Developing a Digital Framework to Optimize Quality Management Processes

In my last article, I discussed the challenges of integrating data from legacy systems and how data mismatches can pose problems for organizations. In this article, I address these challenges through a different lens - and perhaps a more relatable one.

Quality management: A Business-critical Process in Manufacturing

Many manufacturing organizations have a tacitly developed quality management process that is largely person-centric and dependent on the deep knowledge and experience of their quality inspectors. 

However, quality management needs consistency in its output. The quality control process typically consists of best practices to deliver products with zero defects. It is a process where the existing quality parameters of the manufactured product are checked and compared with a predefined quality standard set by the manufacturing unit.

With a zero-tolerance policy towards quality control, manufacturers can reduce wastage. They can course-correct proactively to prevent brand value erosion and other losses resulting from defective products, such as product recalls.

Common QA Challenges Manufacturers Face

Manufacturers must maintain quality standards; failure to do so could result in unhappy customers and spiraling costs on the supply chain via product recalls or returns. Some of the key challenges manufacturers face in quality control are:

Subjectivity in the QA process:  When a human interface is involved in making judgments during quality inspections, a lot depends on the tacit experience of the quality inspector. Capturing inspection data into digital systems is critical to make it less person-centric and more objective. It becomes a reference point for the quality team to finetune and better their inspection processes and bring in process standardization to ensure consistency.

Siloed QA data in disparate formats: As the organization grows and matures, collecting data about observed manufacturing defects is critical for continuous process improvements. Without an automated system, quality inspection data may be spread everywhere - in local Excel sheets or other applications in proprietary and disparate formats. Siloed information must be aggregated into a single database - creating a digital single source of truth. 

Proactive decision-making : Once QA teams have access to clean, consistent, aggregated data for current and historical periods, it matures the quality management practice within the company. Data interoperability between systems and a seamless flow of data (between shifts or teams) fosters a proactive rather than reactive approach to quality improvement.

Quality Management: A Case in Point

One of my clients was concerned that their quality management lacked an automated system for active acquisition and systematic arrangement of integrated multisource data. They wanted to enhance the current inspection practices to eliminate data mismatches across shifts and groups in an effort to improve quality output.

In this company, quality inspection teams are responsible for ensuring and measuring the quality of products at various stages during the fabrication and assembly process. Once the inspection is done, the inspector's notes or observations are manually recorded. These physical inspection data sheets may be prone to loss or misplacement and, in rare instances, even fudging or misrepresentation.

We designed a study to understand the factors affecting the efficiency and consistency of an automated quality management workbench. Our team prepared a set of key questions to be answered as part of this study. To give you an of what we were after, here are some sample questions similar to the ones we used:

1. What was the total time duration of a typical quality inspection?
2. Were there differences in how different quality associates measured the same production output?
3. What type of architecture (IT, OT, and HW) would effectively bring together data from multiple sources and integrate information from various production shifts and quality teams?
4. What would help quality associates focus more on their inspections rather than worry about mundane tasks like bringing together manual data from different production shifts or organizing information manually?
5. What would be the required speed of communication or dissemination of information for an integrated, automated data collection system for QA? 

The SPinteg Solution

Our teams went to work to find solutions to understand what was needed to develop a framework for an automated quality management workbench.

A comprehensive IT architecture was designed using automated, connected devices for quality, creating a data lake and a centralized database for all quality data.

• Data was captured and stored at required regular intervals from multiple sources, leading to organized and consistent data collection.
• Time efficiency and data consistency parameters were stringently recorded, and an optimized process was set for the inspection.
• A system was devised to record and display any manufacturing defects observed during the inspection to decide whether the produced piece must go back for repair or proceed through production.
• Unique identification was designed for each quality associate as well as the batch or piece being inspected. 
• Time stamps for the start and end of inspections were automatically recorded. Supervisory controls were established. 
• A set of reports was implemented, including shift-wise, employee-wise, and station-wise reports on a daily, weekly, and monthly basis.    

The system effectively highlights the following:

1. Thoughtless errors, such as assuming that a particular parameter check is not required
2. Irresponsible errors, such as skipping a step in the defined process
3. Intentional errors, such as violating the rules despite knowing them
4. Time delays that can be addressed in training if an associate displays frequent and repeated delays in completing the inspection steps

SPinteg's system successfully digitized data, brought it together in a data lake, provided an architectural bridge between connected devices and controllers and created easy-to-use interfaces for display and interaction.

Benefits & Value Added

In enhancing the quality management practice, our customer witnessed significant traction in various aspects: 

•  Deploying an integrated data framework benefitted the organization in capturing product quality data and communicating efficiently between production shifts. 
• Data is collated and stored in secured databases from multiple sources, leading to employees leaning on centralized data repositories for a digital track and trace.
• Stakeholders received consistent data from multiple sources, enhancing the quality of the information they received.  
• The quality practice has become more proactive in its actions instead of being reactive on the assembly line.
• The digital platform solution has helped the organization focus on continuous product quality improvement.    

In Sum: 

Modern-day assembly lines carry a heterogeneous mix of equipment: state-of-the-art machinery co-existing with legacy systems. With a diverse set of assets, old and new, manufacturing teams are measured on production targets with KPIs like maximum operating efficiency, defect ratio and more. Interestingly, legacy assets that have aged are often tasked to execute mission-critical tasks on the shop floor, putting enterprises at increased risk. 

 As per a survey at The Manufacturer, almost 74% of companies still rely on legacy systems and spreadsheets, resulting in data that lacks interoperability and may even be out-of-date. The survey showed that 74% of companies believe that using digital tools would be a way forward to mitigate these risks.

As Keith Tilley, CEO, Intoware put it: "A reliance on siloed data severely hinders business operations with accountability and visibility issues, as each department has their own interpretation of data, which is a problem for businesses that are increasingly under pressure to evolve how they manage resources and communicate data insights." 

SPinteg is helping manufacturing companies unlock siloed data and tacitly held information into interoperable digital formats so it can be processed and made available for effective decision-making.

Partner with SPinteg to mitigate the operational risks of legacy data and reduce hardware dependencies. 

Our extensive experience on both the hardware and software side of the business allows us to provide architectural inputs and deploy integrated solutions to address critical pain points for manufacturers.

We use a unique combination of technology levers like IIoT devices, including handhelds, scanners and more, with computing devices that help aggregate the information to be re-purposed for improving business process efficiencies.

SPInteg is your go-to partner with rich vertical-driven experience in the manufacturing sector. 

Do you need help with siloed data or standalone legacy systems?

Talk to SPInteg for a free consultation today!