Neoskilling to Support Digital Transformation in Manufacturing
Published by : Industrial Automation
Having the content and rolling out training programs alone will not be sufficient for successful Neoskilling, say Prof L Prasad and S Ramachandran.
The onus on individuals to keep themselves employable throughout their careers is paramount in the coming era of Digital Transformation and the accompanying Artificial Intelligence (AI) revolution, commonly referred to as Industry 4.0. Unless one is capable of being creative/innovative, exercising judgement/improvising while thinking on one’s feet and showing compassion/empathy, the individual could be replaced by bots or deep learning AI systems.
The demand supply gap
According to recent media reports, the ‘American Workforce Policy Advisory Board’ created for workforce training reported interesting findings. Limited availability of skilled talent was a top challenge for business leaders. The level of commitment seen for organisations to invest in employee training is at the highest levels in recent years. But developed countries have already undergone the earlier waves of automation.
In developing countries such as India which missed previous industrial revolutions, Digital Transformation is in a tricky situation. Globally, automation technologies have proven what they are capable of. In India, it is the lack of skilled workforce that is slowing down large scale adoption of these technologies and the benefits they offer.
As IBM CEO Ginni Rometty mentioned in a recent conference, there are several job openings available, but what is lacking is a skilled workforce to take them up. To quote Ginni, "In India, you have the same issues. Open jobs, (but) no matching skill sets." She also suggested ways ahead such as focus on skill instead of degree in the education sector, business-government collaboration etc.
The situation is not limited to the IT sector
What can business leaders do to make sure we do not miss the bus Industry 4.0 bus this time? The people aspect plays a key role. Neoskilling is a holistic, futuristic approach for building human capabilities, keeping tomorrow’s needs in mind. Organisations should adopt it for proactive building of skills required to excel in the digital economy.
Neoskilling for Digital Transformation
Neoskilling is an all-encompassing approach. In the continuum of skill development initiatives, Upskilling is incremental improvements to what is done today. For example, training a conventional tool operator to work on a numerically controlled, programmable version of the equipment. Reskilling is a lateral shift to new domains. General Motors recently moved thousands of its engineers from Internal Combustion (IC) engine to Electric Vehicle (EV) programs which have a completely different architecture, with much more software embedded in them and fewer moving parts.
Neoskilling is not just the training of workforce on discrete emerging skills. It is a cultural transformation to start believing in life-long learning. It is a mental metamorphosis to work in coherent teams with a common goal, not just with humans but with combinations of robots that are collaborative, software algorithms for decision making, bots for machine-human interaction, in a virtual world, etc.
The hierarchy of Neoskilling – where to make a start
Neoskilling is not limited to class room sessions where specific skill sets are taught. Where can organisations make a start? Figure 1 shows the hierarchy or the stages of Neoskilling. It starts from a basic Awareness of what skill sets will be important. Following awareness is Access to training programs, internal or external partners, content providers and delivery of the content using multiple channels. Once access is established, funding or Affordability will be required for support and sustenance and eventually an Appetite to learn and Ambition to excel in the new skills learnt.
An online survey was conducted for the book1 on Neoskilling which we recently published. More than 300 senior business leaders and influencers responded when approached to share their inputs. Interestingly, our study findings show that the biggest challenge faced by organisations is the initial phase or awareness – how to make a start in Neoskilling, what skill sets, who will deliver the training, how will trainees be assessed for readiness to take up jobs?
Having the content and rolling out training programs alone will not be sufficient for successful Neoskilling. Incentive programs need to be implemented for employees to adopt the new skills. Training should be followed by roles commensurate with what is learnt. The new generation workforce does not worry about designations or the level in the organisational hierarchy. We are moving towards an employment scenario where skills that can be demonstrated are much more valuable than degrees or certificates.
Fig 1. The hierarchy of Neoskilling.
Neoskilling for Industry 4.0
Industry 4.0 by the very way it has been defined could be a good template for manufacturing organisations looking at where to start Neoskilling. The basic tenets of Industry 4.0 are
- Connectivity using technologies such as Internet of Things (IoT) for closed loop systems
- Analytics to identify meaningful insights and actionables from the data collected with IoT
- Digital twins to create virtual models of systems and run what-if type of analyses, and
- Autonomous systems leveraging AI to take decisions on their own and work without human intervention.
