When investigating a statistical process control (SPC) system, it can be difficult to build a business case for it. As a quality engineer, the value in having easy access to data seems obvious, but this can be a hard sell to an organization focused on cost and payback opportunities for capital projects. There are a number of ways an SPC system can be used to drive improvement and reduce costs.
Look in these areas to identify and then make the business case for an SPC system.
Incoming raw materials and supplies. Incoming inspection of raw materials data can be valuable in determining the best suppliers. However, in a paper-based system, the data are difficult to analyze. An SPC system can be used to quickly document the inspections and be available for analysis of the data.
When the data are available for analysis, they can be leveraged with the various raw material suppliers to drive both quality and cost improvements.
Compliance. An SPC system can be used to evaluate and improve compliance to procedures. As with raw material data, an SPC system can help to quickly evaluate compliance to procedures.
An SPC system drives major savings compared to the time it takes to evaluate paper records. Further, the paper, printer, storage, and retrieval costs can effectively be eliminated.
Investigation. As in the case of compliance, investigation of complaints can be much improved with an SPC system. Data can be quickly retrieved to evaluate for root cause and also to determine if other product might be affected.
During a recall, this quick retrieval and analysis of the quality data can be invaluable not only to the products affected by the recall, but also in preventing in-process materials from facing the same issue.
Quality improvement. The data retrieval and analysis that can be used for compliance and investigations can also be used to determine areas of improvement in the factory. These data can be used to break through preconceived ideas about problems and to direct improvements using easily compiled data.
Reductions in scrap, rework, product giveaway, machine setup, product sorting, and inspections are all improvements that affect the bottom line and are precisely the improvements that SPC targets.
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Who could possibly say no to the improvements listed above? Often, the most difficult part of establishing a business case for SPC is quantifying how much money is currently being wasted on issues like those listed above and how much you can expect to gain from implementing a modern SPC system. This quantification will most likely require baselines of your current process, predictions based on past experiences, and predictions based on current production volumes and process knowledge. Examples might include:
• Reducing product giveaway by X percent will result in $Y savings.
• By performing a study on production line one, scrap was reduced by X percent and resulted in $Y savings. Extrapolate these results to other lines if an SPC system is implemented.
• The quality department spends X hours/weeks retrieving and organizing records. By implementing an SPC system, we expect this to be reduced by Y hours/weeks, resulting in $Z savings.
• Consolidating from three suppliers to two suppliers for these materials will give us a volume increase that will allow better pricing.
These examples are general and serve only as a starting point, but they demonstrate the thoughts that every customer who has driven cost down through improved quality have had.
As discussed, a modern SPC system can be used in many ways above and beyond traditional SPC. With a modern system you get all of the benefits of SPC, allowing operators to quickly and efficiently make sound quality decisions about the product that they are running. Add to that, modern SPC allows for instant product and process analysis that was formerly time-consuming with paper-based SPC and other manual data collection systems.
Erik Martinsen is an application engineer at InfinityQS International, a provider of statistical process control (SPC) software and services. Martinsen works on site with customers to provide customized implementations of InfinityQS products, and he leads fundamental training and follow-up consultations. Martinsen worked in the packaging industry for 12 years as a quality manager, where he also implemented, supported, and maintained InfinityQS software in 10 plants. Martinsen has a bachelor’s degree in mechanical engineering from Georgia Tech. He is a lean Six Sigma Black Belt and an ASQ-certified quality engineer.
