"Neoskilling for Digital Transformation" by Mr. S. Ramachandran
#Readtolead #Neoskilling #DigitalTransformationRead-To-Lead is an initiative of ProMFGmedia to interact with the authors of business books to learn from their literature and experiences.
S Ramachandran spoke about his latest book, " Neoskilling for Digital Transformation.” The book is about how managers must reskill their teams, to meet today’s needs. Leaders should think ahead, for the futuristic long-term needs of their organization and the overall ecosystem to excel and reap the benefits of Digital Transformation we are undergoing.
Q: What are the differences between Neo skilling versus the terms that we get used to our rescaling and upskilling?
Mr. Ramachandran: Although digital transformation is the popular trend in the industry, not many initiatives turn to fruition. According to Boston consulting group, only 30% of the digital initiatives are successful. The chief reason for the digital initiatives to fail is the individuals’ lack of adaptability to new changes in the system. The rigid mindset plays a major role in unsuccessful initiatives and hence it is crucial to introduce adaptability skills in the system. It is essential to train the individuals with the required skill set, as it will help to accept the new changes in the system. Amidst the rapidly growing economy, individuals should train themselves with special skill sets, which will enhance their value in the organization. In the current times, apart from holding professional certification, it is critical to possess additional skillsets. The skillsets also help the individuals in adopting the change, which is the core of digital transformation. Organizations should adopt it for the proactive building of skills required to excel in the digital economy.
Neoskilling is a part of the skill development continuum that focuses on building human capacities. To cite an example, often in the corporate world spreadsheets are used to initiate or conclude any rational decision, known as basic skill development. However, in the continuum, individuals focus on upskilling, wherein they learn to write the programs and replace them with manual work. Upskilling is a process of learning new skills or of teaching workers new skills, whereas reskilling is the process of learning a new skill to perform the same job differently. Neoskilling is a holistic approach to prepare ourselves for the future by realizing the requirements of tomorrow. Predicting the future of work post-pandemic is tricky, and no one can predict what the future beholds for the business world.
Citing an example for Neoskilling, we use artificial intelligence or simple machine learning to let the algorithm autonomously decide without human intervention. Thus, it not only does the calculation but can also take the decision. This self-learning process is known as Neoskilling. Technologies introduced in today’s digital world have low shell life, which leaves the industries on the edge. Business leaders must have a futuristic perspective considering the future trends, and hence they must prepare the team to tackle any unwanted crisis. This approach will provide the leverage and help to stay ahead in the market. This is the story behind the term Neoskilling for digital transformation.
Q: Your book mentions the MINIMEC trap. Kindly share more information on this trap.
Mr. Ramachandran: The MINIMEC trap is an acronym that stands for strategically Myopic, Intellectually Impoverished, Ethically Challenged management. Due to the rat race culture to achieve the digital transformation, the organization often falls into the trap. In need to achieve the quarterly or annual results, leaders overlook the long-term perspectives. The leaders need to identify the long–term and short–term perspectives and undertake the calculated risks to avoid any strategically myopic. The intellectually impoverished limit their focus to the obvious cause and effects and quick arrangements. Such exercise is called single-loop learning but eventually, the leaders must graduate to double-loop learning. The double-loop learning process questions the learning and assumptions of the business models.
To cite the best example of double-loop learning is Aravind Eye Hospitals, which is known for its magnificent work in the area of eye care. They majorly focus on eradicating cataract-related blindness in India. Although the organization has been dedicatedly working towards providing eye care, they commenced deep research on the prime cause. Their study pointed towards malnutrition, and hence rather than just addressing the cataract surgery, the organization started to provide nutritional supplementary programs in rural areas for women and children. The proactive measure to reduce the number of cataracts is the perfect example of double-loop – learning. Companies can avoid being intellectually impoverished by practicing such steps in the processes.
In the current times, we often witness that companies skip practicing rigorous ethics in the business, which creates chaos and an unhealthy work environment. Humans as well as algorithms were designed and implemented to make our systems foolproof, tamper-proof, and more accurate, but we have bypassed the checks and balances of the system. The business world has experienced numerous cases wherein ethics have been compromised for personal gains. Examples such as Volkswagen’s diesel-gate scandal and the case of false quality reports submitted by Japanese steel & automotive companies are testimony to unethical human nature. However, in the algorithm aspect biased decision-making is generated due to the implementation method of the algorithms. The decision-making process is affected due to data fed by humans. These algorithms are trained to use the historical data incorporated by humans.
Any biased decision inherent in the legacy process gets carried over into the automated, digitized - a new way of doing the process. Amazon recruitment tool was one of the biggest examples in the industry, which fell prey to a similar trap. The tool had created a preference for certain types of candidates using equipment, and facial recognition tools hit a lot of backlash due to the bias inherent present in their system.
The organization must identify, quantify and eliminate bias decisions in the algorithms. Whether it is a digital or manual business, organizations should be trained for ethical business practices. In the current digital world, ethics have adopted a whole new perspective, and organizations must work towards eliminating bias as a part of their skill development journey.
Q: What are the key takeaways from your book which can be helpful for manufacturing professionals?
Mr. Ramachandran: On the individual aspect, professionals from the manufacturing industry must believe and implement a lifelong learning attitude. Self–motivation is the key to shape your personality and learn new skills in the industry. On a similar note, leaders and organizations must provide essential space and resources to the employees to grow in their respective domains. The practice of lifelong learning should not be limited to the classrooms or training centers, but it should be incorporated into opportunities or new projects. The digital transformation has swamped the industrial business, and one can witnesses a massive transition in technology. All the jobs in the organization can be categorized into two different parameters – Programmability and Decision making. Programmability enables the ease with which a role can be expressed as repeatable, mechanical steps, whereas decision making can be described as the number of exceptions or unexpected situations encountered in a job needing judgment and decision making.
Considering the highs and lows of the two parameters, the jobs high on programmability, and low on decision making are the ones at the highest risk for automation. This is one of the ways to identify opportunities for automation specifically in the manufacturing sector, and can also be applied across industries. It is also an opportunity to reskill the staff for the automation process and prepare them for the future. Automation is surrounded by numerous negative perceptions, but replacing humans with machines is one of the most popular perceptions. However, automation is the flavor of the current industrial era. There are specific jobs that machines do better than humans. Therefore, it is the responsibility of leaders to identify such jobs and implement automation in their systems accordingly. Many new roles are coming to the surface, that did not exist before, due to automation. The employees of today must focus on upskilling themselves to be prepared to take up the new role tomorrow. At the same time, the industry must ensure that the right talent for these new roles of tomorrow is available to avoid a demand-supply gap later. Often the Indian manufacturing organizations focus with respect to the first-order environment, comprising of customers, suppliers, and partners, but it is essential to focus on the second-order environment, which comprises the government, the analyst, community, education sector, and policymakers. In the current cutthroat race, focusing within the industry is not the only solution to match the pace of the aggressive industry. The manufacturing industries must focus on the parameters outside the industry to match the pace of the latest developments.
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