IoT helps in monitoring and controlling an equipment’s operations remotely, bringing in an element of programmability. Data available about an equipment or a process and its performance is an opportunity to apply analytics. These two can be the starting points for Neoskilling. Any role in the near future will have minimum requirements of programming and data analysis. Digital twins and autonomous systems will need much more advanced capabilities.
Software is increasingly becoming embedded in products. It will become important for the Workforce of the Future to have a basic competency in programming an equipment, independent of on any specific language or technology.
Traditional approaches like Six Sigma which advocated a data based approach for continuous improvement should be repackaged for the digital context. Data in today’s digital era is not always measured physically but mostly in digital format. Employees should be trained to ‘read’ data to make sense out of them. Consumption of data should be secure from cyber threats and ethical in nature, avoiding any discrimination.
Prioritising Neoskilling based on roles
It is important to prioritise and start Neoskilling based on different roles in an organisation. An effective way of doing it is by adopting a modified approach of Charles Perrow’s classification of technology. All jobs in an organisation should be analysed based on two parameters – i) Programmability, the ease with which a role can be expressed as repeatable, mechanical steps, and ii) Novelty, the number of exceptions or unexpected situations encountered in a job needing judgement and decision making. A two by two matrix can be used to classify roles depending on High or Low Programmability and Novelty (Figure 2).
Routine jobs that are high on Programmability and low on Novelty are at the highest risk of getting replaced by automation as they are repetitive in nature. There are several estimates on the size of this segment, 15% being an approximate, conservative one across sectors by several experts.
Fig 2. Perrow’s classification of technology.
Professional roles that are high on both Programmability and Novelty could be the next in getting replaced using AI, with a rule based engine for judgement and decision making. Examples are project and people manager roles.
Craft occupations include those in maintenance, prototype shop or pilot plant engineers etc., who need to improvise and ICU nurses who need empathy towards patients in healthcare. Craft jobs are at the least risk of getting replaced by machines.
Non-routine roles such as scientists and R&D engineers will be immune to AI due to their creativity and innovation which no machine can duplicate. For example, basic research scientists come up with great ideas which seem intriguing. Applied research engineers then evaluate these esoteric ideas and translate them into models for demonstration in the lab.
Employees in Routine category of jobs should be Neoskilled to take up Craft type of jobs. Organisations should support employees to make this transition as it will be a big shift in mindset and the type of practical skills required.
Individuals in Professional Technology category should be Neoskilled for Non-routine jobs. Those who want to, could be reskilled for Craft jobs. For example, many “proof-of-concept” working models rigged together by R&D personnel with whatever components are lying around, tend to be rather “incomplete” designs. Savvy, practical, hands-on Engineers could provide valuable insights into “engineering” these designs for manufacturing and post-sales servicing.
Organisations undergoing Neoskilling should keep in mind the concept of Imbedded Technology Capabilities (ITC) proposed by Albert H. Rubenstein. ITC is the subject matter expertise gained over years of experience in one’s own domain. Because of their intuitive nature, it is difficult to document or teach in classrooms. Hence, such expertise is immune to automation and AI.
Leaders should know where the organisation’s mix of these four role types is today and more importantly where they would want it to be in future. Neoskilling is not just to ensure employability and a long-term career. A workforce skilled in emerging technologies offers a competitive advantage for organisations to stay ahead of competition.
Neoskilling will be specific to each organisation depending its maturity level in digital capabilities and where it wants to be in future. There is no one-size-fits-all approach. Digitisation is not limited to picking up of new age skills. Instead, it is an amplification of the knowledge one has already gained in a particular domain, leveraging it with emerging technologies for making a significant business impact.
The technological challenges are immense but not insurmountable. Overcoming them will be smoother only if corporations, professional associations, government agencies and universities join hands to unitedly formulate and implement responses. Each stake holder has a strength. Working in collaboration will have a multiplication effect for an ecosystem that is strong in meeting the digital demands of today and tomorrow.
1 Prasad L, Ramachandran S, “Neoskilling for Digital Transformation and the Artificial Intelligence Revolution”, Wiley India, Jan 2019