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Quality Insider

Data, Dashboards, and Decisions

Generating quality results

Published: Wednesday, September 9, 2015 - 23:00

Sponsored Content

Manufacturing organizations across multiple industries are continually pressured to balance maintaining superior product quality with reducing costs and maintaining margins. Fortunately, the technology needed to deal with these challenges is available.

Enterprise quality management software (EQMS) solutions have already proven themselves very effective for manufacturers. By optimizing and automating quality processes, an EQMS gives the right people visibility into relevant tasks and helps ensure they perform required tasks within allocated times, leading to reduced cycle times, and reduced wasted material and resources. In addition, organizations can also reduce costs by tightening cycle times, removing unnecessary manual tasks and optimizing processes.

A large amount of quality event data is captured and stored in an EQMS. Beyond just querying information about a specific quality event, what additional information can be derived? Can we quantify the reduced cycle time or identify areas for improvement? Is there a way to provide our procurement specialists with up-to-date supplier scorecards for our vendors to help ensure product quality?

The data stored in an EQMS—or any type of database—can help us answer these and more questions when transformed, aggregated, correlated, and visualized in leveraging business intelligence solutions. However, although the adoption of business intelligence solutions and tools has greatly increased in recent years, these initiatives can be mired down quickly with the deluge of available data, leading to the inevitable “analysis paralysis.”

So where do we start?

Although we titled this article, “Data, Dashboards, and Decisions,” our journey will actually be the reverse.

Decisions, decisions

Let’s start with a simple question:

“What do I want to learn from my quality data?”

At a basic level, quality data can provide details such as average cycle time for a quality event, number of complaints by product or location, and perhaps the number of quality issues from suppliers or vendors. Informative to be sure, but what else can we learn from our quality data? This is when business intelligence solutions play a critical role.

When the topic of business intelligence and analytics is introduced, you’ll often hear how these solutions can help achieve “actionable insights” or identify “critical business decisions.” True. But it’s important to clearly define the insights and decisions the organization is seeking.

For example, the quality data captured within an EQMS may provide the average cycle time for a quality event. How can we use that data to reduce those cycle times? Can the data tell us the location of the bottlenecks within the quality workflow? If we can identify where delays occur, we can initiate a plan to potentially eliminate those delays and reduce the average cycle time. Actionable insight, indeed.

Dashboards: telling the story

Once we have defined the insights and decisions the organization is seeking, the quality dashboard needs to effectively “tell the story” so that the necessary actions and decisions can be made.

In an automobile, the dashboard shows the critical measures the driver needs using visuals such as dials and gauges—speed, amount of fuel available, engine temperature, etc. At a glance, the driver can understand how the vehicle is performing and if a critical issue should be addressed, such as a low fuel level.

The quality dashboard should operate in a similar fashion. It should provide a visually compelling assessment of the organization’s quality performance, providing the business user the critical measures needed to perform his or her duties.

This leads us to another aspect to effectively telling the story—understanding the audience. By understanding the applicable job roles within the organizations, you can empower each role with the appropriate dashboard information. For example:

Quality executives want to:
• Make data-driven decisions to address the cost of poor quality and improve overall quality management
• Increase the visibility of quality activities to measure key performance indicators (KPIs) across the organization
• Identify emerging trends and implement corrective actions before a trend becomes a costly problem

Quality managers want to:
• Ensure team efficiency by exposing issues early so they can be quickly addressed and resolved
• Have views of process/event status in near real time to manage open team tasks and issues
• Understand quality performance information specifically by region, site, or product

Quality data consumers want to:
• Highlight what requires immediate attention to facilitate appropriate prioritization
• Enable day-to-day task management and improve operational reporting

By understanding how each role is affected by quality data, you can create an effective dashboard to ensure success.

Discovering your data

Now that we have identified the decisions and actionable insights, and defined effective dashboards for the applicable roles, we need to ensure that the necessary data can be found. The data is out there, but it needs to be uncovered in a logical way, deciphered for patterns and trends, and made available for dashboards to tell the story.

To maximize the effect of your quality metric data and create effective dashboards, it is essential to bring together your EQMS data with information from other relevant enterprise systems, like enterprise resource planning (ERP), project lifecycle management (PLM), and customer relationship management (CRM) platforms, so that all the data can be seamlessly accessed and analyzed. This data harmonization-and-standardization step is key to ensuring that metric data can be rolled up across the enterprise and where possible, benchmarked.

Once you understand what data are available, you can begin to envision what those data can tell your organization. What quality or system trends and patterns—such as vendor performance levels or bottlenecks within a specific quality process workflow—can your quality data uncover? By exploring these data and testing various hypotheses about relationships and trends, you discover meaningful insights to make those data-driven decisions.

Be proactive

In today’s demanding market, investment in an EQMS to optimize quality processes is not enough. Organizations must be able to leverage their quality system’s data to support a comprehensive quality management strategy and to be proactive in measuring and managing quality across the supply chain. It’s only when you act upon those decisions will you realize the true value of your quality data.

For more on data, dashboards, and decisions, join Jamie Chumas and Dirk Dusharme on Tues., Sept. 15, 2015, at 11 a.m. Pacific for the webinar, “Data, Dashboards, and Decisions: Turning Quality Data into Actionable Insights.


About The Authors

Jamie Chumas’s picture

Jamie Chumas

Jamie Chumas is a senior product manager at Sparta Systems, developer of quality management software used by quality, manufacturing, and regulatory affairs professionals. His responsibilities include defining market and business requirements and ownership of the end-to-end product life cycle. Prior to joining Sparta Systems, Chumas worked as a software engineer, project manager, product manager, and director of enterprise data quality at organizations including at Becton Dickinson, UPS, Saatchi & Saatchi Healthcare, and  more. Chumas has a bachelor of science in electrical engineering from Drexel University.


Jaseem Mahmmdla’s picture

Jaseem Mahmmdla

Jaseem Mahmmdla is a senior product and solutions manager at Sparta Systems, developer of quality management software used by quality, manufacturing, and regulatory affairs professionals. Mahmmdla supports the successful delivery of software solutions to customers, and he ensures customers are getting the maximum value out of new products. Mahmmdla has bachelor degrees in computer science and engineering. He has 15 years experience working with life science and financial organizations as an enterprise software engineer and a technical leader with specialization in business intelligence and analytics.