ML Journal April 2021

ML Journal April 2021

6 Pillars of Successful M4.0 Execution

Adapt to change, including big disruptions like the pandemic, manufacturers need to embrace a set of technologies that enable changes to business operations quickly and to innovate for changing times.

Emerging technologies like machine learning, robotics, advanced analytics, IoT, and blockchain are bringing fast changes to the manufacturing industry. While these technologies bring new innovation and efficiencies, they also increase the velocity of business. The automotive industry is a great case in point. The internal combustion engine is making way for electric motors. Autonomous driving is making rapid progress. Vertical Take Off and Landing (VTOL) vehicles are getting closer to reality than ever. In an industry that was designed for efficiency and cost (along with quality), there is a sudden need for optimizing for opportunity cost as well. Therefore, companies are iterating and moving faster than ever.

Recently, the pandemic has accelerated the need for business agility and more importantly, business resilience. Going back to the automotive example, amidst the innovation and progress, there is an ongoing shortage of automotive chips (attributed to low demand expectations during the pandemic) that has resulted in crippled production lines. Manufacturers in the consumer goods space have also seen similar Forrester shocks in supply chains, such as the toilet paper shortage in 2020, and rapid changes of customer buying patterns, such as flocking to contactless modes like ecommerce.

>Adapting to such a rapid and varied pace of change is not easy. In large companies the challenge is even more pronounced. However, it doesn’t have to be that way. Companies that are successfully adapting to the changes in business paradigms are also handling business shocks better than their peers. A few factors contribute to this:

1. Technology Adoption

Over the last few years, the role of software has been increasing in both business operations and product functionality. For example, we are witnessing a rapid rise of software-defined products in many sub-segments of manufacturing – especially in automotive, hi-tech, and consumer goods. Such products enable companies to release features and upgrades much faster than their hardware-centric counterparts. The products can be configured to gather real time analytics on the usage of the product, which is helpful for future product cycles, pricing, and service. The iPhone is a prime example of the benefits of this paradigm. In turbulent times, software-defined products can adapt to address immediate customer needs.

On the operational side, IIoT and machine vision-based solutions are enabling efficient and effective real-time monitoring, anomaly detection, worker safety, operational efficiency, and process automation of factory floors, warehouses, and yards. Lately, technologies like augmented reality are enabling the optimization of field service. AR provides the technician step-by-step repair directions and advanced visualization of part assemblies, thereby minimizing repair time and any human error.

With the right data infrastructure, decision makers can see better around the corners and explore alternative plans faster.

On the user self-service and engagement front, mobile digital assistants (also machine learning based) are enabling an interactive experience for customers and employees. Users can use voice and text-based interaction to access information from enterprise backend systems using natural language. Employees, too, can leverage these assistants and minimize the work within work, such as searching for actionable information.

From product innovation to business operations to customer experience, companies are increasingly harnessing the power of modern technologies.

2. Data-Driven Decision Making

A recent McKinsey study has shown that data-savvy companies enjoy higher margins than their peers. that data-savvy companies enjoy higher margins than their peers. For a company to be data savvy, it needs three things.

First, a company needs a unified data infrastructure that can serve the diverse needs of the enterprise workloads – APIs, applications, analytics, real-time systems, batch processes, transaction processing, partner systems, data feeds, data science, computer vision, and more. A well-configured hybrid, edge, and multi-cloud data infrastructure offers flexibility, resiliency, scalability, and agility for changing business needs of the day.

With these underpinnings as the basis, companies can then build a digital thread that affords them 360-degree visibility, as well as insights about the causes and effects of various aspects of business operations. One factor to bear in mind – as the scale of data-driven applications goes up, the cost of scaling the infrastructure also goes up as well. This is where data-savvy companies leverage cloud and hybrid architectures to help them make the transition from on-premise to the cloud and keep the costs under control. Adoption of industry data models and standards lowers the complexity, expedites development cycles, and minimizes maintenance overheads.

Second, companies need the right mix of tools to quickly realize the value of insights, visualization, and predictions from data. In most companies, 90% of the data goes unused. In addition, more than 70% of the time and effort in data science projects is often spent on moving, cleaning, and preparing data. With the right data management, analytics, and data science tools decision makers have timely information to make well-informed decisions. Low/no code tools offer the ability to rapidly automate repetitive business processes, integrate information from diverse enterprise systems, and build custom business applications to empower decision makers and stake holders. With modern concepts like explainable AI, deep observability across layers, and advanced role-based access and entitlements, data-savvy companies ensure the right information is in the right hands, at the right time, for the right purpose.



Companies that are successfully adapting to the changes in business paradigms are also handling business shocks better than their peers.


