Quality’s continuing conversation with Jason Chester, Director of Global Channel Programs for InfinityQS, on SPC and the smart factory. This is the final part of the three-part series of our conversation.
Quality: Other than security, are there any other challenges or risks to a connected shop floor or remote monitoring?
Chester: Security risks are up there at the top of the list of challenges, but there are also many others. Some of the more significant challenges relate to the volumes of data generated by these increasingly connected environments. As previously discussed, data can proliferate exponentially, and (if not approached strategically) this deluge of data can result in data overload, which can greatly increase cost and complexity of operations. Where data and analysis are used by human operators, too much data, and data of dubious quality or reliability, can lead to conflicting insights or lead to what I call “decision paralysis.”
There can also be significant workforce challenges to overcome. Manufacturers that are transitioning from a traditional operating environment to a more connected digital environment will face significant training and reskilling of their workforce, and a need to attract and onboard new and more relevant talent who easily adapt and support a digital environment. As many more manufacturers begin to tool-up for this digital future, the competition for talent will become even more intense, and critical to the success of these transformations.
Quality: What would you say to industries that cite data quality as a hindrance to big data analytics or for industries that need to improve data quality to make SPC work?
Chester: Data quality is a major challenge—and it’s an issue that is all too often overlooked or assigned a low priority. The issue goes beyond just data quality (which relates to the accuracy, currency, and precision of data), but also data integrity (which relates to how data maintains its conformity to rules and constraints). It is a topic that has challenged businesses for many years (ask any CIO) and one with which organizations struggle to this day. The impact of data quality cannot be overlooked because the impact on decision making, planning, and strategy formulation could clearly dwarf any direct operational impact.
Too many manufacturers are caught up in a cycle of managing the downstream impact of data quality with disproportionate resources when compared to implementing a proactive, ongoing data quality strategy. In short, data quality has to start before the physical data actually exists—prevention is better than cure. These challenges are intensified in the real-time environment of a smart factory. The era of batch data processing is over. Today, data is acquired or created, and consumed, almost instantly, 24x7, without the possibility of review, clean-up, and preparation before use.
In this regard, SPC is no different to any other operations or business technology that relies on the quality and integrity of data as input. The quality of intelligence obtained through SPC will be more accurate and more reliable if the quality and integrity of the incoming data is increased.
Quality: What would you say to industries that say things like subject matter expertise, etc., are more important than SPC?
Chester: SPC provides evidence of real-time process performance and trends based on operational and quality data. To an operator, SPC provides the valuable insight into how a process is performing, and alerts them to abnormal trends and variability that required intervention or preventive action, or opportunities to reduce waste (across man, material, and machine). To a supervisor or quality manager, SPC provides greater insights of performance across multiple parts, processes, and features, enabling them to monitor performance over wider fields of view and across longer timescales. To a continuous improvement professional, SPC provides insights that enable them to pinpoint where performance bottlenecks or quality risks are more prevalent. This allows them to focus their efforts more effectively and monitor the results of implemented changes. A senior manager or executive can use the KPIs provided by SPC to monitor operational performance, capability, and yield across multiple lines, plants, or regions. The common thread across all of these scenarios is that the evidence-based data provided by SPC are a critical component to the ability to use that insight to make informed decisions about improvements and corrective actions. For an operator, quality manager, continuous improvement professional, or senior manager, the application of subject matter expertise to that insight is fundamental to making the right decisions at the right time, based on the evidence-based SPC data and insights.
Likewise, a subject matter expert who does not have access to reliable data and insights will, in effect, be applying their expertise blindly and with a limited ability to judge the efficacy of their decisions. So, SPC is arguably no more or less important than subject matter expertise, but the two are complementary and equally important.
For more information, visit www.infinityqs.com.