Author – Tim Smith
A week ago, I posed a question to the LinkedIn community, my network and several related manufacturing groups. I have a question to ask. What is your greatest impediment to implementing Industry 4.0?
I got seventeen responses from manufacturers and various vendors providing supportive technologies. Comments came in from several countries. Some interesting observations.
- Given the potential base of responders being 100,000 I am surprised in the small response group. Possibly I will post the question again with additional information.
- Given the question was open ended I received repetitive answers for the most part.
- Given the context of the question, anyone reading the responses could pick up a level of frustration apparent in the responses.
I have categorized the responses. Yes, I had to use some subjective analysis to categorize an open-ended response. In fact, some responses populated several categories. Here are the categories I identified and the resulting score for each.
- Cost 4
- Adversity to change 5
- Internal roadblocks 5
- Complexity 2
- Risk Aversion 4
- Resources 4
- Lack of subject knowledge 6
I will address each of these topics. My commentary is not exhaustive and, in many cases, somewhat subjective. But given 45 years of experience I would hope that the commentaries are reasonably accurate, though not totally complete. I will answer them from highest to lowest in score. There are several studies that exist and reviewing them will add to the evaluation below. In fact in the International Journal of Production Economics, Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective, detailing 15 such barriers. (https://www.sciencedirect.com/science/article/abs/pii/S092552731930372X) notably citing “lack of a digital strategy”, being the most prominent. I saw the overall report slanted toward putting the onus of lack of adoption on underdeveloped national policies and regulations. Surprisingly, many of their 15 barriers fall into the same 7 topical categories listed above. Other documents I reviewed from notable companies such as SAP and ptc were thinly veiled sales pitches. The “Nine challenges of Industry 4.0”, authored by Mustapha Zaouini can be fit into the same topical categories as above. In the whitepaper “Digital Factories 2020, Shaping the future of manufacturing” penned by Dr. Reinhard Geissbauer reflects the German perspective I refer to later in the post. In the whitepaper he cites some interesting facts; Companies expect a payback for digital investments in two to five years. My customers see a IRR of 300% with a payback of 4 months. He cites an efficiency gain of 12% over five years whereas historically my customers have seen 10 to 50% efficiency gains within the first 4 months with reoccurring efficiency gains year over year. He does cite some interesting topics and well worth the read.
Lack of subject knowledge
The category with the highest score is “Lack of subject knowledge”. Historically, this has always been the case where an emerging technology or operational philosophy or fill-in-the-blank-here, fights an uphill battle to educate the industry so that the industry should stop and assess the disruptive change event for it’s value proposition. Barcode technology, RFID, Wireless networking, Additive manufacturing, LED lighting, robotics, OEE, Lean, have taken years, if not decades to become mainstream and be embraced by manufacturing. Millions of dollars are spent year after year to make the case for these and many other technologies which could drive operational efficiencies. But the adoption of a technology in and of itself will not drive the benefits inherent in the new technology unless the philosophy is also adopted. Cost accounting has to give way to throughput accounting, process engineering has to adopt a more holistic approach, IT has to understand that they should be enabling the enterprise for profitability and not hold to a myopic gatekeeper mindset. Management needs to adopt technology to mitigate the loss of knowledge occurring through attrition. We are all responsible, both teacher and student to take time and effort to continue to educate ourselves and others to the great potential which could be driven by and through new and emerging technologies.
Adversity to Change
We are creatures of habit and slaves to routine. From the perspective of work, a prescribed methodology to any operation would dictate a known outcome and mitigate any outliers. In other words, if you do the task in this manner you will get this outcome, so do not deviate. Prescribed operations and processes help to guarantee consistency in outcome. Change and more specifically disruptive change is considered unacceptable to an organization with a working operational model. The adage, “If it ain’t broke, don’t fix it.” is the mantra from the operational gatekeepers. That adversity is not just at the management level but finds its roots all the way down to the shop floor. In fact, I’ve talked to several managers who said that the acceptance of new technology impacting change on the floor is the key to acceptance throughout the organization. On the flip side I have seen processes in place which reduce the floor personnel to automatons in response to employee turn-over, to eliminate knowledge loss. In any case, change disrupts and therefore is avoided.
Internal roadblocks can occur from adversity to change but usually it has its roots in fiefdoms. Long standing dominions within the corporate hierarchy are the status quo in most organizations. Enterprises have grown over time and individuals have been promoted into positions of control and power. Positional leaders do not want their fiefdom eroded in any way. The addition of technologies which make data and the resulting information readily available sidestep the ability of gatekeeping. There are many managers compensated for their quotas and are fearful that if the true efficiency metrics were available that their compensation may be negatively impacted. The case is actually opposite in that when the current state is known, improvement to a future state is possible. The internal roadblocks are always caused by people.
