Two recent independent studies on employment opportunities and digital technology-induced changes have tried to gaze into the crystal ball and predict the future.
A study done by MIT Sloan School of Management clearly highlights a trend of growing disparity between productivity and employment generated, the second study, while corroborating the same in some sectors, predicts a growth in job creation in some sectors. PwC has predicted that while seven million existing jobs could be displaced by robots and algorithms, 7.2 million jobs will simultaneously get created.
The OECD (Organization for Economic Co-operation and Development) supports the aforementioned views, but puts the figure at about 10% for the US and 12% for the UK as the jobs that would see their tasks changing significantly or are at ‘high risk’ of being automated over the next two decades.
In addition to the above studies, observations across the manufacturing industry have one thing in common – IoT led transformation is changing the norms on the factory floors of manufacturing organizations; all at a pace and on a scale that humans in recorded history have never experienced.
While the premise of this blog is neither to challenge nor support the efficacy of the claims on jobs, what it does propose is to highlight some of the strategies that factories could deploy in order to drive the digital transformation initiatives while optimizing the workforce at hand. Before we delve into the strategies, let us take a quick look at the principles of the IoT-led change.
While ‘people’ is one of the three change principles described above, it is ironic that it has commonly been overshadowed by the other two factors since the beginning of the industrial revolution. Hence, it is no wonder that when factory managers talk of implementing Industrial Internet of Things (IIoT) or such other initiatives, they often encounter cynicism and a disconnect in large. Everyone, from the operator to the line supervisor to his manager, exhibits resistance when the topic of IIoTization is broached.
So, what strategies should organizations adopt in order to preemptively mitigate any failure of the IIoT big ticket initiative? The single most important thought that CTOs, CIOs, and plant managers should consider, while implementing IIoT, is focusing on synthesizing the human element – utilize big data and automation without losing the intrinsic human element in the process. If people are valued the right way, they will be drivers of success, no matter what the change is.
#Strategy 0: Having a digitally-literate leadership team
IoT-led transformation strategies have to be, without a doubt, top driven. This requires a supervisory or executive board that is multi-generation, diverse, and possesses the right expertise to advice on fast-moving business and digital technology topics. While this team will take policy decisions on technology, they would also need to be sensitive to the human element by enabling, training, and getting the workforce up to pace with evolving technologies.
For that to happen, the leadership team needs to identify advocates from various disciplines of the organization and not just rely on IT for the end outcome. This is a prerequisite for a robust digital transformation strategy that aims at bearing desired fruits for the business.
#Strategy 1: What is in it for me? Ownership and accountability
This is often seen as a selfish, self-serving query. But if plant managers can articulate the benefits that the workforce can accrue, courtesy of IIoT, a smooth and seamless transition will surely happen.
For instance, during a MES implementation, the quality head at an automotive plant highlighted how his team of quality inspectors was spending hours collating observations and communicating with stakeholders for feedback, feedforward, and alarm escalations. Once the team was made aware of the benefits of digitalization in saving effort by the use of an IT-enabled quality module, the team immediately latched onto the idea of keying in their observations online and letting the system take care of the rest.
#Strategy 2: Continuous improvement as a necessity
While it is widely accepted that Lean Manufacturing, Six-Sigma, and Theory of Constraints are the three most important manufacturing improvement methodologies, it is a misconception that these improvements are to be done by a dedicated team of employees alone. Who is better equipped than the person working and maintaining the equipment to work on its improvement?
However, improvements in the real world often tend to take a back seat because:
- The quantum of tasks to be carried out as BAU is too big
- Missing ‘outside view’ being in the ‘thick of things’
In order to extract the most and the best out of the workforce, it is crucial to empower it. Empowerment of the personnel can happen through effective communication of the various benefits of digitalization and laying down realistic expectations. In order to capitalize on human capabilities and utilize them to best effect, it is important to:
- Educate employees on how IIoTization can ease the load of mundane activities and free them to discover and work on new ways
- Empower them to generate new ideas where digitalization might support business
- Enable them to take risks in trying out new ways to work. For example, rapid proto-typing will allow them to learn from failures, while also collaborating with others in the organization will ensure an improved likelihood of success
For instance, during the implementation of a point-based IIoT solution at a commercial vehicle manufacturing unit, it was observed that maintenance personnel were bogged down by activities like extracting data from equipment’s for wheel alignment, brake testing, and emission testing, aggregating the extracted data to a common database and sharing it with production and quality department, in addition to regular PM and breakdown responsibilities. This left them with no bandwidth for improvements. When a simple ETL function was shown to do the same tasks in a fraction of the time and effort, the maintenance personnel’s bandwidth was freed up to plan and execute improvements, leading to reduced MTTR and improved MTBF.
