. It is meant to optimize the entire process and allow for better control and documentation of processes, and faster interventions. Identifying the relationship between control needs and process capability would invite process improvement to maintain small tolerances and mean variations. Using control charts to calculate the overall fraction defective will offer better cost and revenue projections. Process flow and resource allocation will be more efficient and also allow savings. Standards and specifications will drive better processes for improved quality to have greater process integration, reduce margins of error, and increase customer satisfaction. These can positively impact brand equity, market share objectives, market expansion, and overall profitability.

SPC has been gaining attention in the last few years because of the increasing popularity of more comprehensive quality systems like ISO, QS9000, MSA, and Six Sigma. Manufacturers and service providers in both small and large enterprises are under pressure to improve quality and customer satisfaction, reduce response times, increase productivity, and reduce costs in order to remain competitive. Two major impediments to bringing quality improvement to many companies are cost and time. In terms of cost effectiveness and implementation time, relatively simple techniques like SPC and EPC can achieve great quality improvement and system cost reduction at a fraction of the cost of more comprehensive systems like Six Sigma or ISO. Lower cost SPC systems have proven to be effective in process improvement and can bring adequate data analysis for managerial/technical corrective actions where financial resources are limited. 

While the Six Sigma system is expensive to implement, a cost-benefit analysis might reveal that these costs, when amortized across the long term benefits of the strategies, can be offset by the sustained level of savings. Six Sigma efficiencies will smooth process problems like bottlenecks, starving, and blocking to eliminate production delays and reduce throughput times thus saving on costs and improve quality. Statistical analysis will identify areas of system vulnerability to defects in order to minimize waste and facilitate savings. But while smaller companies need such a system, they may not have the resources to afford the initial investment and might consider the SPC route to inject better process control. 

The complexity of the Six Sigma system and the method for gathering and analyzing data makes the steps to DMAIC time consuming and tedious, and training is also time-consuming as the system requires an organizational culture that supports implementation which may translate into major structural change. Six Sigma, however, has proven beneficial to large manufacturers like Motorola and Lockheed Martin resulting in across the board savings, streamlined operations, and improved quality. But implementation tended to begin in smaller pockets around these large organizations before incorporation throughout the entire organization. It is a strategy that requires deep pockets and a long term vision where the time to implementation is an asset to the process of organizational change. Many smaller organizations have found it difficult to create employee buy-in as early implementation adjustments can be disorienting and dislocating and bring questions as to the system’s value.

This is compounded by the fact that Six Sigma is more specifically grounded in objective analysis, and application to the unique characteristics of smaller businesses may prove difficult as the knowledge base and experience of employees are not factored. Cost savings are also difficult to determine in the short term and there are questions about the high cost of implementation. The method can also inhibit the creative process as statistical systems and standardization favor quantitative approaches to problem-solving as opposed to the more qualitative requirements of the creative process or a new small business still trying to map its way.

Meanwhile, companies like Toyota, John Deere and Raytheon have been using the Japanese Shingo system which places emphasis on integrating only value-added activities into the production process as they pursue lean manufacturing.  SQC methods like Six Sigma are not structured to prevent defects or eliminate them, they tell us the probability of a defect occurring. The Shingo system seeks to prevent defects from emerging at the end of a process by embedding controls within the process through feedback and corrective action immediately following detection of an error. Feedback is provided through different levels of inspections that facilitate detection. Source Inspections identify errors that can cause defects before the defects occur while Successive and Self-Checks are done for defects themselves. The supporting practice of Poka Yoke requires the stopping of a process as soon as a defect occurs, identifying the source of the defect, and taking proactive measures to prevent recurrence. Smaller  operations may find it advantageous to adopt this method.

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