The next topic is related to the “adversity to change” and “internal roadblocks”. It has its own merit not reflected in the other two. Risk management, for the most part is a good thing. Aversion is a response linked to an acceptance level. The dictionary describes aversion is a strong dislike or disinclination. Risk aversion is the behavior of humans, who, when exposed to uncertainty, attempt to lower that uncertainty. The response from individuals experiencing risk aversion is the proverbial dipping the toe in the water response. These are the folks that ask for free trials with no commitment. Small “Proof of Concept” projects which can be stopped and hidden without much effort. They seek to control the ingress of technology with as little disruption as possible, with as little commitment to resources as possible and with a very subjective and ambiguous set of goals. These goals enable them to cancel projects without challenge. People exhibiting this characteristic should be avoided at all cost. It’s not that they do not believe the technology has merit nor that they are unwilling to make a change, they are unwilling to be accountable for less than optimum results. Results that they will determine as they see fit.
This topic, though not a top scoring category is a topic which can make any project a non-starter or guaranteed failure. This “condition” can be defined in both physical and human resources. A technology can pass all the other hurdles and be stopped cold right here, at this point. It results from stakeholders not willing to provide resources or not capable of providing resources. The result is the same. If a new, emerging technology finds a champion in the company, then the ability of finding resources has gone up considerably as long as he can make a case for it. If there is no champion or he cannot articulate the value proposition surrounding the new technology, he will not be able to marshal the resources to effectively assess the technology. Remember that the resources are usually coming from several departments. He must be able to evangelize his peers and the other department to get commitment for resources for the project. Resources are a requirement. Sometimes the toughest part of any technology adoption. Resources are what IT must provide. Resources are what supervision and management must provide. Resources are what the shop floor personnel are. Resources are access to machinery and infrastructure. If you can’t get a commitment to or access to resources then the project is doomed.
Surprisingly, cost is sixth on the list. Most would think that cost would be first or second, but historically, and proved out by the survey, cost is not the mitigating factor in assessing a new technology or philosophy or whatever. Cost will inevitably come into play when a project is tabled and gets to a point of short-listing vendors. Cost as a roadblock at any level is subjective only. Back in the eighties when an emerging technology, DNC for file transfer was introduced. High value, low volume manufacturers in the aerospace industry spent hundreds of thousands of dollars developing methods of file transfer to eliminate paper tapes. The technology evolved over the next fifteen years until it became cost effective for the average machine shop. The term, Industry 4.0 emerged almost a decade ago in Germany from a project in the high-tech strategy of the German government promoting computerization of manufacturing. At the same time OEE or Overall Equipment Efficiency began to be adopted as a metric driving machine data collection back as far as 2008. The parallel paths, as the technologies evolved finally crossed over about four years ago when the subject of connecting the shop floor was referred to as Industry 4.0 or I.4.0. Interestingly, if you ask a pundit of the original evolution out of Germany he will describe a technology which has not matured as yet, whereas, if you talk to a pundit of OEE and MTConnect, they will tell you that the technology has finally matured and is productized. A solution from each camp can be hugely disproportionate in cost. Add to that late comers, cloud services, IoT providers, all looking for their place in the technology adoption, has probably muddied the waters.
This topic is directly related to the last topic in that some approaches are heavily engineered, and others are productized. The uninitiated manufacturer can only draw their initial conclusions from experience and investigation. The challenge is that the “investigators” will greatly skew the results. If one were to leave the investigation up to IT, the results will be assessed, and presented through an IT colored lens. If the investigation is left to engineers, then the same results will be presented through an engineering lens. If the investigation is done predominantly through members of operations or production the results will be shewed to the important aspects of those disciplines. Regardless of the ability of a manufacturer to assess current technology options, I want to emphatically and allegorically state that, given a decade of focused deployment, the technology, for the most part is productized and therefore the complexity is greatly reduced. Where, a decade ago, technology dictated that to connect a machine would take days of engineering, today a machine may well come equipped and ready to connect to the network and the operational and process data be available immediately. Even a legacy machine, decades old can be connected within a couple of hours. Machines are nodes on the network. The host Manufacturing Operations Management system is likened to any other mature technology running on host VMs and, for the most part, almost maintenance free, requiring very little interaction from IT. Users begin using the software and training is rapid and results are quick. I’ve seen a system provide benchmarking data within two weeks of connection with sizable efficiency improvements as quickly as engineering and operations could act on the constraints. Complexity is not an issue when the correct technology is adopted. If I were to assess what should be considered as potentially complex, would be inherent in some of the methods of deployment. If a company elected to employ a cloud-based technology what is the challenge to get their data out of the cloud is needed? What are the ingress and egress costs? What is the data storage costs? What are the latency issues?
99% of the manufacturers I have worked with have the solution on-premise.
In summary, if you are a manufacturer beginning your journey of Industry 4.0 then I hope the article has helped in providing a framework from which to assess your position. To make your journey as successful as possible, assemble a team represented from each department through which you can circulate a charter by which you can define a goal that is objective.
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