Your quality strategy shouldn’t be limited to how your organization reacts to quality issues. Predictive quality is the key to achieving consistent results and positioning yourself as a reliable product or service provider. You can switch from reactive quality to predictive quality by learning how to get the most out of your quality control analytics.
Centralize your data
You can’t get foresight into quality issues if you don’t have good visibility over quality-related information. Building a centralized database will allow you to mine your data and generate predictive analytics regarding quality issues. Your quality database should include data from call centers, warranty claims, and from the tests performed throughout the manufacturing process. Focus on quantitative analysis since you will need to integrate information from customers’ feedback and will also need to work with information that categorizes the type of quality issues customers have encountered. Create a system that will automatically extract relevant data and enter it into your new database without any manual input so you can maintain optimal visibility over quality. Find a solution that allows you to gather data from your suppliers as well, since the quality issues they encounter could be indicative of issues you will run into further down the line.
Mine your data
You can build a new system geared toward predictive quality once you have established relationships between existing data sets. You can achieve this by mining your data. This process might reveal that higher production volumes increase the risks for a specific type of flaw, and another type of flaw could indicate an upcoming equipment failure when it is recurring. Data mining is a complex process. You will have to determine which factors are linked to quality, and might need to secure new expertise for this process. You will also need to settle on a data-mining model that makes sense for your organization. Recursive partitioning, also known as the “tree method,” is a common approach since it allows you to work with if/then relationships between data sets.
Shift your focus toward predictive quality
The data-mining process should help you identify the factors you need to monitor to predict quality issues before they affect production. You can then update your quality management strategy to be more reactive. Customize your quality management system to send out alerts to specific people when certain factors are detected, or automate some responses, such as scheduling equipment maintenance when specific flaws that have been connected to equipment failure in the past are detected. You can also use the outcome of the data-mining process to reassess risks. You might need to rethink some steps of your production process if data mining reveals higher risks than you had previously assessed. Analytics are an important aspect of predictive quality, but you shouldn’t ignore the human factor of the equation. Develop a company culture that encourages everyone in your organization to take ownership of quality standards and to report any issues they notice. Production line employees could catch flaws before your quality control system does. Test your preparedness. You can randomly introduce problems or run hypothetical scenarios to assess how efficient your quality management strategy really is. This preventive approach will help you build a better-performing quality management strategy and make sure everyone is aware of their role when an actual quality problem arises. Having a clear framework in place to react to quality issues is only half the battle. You can leverage the power of your software-based quality management system to develop a new strategy that focuses on predictive quality to catch problems as early as possible, and, if possible, before they even arise.