#Strategy 3: Creativity through data
In the manufacturing industry, creativity on the factory floor requires not just intellect but also data. Since data lies at the core of any IIoT journey, using it to drive creativity on the factory floor can help the manager not only improve the processes, but also keep his workforce actively engaged.
In this context, creativity is expected to result in development of new revenue models.
Through servitization, equipment-as-a-service, subscription models, and pay-per-use programs, IIoT lets machine manufacturers expand their digital technology-focused business opportunities. It also helps address significant pain points for their customers and operators.
At a major car manufacturing factory, there were more than 350 units of panel coolers that were being serviced by the panel AC service provider. Traditionally, servicing these required the technician to manually check the critical operational parameters and take corrective actions in case something was wrong. IIoTzing the panels, so that each of the panels is not only connected with a central aggregator to share the vital stats of operation, but also raised an escalation in case of abnormality, ensured that the assets were serviced as soon as any anomaly was registered. This also reduced the cost of unnecessary supervision.
Utilizing IIoT in similar creative ways not only adds value to the process but could also lead to opening of newer revenue models. Here the creative process begins by challenging the users with a simple set of questions: What are the major pain points? What are the advantages that can be accrued by alleviating those pain points? How can modern technology enable and catalyze those efforts?
#Strategy 4: Keeping it simple
Many a technology implementation fail due to the operational complexities involved. But, when humans are integral to repetitive tasks, it is crucial to keep things simple. While data is at the heart of any IIoT implementation, it can also be intimidating to many when presented in a raw form. It is, therefore, important for architects to bear in mind while designing a data-intensive solution that simplicity of depiction and ease of understanding are primary for the end user, especially when most of them are right there on the factory floor. Not just the visuals and the colors, but the entire UI needs to be designed to create high visibility, clarity in messaging, and unambiguous interpretation.
At a medical equipment manufacturing site, a system integrator realized it the hard way that IIoT solutions to predict equipment failure were not deriving accuracy because the operators were failing to key in the required fault details effectively. After spending months analyzing this anomaly, it was understood that the operators were expected to note the observations and key in their interpretations using predefined terms that they seldom understood or what purpose they were used for.
#Strategy 5: Empower to take decisions
Never in the past have machines come so close to mimicking human decision making and action initiation. However, in a factory scenario, as anywhere else, the number of factors contributing to a given situation or a desired outcome are simply too many to account for in machine algorithms; oftentimes, this is not as simple as a mathematical model.
While the real value of collecting and sharing data is realized only when it is used by someone to take action or make a decision, a practical deployment of digital strategy is to digitalize the trends, analyze the data, predict the possibilities, and even suggest course correction alternatives. In other words, it is crucial to leave the decision making and action initiating responsibilities with humans. Not that this cannot be mechanized, but given the possibilities and the likely chances that some data points may not have been accounted for in the machine logic, the final decision may not be right in its entirety.
As a case in point, a predictive model for remaining cycles in a turbine was developed at an aviation facility. Considering various parameters, the areas that are likely to fail were highlighted and notifications were generated for the relevant teams to fix the issue. However, the final decision on whether to take the turbine out of operation or to continue using it for a less demanding flight route till its scheduled overhaul was left to the maintenance experts.
Charting the Future Roadmap
These are some of the strategies that plant managers and key IIoT decision makers should consider when embarking on their IoT-led transformation journey. While the jobs that we see today will continue to be challenged by the plethora of next-gen technology options, its larger impact will be on complementing and augmenting human capabilities and not replacing them. Once this thought percolates to all levels of manufacturing, the workforce will be more enthused, engaged, and receptive.
IoT WoRKSTM, a dedicated Internet of Things (IoT) business unit of HCL Technologies is enabling organizations maximize effectiveness and returns on their asset investment by co-creating best-in-class IoT-driven solutions with their customers. These solutions enable IoT-led business transformation through creation of more efficient business processes, new revenue streams, and business models that deliver measurable business outcomes.