For several decades, manufacturers have driven their continuous improvement and quality management programs based on Lean, Six Sigma or Overall Equipment Effectiveness philosophies.While these provide a solid foundation for operational excellence, a new wave of approaches driven by industry 4.0 'digital' technologies is offering unparalleled improvement opportunities.
‘Digital maturity’ is correlated with competitive advantage in areas such as time-to-market, cost efficiency, product quality, and customer satisfaction. So what does it take to be ‘digitally mature’? Emerson’s analysis of Top Quartile industry performers report provides a good framework to begin. It outlines 5 competencies of digitally mature organisations: automated workflow, decision support, mobility, workforce upskilling, and change management.
We’ve outlined these 5 key competencies below, with reference to how they can drive improvements for operational performance in manufacturing:
Competency 1: Automated workflow
Many manufacturers run their production work orders, preventative control plan or raw materials workflows by compiling information from a Manufacturing Execution System (MES), Warehouse Management System (WMS) and a number of other information sources and formats such as excel spreadsheets.
Digitally mature manufacturers take this to the next level; they use automated workflows to orchestrate these different systems, people and machines to 'run the show'. These workflows pull information from disparate systems, assign tasks to people, respond to events, and push information back into the systems. Think of a conductor of an orchestra – he doesn’t play any instruments but he essentially instructs dozens of people to play their instruments at one time. That’s what we mean by ‘workflow orchestration’.
Automated workflows equip employees with the right data at the right time where routine tasks are automated, and attention is redirected toward more valuable activities like continuous improvement. They also ensure every workflow is executed according the standard operating procedure, every time.
What this means for quality: Different events such as a machine stoppage, or a quality nonconformance event could trigger a different workflow to improve quality and downtime response. e.g. The workflow assigns an operator on a bottling line to take torque gauge samples on bottle screw caps periodically through the work order. If the torque samples fall into tolerance the workflow marks it as a ‘pass’, if one falls out of specification, the workflow would mark it as a ‘nonconforming sample’, and then automatically escalate it to a QA Review. The QA technician would be notified, and they would decide on an appropriate resolution. This stage leads into decision support – where by the workflow can make decisions for you based on data available.
What this means for operations: Workflows can improve operational performance in many areas e.g. if a machine stops, the workflow can create a user task prompting a user to enter a downtime reason as well as raise a maintenance request to fix a mechanical issue. Workflow automation can integrate with and orchestrate existing downtime systems and maintenance systems. This workflow arrangement prescribes user tasks, instead of relying on users to monitor systems and machines and manually intervene, which results in time savings
Competency 2: Decision support
Digitally transformed organisations will usually leverage at least one of two types of ‘decision support’:
1) Automated decision support
2) Expert 3rd party decision support
We’ll briefly cover each:
1. Automated decision support is where decision support is embedded into the workflow so that decisions can be made in an automated fashion. The workflow could contain a decision matrix that is able to make decisions based on data and decide what actions it should take, what needs human intervention and what doesn’t.
2. Expert 3rd party decision support can take multiple forms. It could be an external consultant working on a project, an embedded expert within the business, a remote expert who can access your data when required to lend advice, or internal highly skilled domain specialists who have analytical skillsets to be able to make sense of data. Essentially these people should be able to make the right choices using the information made visible by integrated datasets, real-time operational data and/or quality data.
What this means for quality: In the previous example where a nonconforming sample was escalated, normally a QA technician would be notified and be required to make a decision on how to resolve the nonconformance. A decision matrix can be used here if there are business rules that allow the sample tolerance to be widened based upon the product export market for example, or if the machine needs to be calibrated by a maintenance technician.
What this means for operations: When business rules are automated using a decision matrix operators will spend less time contacting team leaders, QA, and maintenance technicians to make decisions around manufacturing exceptions. This frees them up to spend more time producing product and performing continuous improvement tasks.
Competency 3: Mobile and remote accessibility
The next logical step of automated processes and decision making is in access. That is, how you and your staff interact with physical and IT/software systems to do their jobs.
