Earlier this year, McKinsey & Co. published the findings of their latest Industry 4.0 survey in an article titled “How digital manufacturing can escape ‘pilot purgatory’.” The survey contained some interesting findings.
At the opening of the piece, the authors share that nearly 70% of the survey respondents named “Digital Manufacturing” their top manufacturing priority. Not really a surprise since so much energy and effort has been expended on supply chain integration, advanced planning, kaizen, six sigma/lean, TQM, and other operational improvement strategies and tactics through the years.
The article goes on to identify three broad use cases for digital manufacturing initiatives that are equally popular focus areas, as follows:
- Connectivity – solutions that improve and facilitate operational performance, management, and everyday collaboration of employees
- Intelligence – use of analytics, predictive modeling, and cognitive science to gain insight and improve decision making
- Flexible automation – use of new digital equipment to increase efficiency and flexibility in the production system
Again, not surprising. Most of our customers are exploring multi-faceted Industry 4.0 strategies that encompass some or all of these areas. And here at Mariner, we’ve pursued a product development roadmap that supports each of these broad areas.
For us, the most interesting finding of the latest survey was an insight into where most of these global manufacturers are in their Industry 4.0 journey. Almost 30 years after Mark Weiser at Xerox’s PARC envisioned “ubiquitous computing” in our future, and more than a decade since the digital transformation really got underway, many manufacturers struggle to get traction in these initiatives.
The authors go on to share six “success factors” that manufacturers demonstrating “at-scale” impact appear to share. These are:
- Approach the opportunity “bottom-line-value backward” rather than technology forward
- Communicate a clear vision and change story for competitive advantage
- Select a comprehensive technology stack that scales and supports analytics early on
- Select the right technology partners who can build a focused ecosystem
- Secure enterprise-wide sponsorship – don’t treat this as a “one off”
- Invest in skills building to get ahead of the capability gap.
We completely concur with these recommendations. It’s one of the main reason we offer a completely different approach to Industrial IoT implementation than most of our competition. Rather than rip and replace, starting with an expensive platform implementation investment, or treating IIoT as a lab-based pilot undertaking, we start with a true production trial.
What is a production trial? It’s an implementation of the platform and services that will remain the foundation of a full-scale enterprise deployed solution. It’s solving a real-world shop floor issue in production, not with extracted “representative” data (or hypothetical mocked up datasets). And most importantly, its engaging the actual manufacturing stakeholders – production operators, manufacturing engineers, maintenance leads and QA professionals, and plant leadership – to address the opportunity and prove the results. In our experience, technical obstacles can be overcome. Success in these efforts, like we’ve seen in enterprise technology programs that pre-date IoT, are more often determined by so-called soft factors like sponsorship, focus, and change leadership.
So, what have we seen work successfully in these initiatives?
Start with the end in mind. Simple and obvious, right? It is amazing to us how many manufacturers are letting their technology organizations invest millions in infrastructure preparations and pilot learnings without a solid business case or vision for how the effort pays for itself. In our experience, “build it and they will come” plays well in the movies but rarely pays off on the factory floor. It’s a popular myth that successful IoT requires major investment in upgrades in IT and OT infrastructure first before real impact can be realized. The reality is that identifying and focusing on specific issues or opportunities, combined with leveraging a mature, robust third-party (i.e., cloud-based) technology stack, shrinks the capital investment requirements enormously – allowing a modest single line case to yield positive impact and set the stage for a broader rollout across other lines and facilities.
While all improvements offer a mix of qualitative and quantitative benefits, working up front to estimate and build consensus on the quantitative benefits and return on investment (ROI) can really clarify the solution requirements that matter. In our experience, if an ROI case cannot be made the focus may not be clear enough to sustain the implementation. The good news is that when that focus occurs, the result is usually significant and compelling. Here are a few recent examples from our customers:
- A food manufacturer implemented remote monitoring and real-time alerts to cooking processes where a blockage in the product flow could result in batch loss within minutes if undetected. A single incident could result in a full shift of lost production capacity. The customer conservatively estimated a four-month return on investment on better control of single condition. That investment also funded other monitoring improvements throughout the entire manufacturing operation.
