The #MondayMusings blog series provides executive level insights and analysis for the Industrial Internet of Things (IIoT) and Digital Transformation from the previous week’s briefings, events, and publications @LNSResearch.
Demystifying Smart Manufacturing
Andrew Hughes and I had the opportunity to attend the MESA 2016 conference in Chicago this past week, co-located with the Industry Week Manufacturing Technology.
As always, MESA provided an opportunity to share and exchange ideas in an open forum, and LNS Research was lucky enough to co-host two of the unconference sessions.
Metrics that Matter
The first session was presented by Andrew Hughes and John Jackiw from Alta Via Consulting who shared some of the most interesting results from our Metrics that Matter research study. Two of the results Andrew presented that triggered the most discussion (or were most “controversial”) were on the impacts that Cloud technology will have on MES and the benefits of taking a lightweight more modular approach to Manufacturing Execution System (MES).
- Cloud: From the Metrics that Matter research it was clear that Cloud will increasingly be in the mix as part of (or even the entire) delivery model for MES software. This played out in the research, but even more so in the participation of 42Q and Plex customers in the sessions. Plex had some of the happiest and most vocal customers in the session. 42Q announced at the event the first ever entirely cloud based MES for high speed, complex, and regulated manufacturing processes.
- Lightweight Apps for MES: From the Metrics that Matter research it was also clear that over the years the adoption rate of MES has largely stayed the same (~20%) even though there are consistently high percentages of companies in the planning stages of adoption (~15%). LNS Research believes much of this is because the industry still takes a big bang project based approach. By taking a platform and app approach, manufacturing companies can take a measured, incremental, and layered approach to moving from legacy systems that likely include both homegrown and existing packaged software to a next-gen software solution.
The second session was hosted by myself and Mike Hitmar from SAS. In our discussions regarding analytics there were a number of interesting discussions that were triggered. My biggest takeaway was that many manufacturing organizations feel they have a good handle of analytics. However, most manufacturing organizations mainly define analytics as metrics and KPIs.
Metrics and KPIs are only one type of analytics – descriptive. For manufacturing companies to effectively move from descriptive to other more advanced types of analytics like predictive and prescriptive, a number of actions will have to be taken.
Manufacturers will have to change the language and training used. Today we talk about metrics, KPIs, process improvement, and optimization. There is a whole world of data science and analytics tools that have not yet been applied to manufacturing. We need to operationalize and program these Big Data Analytics tools to make them work in manufacturing. The next evolution of Lean and Six Sigma need to build these tools in.
Luckily, companies like GE and Toyota (some of the original innovators Lean Six Sigma, MESA participants, and LNS Clients) are working on using the Internet of Things (IoT) and Big Data Analytics to transform manufacturing processes and training programs.
Defining Quality at a Global Food and Beverage Company
Quality Management was the first research practice launched at LNS almost 5 years ago. There is an important reason for this, quality touches every aspect of a company’s operations and corporate strategy.
However, 5 years into our research, many manufacturing companies still struggle to define quality. Maybe a new survey question could be as follows:
How do you define quality?
- Customer Experience
- Manufacturing Excellence
- Corporate Value
- Market Differentiation
- All of the Above
Although my suspicion is the majority would chose g), the challenge most companies still face is that knowing this definition doesn’t make dealing with quality any easier. It really means lots of different roles have different definitions. Which, in turn, means we as quality professionals have a communication and branding problem.
Dan Jacob LNS Enterprise Quality Management Software (EQMS) Research Analyst, recently broached this subject during a discussion about executive perception of Quality with one of our Quality Global Executive Council (GEC) members this week. Her company was executing two Quality projects – a new SPC system and a new EQMS system. The SPC project was perceived by Executives as Operations rather than Quality. It was perceived as operationally critical, and received Executive sponsorship and direct oversight. On the other hand, the EQMS project was funded as a Quality project, was viewed as Departmental, and was not championed by executives.
While executives at this company are investing in Quality, there is not a consolidated view of Quality and Quality Management. Therefore, each project is independent, and some are seen as lower value compliance activities, while others are seen as business-critical with operational impact. Our GEC colleague identified that the business case for EQMS was the root cause. Ideally, they would have used Cost of Quality to justify the EQMS purchase, but without an evidential connection the Business Case was tied to departmental improvements.
If you’re an organization hoping to make a better business case for quality, and wondering how the Business Case directly impacts executive commitment click here.
Access this NEW eBook, "Manufacturing Metrics in an IoT World: Measuring the Progress of the Industrial Internet of Things," presents results from the fourth iteration of the biennial Metrics that Matter research study conducted between LNS Research and MESA International. It places particular focus on what IIoT means to manufacturers in the MOM space.