Access can be divided up into two unique areas:
1. Mobile connectivity 'the connected worker'
Mobile technologies are being increasingly integrated into solutions including OEE systems, paperless quality management systems, and maintenance management systems. This capability allows people to move freely around the factory floor and focus on other core tasks such as continuous improvement, tending to exceptions by notification rather than monitoring systems and machines.
2. Remote accessibility 'visibility for management, anywhere, anytime'
Remote accessibility here refers to the ability for managers to review metrics remotely, in real-time. It also refers to other's being able to remote into your system to 'see what you see'. This level of access and mobility also enables manufacturers to better-leverage domain specialists from around the world to gain expert insights into system alerts in real time, without the need to be onsite. In addition, virtual reality and digital service support apps are becoming more prevalent for manufacturers looking to get fast support for their systems and equipment.
What this means for quality: First, the ability to scan barcodes of raw materials for track and trace, take photos of labels for record keeping, and enter quality check information on-the-spot increases the speed and accuracy of inputs for overall quality compliance. Secondly, mobile apps allow faster response to nonconformance events; for example a QA technician could be at lunch, see the nonconformance alert, assess it and expand tolerances on the spot using the mobile app versus walking to the floor to review.
What this means for operations: Remote accessibility and tools like augmented reality or even video calling enables remote support technicians to get manufacturers up online faster to minimise downtime and improve maintenance practice.
Competency 4: Workforce upskilling
All this automation brings efficiencies to production, engineering, maintenance and quality processes to name a few. This change naturally has implications for resourcing and skills requirements. As such, every digital transformation initiative should be centered on workforce training and change management.
Employees
While there is merit in the idea that your current workforce will need upskilling in mobile technologies or new software systems, a large proportion of training will need to focus on building employees' manufacturing skill sets. Mobile phones and apps are native to people because they are ubiquitous in our daily lives. As such we don't usually see much resistance from employees when we introduce them to the workplace. The real challenge lies in upskilling operators to perform skilled tasks such as continuous improvement, preventative maintenance tasks and in understanding how the whole line operates, not just their one isolated machine.
Managers
For managers using automated workflows, the skills shift lies in refocusing their 'managing' efforts (previously focused on asset management) towards deeply managing their workforce and the process or workflow itself. Automated workflows come with dashboards that display the workflow, and how it is performing through heatmaps and graphs that might show bottle necks, times, output etc. As such managers will need to be skilled in data analysis and subsequent decision-making to optimise workflows.
In affect, digital technologies like mobile phones and automated workflows are removing repetition and wasted time, then giving individuals more responsibility to keep systems online, make improvements based on data and problem solve when issues arise.
What this means for quality: Operators could be skilled in responding to nonconformance escalations, (normally flagged with a QA officer) with help from the workflow's embedded decision support.
What this means for operations: Managers have the opportunity to use workflow data dashboards to evaluate process efficiency and increase adherence between agreed process and execution through optimising the workflow.
Competency 5: Effective change management
Though workforce training plays an important role for digitally transformed organisations, change management as a whole should not be overlooked.
“43 percent of 4,500 CIOs surveyed for the 2017 Harvey Nash/KPMG CIO survey cited resistance to change as the top impediment to a successful digital strategy.” –CIO Magazine
Effective change management includes consistent stakeholder engagement throughout the project, cross-department collaboration, and seeking input from people who will ultimately be running the new technologies or processes - they'll have the best ideas, and their uptake of the change will make or break the project.
What this means for quality and operations: Effective change management can result in 100% uptake of new systems and processes by staff as experienced by Graincorp in their new warehousing and automated guided vehicle monitoring system
Conclusion
Quality control and operational uptime are not new problems, but industry 4.0 technologies such as automated workflows and decision support offer manufacturers a new way to solve them. As more and more data flows in from different systems and devices, manufacturers will do well to harness automated workflows to orchestrate people, systems, machines and process, and take smart action from this data.
Integrating your disparate data systems from your MES to warehousing system to business finance management systems is a foundational step in any digitisation initiative. It will set you up to be able to scale other business-wide improvements such as workflow automation. Read our free guide to integrating IT and OT systems to get started:
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