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25 May 2020
Quality Transformation with Enact - 1: The Problem of Dysfunctional Data
“Dysfunction” is a term we hear bandied about occasionally. We all know it means “not operating normally or properly.” Usually, we hear this term when someone discusses a quirky family, a sports team that can’t seem to get their act together, or a political party. It is not a term that we often hear when describing quality management challenges or the operational data that we use to manage quality or manufacturing performance. But perhaps we should.
In today’s high-stakes world of manufacturing, you cannot sit quietly by while your competitors make continual efforts to improve; you cannot remain content with your organization’s status quo, or you risk being left behind. The underlying causes of performance and quality challenges in manufacturing today often can be traced back to a single problem—data.
This is the first of a series of blogs discussing the transformative powers of Enact®, InfinityQS’ award-winning, cloud-native Quality Intelligence platform. In this first article, we’ll be discussing “dysfunctional data” and why dysfunctional data holds back your manufacturing operations in many preventable ways.
By the late 90’s the industry realised this wasn’t a viable approach. Tier 1 suppliers, those supplying vehicle manufacturers, or “OEMs” (original equipment manufacturers) in industry parlance, were often supplying manufacturers in different jurisdictions. Tier 2 suppliers—those supplying the Tier 1s—were in an even more complex situation.
Data collection, as colleague Eric Weisbrod, InfinityQS VP of Product Management, says in his blog series, is “an essential part of your daily routine in manufacturing. It’s the engine that drives your quality improvement efforts, whether they are continuous improvement, Six Sigma, Lean, statistical process control (SPC), or anything else. From the data you collect, you can discover things about your operations that you might never know from walking the plant floor.“
So, just what do we mean by dysfunctional data? Dysfunctional data are data that are either incomplete, inconsistent, isolated, or inefficient. If your data suffers from any of these things, then it’s time to take a cold, hard, serious look at how you collect it and what you do with it. To begin our journey from dysfunctional data to actionable intelligence, let’s walk through each of these data challenges one by one.
“Incomplete data” are, quite simply, missing data. In other words, if you do not have access to, or are unable to leverage value from, any source of insight that fully describes your ongoing operational and quality processes, then you have incomplete data.
This may be due to unreliable data collection methods, which lead to gaps in your data. Perhaps you are relying on outdated methods—your operators complete manual checks and then record that data on paper. This can lead to gaps in the data if the checks are not performed when and how they should be. Worse still, this data almost always remains on paper and is not used for further analysis in order to generate operational insights.
It may be that you simply don’t capture data that has intrinsic operational value. These may be process parameters, operational events, or quality characteristics that together provide greater insight into your manufacturing and quality processes.
Having incomplete data means that you may have potentially valuable quality or operational data, but you are unable to leverage it for operational improvement. Either way, the data is “incomplete,” and the big picture of your manufacturing operation is missing some important pieces.
Having inconsistent data means that the data that are collected, or available for collection, are inconsistent across manufacturing operations—this can mean many things, such as variations in naming conventions, measurement units, sample rates, procedures and methods, or calculated metrics. Data might be inconsistent across different dimensions such as processes, shifts, plants, etc.
This makes cross-comparison of your data nearly impossible. If you cannot compare across the various sections/areas of your manufacturing operations, you will never get a “big picture” of how everything is really performing. As Doug Fair, InfinityQS COO, notes in this blog, “more companies need to focus on the big picture of extracting manufacturing intelligence from the quality data they have already collected. It’s not hard. You just need systems that will support shop floor, enterprise-wide data collection and a means of aggregating that data and making it easily consumable and understandable by managers, engineers, and quality professionals.” What he’s talking about here is software-as-a-service (SaaS) or, more precisely, Enact.
Disparate data held in different systems and in different formats such as paper records, spreadsheets, Enterprise Resource Planning/Manufacturing Execution System (ERP/MES) or proprietary legacy systems is what we mean by “isolated” data.
For instance, your data may be inaccessible, or siloed—that is, located either in a remote location, or perhaps in separate systems, or in a proprietary format. Or your data may only exist on paper forms, which means that it's only available if you're physically looking at that piece of paper. No matter how your data is isolated, it’s on its own and cannot be easily compared, contrasted, or analyzed (like it should be) to give up the “golden nuggets” of insight it inevitably contains—information and insights you can use to improve your processes.
Data collection and reporting that comprise a manual process can be resource-intensive and time-consuming—thus, inefficient. Manual data collection can be slow and fraught with errors. Analysis of paper charts can be cumbersome and difficult to collate, cross-reference, and share. Reporting using arcane methods is a time killer—with too many people spending too many hours collecting, analyzing, and presenting information that, quite frankly, a solid quality management software system can deliver instantly.
And, as stated above, it’s surprising to discover how many modern companies are still collecting, analyzing, and reporting on their data with paper and pencil, or unwieldy spreadsheets.
Data that is presented in densely populated tabular reports or spreadsheets often makes interpreting that data difficult and gaining meaningful insight and knowledge from it even more difficult. This adds to the inefficiency of data, in that it takes unnecessary effort and resources to extract meaningful value from it.
The Impact of Dysfunctional Data
Dysfunctional data inevitably leads to three fundamental operational challenges: impaired operational visibility, uninformed decision making, and quality compliance risks.
Impaired Operational Visibility
Because of dysfunctional data, you don’t have the 360-degree view of your manufacturing and quality operations that you need to institute real change and improvement.
Uninformed Decision Making
Due to that lack of insight, you’re either not making the decisions you should be making, or you’re making them without reliable facts or evidence to support them. Or, you may even be unaware of the important decisions that actually do need to be made.
Quality Compliance Risks
When that impaired operational visibility leads to uninformed decision making, then, ultimately, you are exposed to unnecessary risks. These risks may be on a day-to-day operational level impacting on performance, or more high-level strategic risks such as a failure to comply with internal/external quality standards or customer-, government-, or industry-specific requirements. It’s easy to see that dysfunctional data not only exposes manufacturers to greater risk, but also impedes their ability to improve operational performance. But the good news is that the challenge of dysfunctional data is one that can be met head-on. In the next article in this series, we will discuss how manufacturers can move away from dysfunctional data and towards what we call actionable intelligence.
Director of Global Channel Programs
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