Quality, performances and maintenance have traditionally overlapped in the industrial world, but with the maturation of smart manufacturing, these disciplines grow increasingly intertwined. And smart business leaders encourage that. Here we chat with one such leader, Parsec Director of Product Advancement Dave Ray, who explains how considering quality, performance and maintenance as a common goal is central to developing a truly smart business strategy.
Q: What is the overlap between the worlds of quality, performance and maintenance in this data-driven era?
A: In the past, each of these worlds had their own systems for managing their processes and activities that were siloed from each other. Data sets were separated, not always shared, and sometimes led to competing goals between different teams. As an example, the quality world would focus entirely on final quality and minimizing wasted materials, while shop floor operations lived in the performance world of maximizing throughput and meeting the production schedule. Everyone wasn’t necessarily rowing in the same direction. These walls are collapsing in the current era where manufacturing data is pooled and centralized. The overlap is now about each of these worlds working from a larger data set and having greater visibility into what is occurring across the board.
Q: What industries/verticals do you find to be excelling in aligning quality, performance and maintenance as part of an Industry 4.0 strategy? What fields are laggards in this respect (and why)?
A: While we see several companies in food & beverage and consumer goods leading the charge here, the divide between those that excel with their alignment activities and those that don't does not seem to be based purely on industry or vertical. Leaders in this arena tend to have a few things in common—fewer demands for regulatory compliance, and higher levels of automation in their manufacturing infrastructure. Regulatory compliance creates inertia in modernizing quality systems for digitalization, as the cost of revalidating these systems isn't trivial. Also, aligning quality with performance or maintenance may subject those areas and their data to potential regulation that companies may wish to avoid. Manual processes or older assets that aren't "data ready" require different types of projects to ready them for Industry 4.0 that, in a vacuum, don't provide direct returns to the business by themselves. Each of these are challenges that laggards struggle to overcome.
Q: How are expectations changing regarding product quality, machine performance and operations maintenance as we work increasingly smarter?
A: It’s all about becoming more proactive rather than reactive. Smarter manufacturing is not just about advancing automation and controls. It comes from having critical, real-time information that drives better decision making. Smarter means seeing the problem before it happens and having the best tools to avoid it or mitigate its impact. The expectation is that we can avoid the “red hour” in each of these areas. That means identifying unacceptable variations in our processes before defects can occur, optimizing PM cycles using real production data so that unplanned breakdowns don’t halt production, and having tools to identify performance issues that aren’t readily apparent to shop floor personnel so that corrective and preventive action plans can be put into place before we have an issue.
Q: Who holds these shifting expectations? Who/what is driving the change in these expectations? Do these increased demands benefit everyone?
A: This shift ultimately permeates the entire enterprise as employees learn more about the advantages and benefits of a digital factory. It helps that digitization is not just limited to the manufacturing world. People live in the information age in many different aspects of their lives, both professional and private. So, there’s less resistance to adopting the changes that come with shift, and the expectations that come with it.
Supply chain management always has their eye on new ways to shrink end-to-end lead times in delivering goods of the highest quality. They understand that real-time, smart solutions are a key enabler to achieving gains in this area. Management at the plant and corporate level also understands that better, proactive decision-making can minimize COPQ, downtimes in production, and excessive maintenance activities that bring savings to a company’s bottom line. Both groups control the budgets that ultimately implement smart solutions that change these perceptions and benefit all groups within the enterprise.
Q: Let's talk modern, smart solutions for optimizing performance and maintenance. What techniques/technologies most excite you?
A: AI and machine learning are very exciting. These use the power of cloud computing to crunch very large data sets into models that can power proactive and preemptive decision making in new ways. Whether they’re used to establish sophisticated digital twins of equipment or more focused models for monitoring critical conditions, AI/ML allows for a more advanced approach to identifying precursors to problems that may be impossible to find through traditional means. The key question for this technology is not what it can do, but what are the best use cases for it in the manufacturing world. There are many different applications for this in terms of performance management and preventive maintenance. As a MOM software vendor, it’s a challenging but rewarding technology to incorporate into our products in a way that offers the best value to our customers.
Q: Where does IoT fit into the conversation? What do you see as the best role or use for smart devices within these solutions?
A: When we talk about smart solutions that utilize the cloud, IoT becomes a key enabler for populating these solutions with data. Many existing sources of data pertaining to quality, maintenance, and the performance of plant operations are not “internet ready.” They either sit on local networks that can’t be exposed to the cloud or can’t be connected in a secure fashion. IoT is purpose built to help bridge this divide using modern communication methods. It can also preprocess and scrub data at the edge before sending the results to a centralized solution, whether it resides on-prem, remote in a data center, or in a private or public cloud platform. Companies struggling with potential costly infrastructure projects to make their factories data-ready should embrace IoT as a more cost-effective means of accomplishing this.
Q: When adopting smart solutions, consideration has to be given to legacy systems still in use. What strategies would you recommend for existing quality, performance, and maintenance systems not ready for the digital era?
A: There’s an initial tendency to want to upgrade or replace these systems with a modern equivalent. Many localized software packages such as LIMS, SPC/SQC or CMMS may not have the connectivity options necessary to push data to smart solutions. The trick is not to replace these systems, but extend their reach with data hub or systems that can pull the existing data from them. Solutions such as MOM software are fantastic information-management platforms that can broker data back and forth, whether it’s with these systems, higher-level packages such as ERP or PLM, or large data lakes that exist in the cloud. The cost of replacement is very hard to justify if it’s only to connect a system with a smart solution. Look for enabling technologies that can make that connection and provide other value-added functionality that may be lacking within the enterprise.
Q: How does proper IT/OT convergence enable better alignment of these areas of focus? Do some elements naturally reside with certain sides…say…maintenance is more of a focus for OT teams while performance is more an IT concern?
A: In a true digital factory, performance, quality and maintenance are not the domain of just IT or OT. They become interconnected disciplines that require IT and OT to unify in new ways. This convergence of IT and OT is critical for a variety of reasons. It opens access to systems and data sources that are typically siloed on an OT network. It allows companies to leverage cloud computing to provide advanced analytics across data from all three areas—performance, quality and maintenance. OT assets require insights from IT systems to further optimize their processes, and IT requires data from OT to help power these insights. In every respect, it allows more stakeholders within a factory's operation to leverage information to make better decisions.