- An automotive supplier applied AI/Machine Learning algorithms to an existing industrial visual inspection camera system and slashed false positive rates from 30% to less than 2% within a few months. This customer expects 3-4x ROI in the first year alone.
An industrial equipment producer uses our Spyglass solution to monitor their field service effectiveness. By monitoring the performance of industrial batteries in use at their customers’ locations, they were able to extend service life, increase uptime, delight customers, and realize a 30% ROI annually.
Starting with the end in mind also implies some nuanced consideration of what opportunities to pursue first. The temptation might be to select a “big payoff” opportunity or a “small, easy win.” Part of the consideration should include the breadth and reusability of the solution once confirmed. For example, you might have a single issue that has a large benefit to solving but is related to a unique manufacturing process or machine that only has a single deployment in your ecosystem. Another opportunity might have a more modest impact associated with the single first situation but, once solved, can be replicated dozens or even hundreds of times throughout your manufacturing footprint. The latter may ultimately represent a larger enterprise opportunity, and also offers the advantage of becoming a proven success for new lines and facilities as the rollout commences.
As the McKinsey team points out, applying value-based discipline to the approach and plan at the outset will keep the focus on the right priorities and build momentum from the outset.
Get your best talent involved early. It is a mistake not to engage a multi-disciplinary team at the outset. Your best operators, managers, maintenance personnel, and automation and process control experts are essential to the successful design and validation of IoT solutions. Even small parts of manufacturing systems can involve a wide array of variability and operating conditions that need to be considered. Leveraging this internal expertise early helps also with ownership and adoption as you plan rollout. Encouraging skeptical dialog can ultimately shorten the timeline to robust success, as long as sponsorship is visible and the program goals are clear and well articulated.
Engaged sponsorship. Again, this probably seems obvious but too often the view is that “this is a (fill in the blank) initiative, not a manufacturing initiative”. Ultimately, the executives responsible for running safe, efficient, high quality production need to take personal ownership for the success of these initiatives. Program leadership can be delegated to a capable individual, but the executive sponsor needs to be engaged, visible, and easily accessible to that delegated program leader or progress quickly slows or halts. Executive sponsors need to recognize that this will be a routine part of their workweek, not a once a month “check in”.
Evolution not revolution. At the outset, be clear that learning is a natural part of these undertakings. Conditions change over time and the root cause of performance deficiencies in a production process will change as stakeholders have better tools and data available to manage operations. Like other operations and process improvement practices we are all familiar with, IoT solutions need to be regularly revisited, enhanced and re-validated. It may seem trite to use the “journey not a destination” metaphor, but recognizing that these analytics-based solutions will need ongoing attention and tuning is important to planning your team requirements and roll-out strategy. Since addressing and mitigating variability is often at the heart of manufacturing process improvement, adopting the mindset that new conditions are not “failures” of the IoT solution but rather, an expected part of continuous improvement and learning process.
Drive to active outcomes. Another frequent shortcoming of innovations that feature “better analytics”, “data accessibility”, “communications”, “monitoring”, or other generalized benefits that are often touted with IoT solutions is that they fail to go the critical last mile to timely action by the right decision maker. Don’t settle for IoT capabilities that ingest and manage massive volumes of data and potential insights, or that leave discovery of trends and improvements to historical perspective. Real impact derives from delivering the right actionable information to the right person to do something with at information, in a timely fashion. Encourage your team to constantly ask the question, “Who will be able to do something differently if we solve this correctly?” “What does that individual really need to make that happen?” If better decisions and actions aren’t directly forthcoming, the risk is considerable that results will not be sustainable.
So, is it hard to successfully deploy and scale IoT solutions? Not if you choose the right approach, a proven platform, a reliable technology partner (or partners), and focus on scalable improvements with measurable results. We’ve had success working with both mid-sized and very large manufacturers across many industry verticals. If you are trying to avoid or get out of ‘pilot purgatory’, let’s talk!