Third, employees need to be skilled (or trained) with data-driven operations and decision making. Up until recently companies relied on traditional business intelligence systems and historian data sources to plan for business operations. With the ever-increasing pace of business, unplanned disruptions, and increased expectations from customers, traditional processes for planning are falling short. Employees need more localized decision-making capabilities and at the same time executives need the visibility and confidence to enable decentralized decision making. With the right tools, companies can lower the friction for employees to perform their duties and minimize the learning curve needed to move towards data-driven decision making.

With the right data infrastructure, data architecture, predictive models, and advanced visualization tools, decision makers can see better around the corners and explore alternative plans faster and implement better strategies for success.

3. New Business Models

Nobel Laureate economist Oliver Williamson showed us that when the transactional cost of an ecosystem changes, the order in the ecosystem goes through a reconfiguration process. Technology is inherently deflationary, and it democratizes value across the value chain. Modern technologies such as cloud, IoT, and AI/ML enable every manufacturer to have the sophistication of business operations and real-time feedback loops that were only enjoyed by the big players that had moat and economies of scale.

One such popular business model is product-as-a-service. In this model, the product is sold as a subscription service to end-users with a pay-as-you-go billing model. This model has quickly caught on in the market as it minimizes the friction of ownership for the user. However, the onus on the manufacturer/seller increases in this model as they are now responsible for the device performance, customer experience, maintenance, and service. Managing the mechanics of the recurring revenue model is another key aspect of the product-as-a-service approach. Since the product costs are often front-loaded, managing the customer acquisition cost (CAC), lifetime value (LTV) of the customer, and churn are extremely important. Avoiding churn to ensure a healthy financial performance for the company is often the biggest imperative across the board.

In the age of speed, companies that organize for opportunity cost are the ones that thrive.



Another emerging business model is that of a composable enterprise. Gartner defines this as the ability to offer dynamic adaptive business applications that assemble functionality from inside and outside the enterprise based on user context. The manufacturing industry is not new to partnerships. Historically these partnerships have been with suppliers that produce the BoM components of a given product.

These days as companies increasingly begin to adopt digital products strategies, the functionality and features of a digital product can be much more dynamic, personalized, and real-time. Through APIs, AI/ML, blockchain, and adaptive security mechanisms companies are weaving in value-added functionality into their product lines. This flexibility allows companies to earn additional revenue, while increasing customer experience and value. This model also affords the companies to experiment with new ideas, leverage partnerships, learn, and iterate quickly, thus providing a viable way for incumbents to compete with nimble startups.

Lastly, based on the evolutionary path of technologies like AI/ML and blockchain, we may see viable forms of data cohorts and data marketplaces. This allows better mechanisms for companies to leverage data from other companies in their ecosystem, third-party data brokers, and also with monetizing their own data. Today, this happens in a very limited way, and in select cases only.

Since we don’t yet have the right control valves for data to manage the flow and consumption effectively and with nuance, companies have to be extra conservative. However, with breakthroughs in cryptography, privacy preserving techniques, and mechanisms for managed data consumption, it won’t be long before companies begin to see data as a monetizable asset on their balance sheets. Once these technologies are commercialized further, data markets and data cohorts will be more pervasive than they are today and offer additional sources of revenue for companies.

4. Innovation and Experimentation

In an age where industries are getting disrupted (first slowly, then suddenly), companies have to pay close attention to their innovation engines, information flow within the organization and in their ecosystem, and the overall mechanism design of business operations at-large. As discussed earlier, new technologies, new business models, new product concepts, and new UX affordances bring a range of possibilities, challenges, and risks as well.

However, not all innovation is the same. In most companies, we often see innovation pursued in the form of partnerships, or open innovation channels in their ecosystem. Some companies have formal processes defined around design thinking workshops, start-up accelerators, and R&D investments. A very few pursue truly disruptive models from within, and rightfully so. This requires a VC-like setup, financing, and leadership backing to support the high-risk, high-reward power law-based paradigm. Each innovation method has its place as well as pros and cons. For companies that are actively pursuing the Industry 4.0 vision, innovation and experimentation are central to their strategy. With the right people, processes, and tools, companies can navigate faster and realize value quicker. The lean methodology that the manufacturing industry is renowned for also works very well for innovation.

The first order of any big change is to take employees into confidence and to help them visualize the future.


5. Well-Integrated Teams

No business transformation is ever successful without the right talent. But a change of this magnitude, especially at today’s speed, is not easy. For companies that are optimized for the traditional B2B model, the new B2C or B2B2C model of product-as-service will bring about a phase of adaptation and adjustment – until the right talent, the right incentive structures, and the right business processes fall into place. Once again, good data, insights, process automation, and collaboration infrastructure will help companies go a long way by empowering employees with better context and actionable insights. In the post-pandemic world, knowledge workers should have reliable portability of work context so that they can stay productive. Modern low/no code systems, mobile applications, and digital assistants enable teams to share information and collaborate in real-time and help organizations develop the right talent. Such systems also improve work-life balance, employee wellness, and engagement.

The ability to have decentralized decision making also empowers teams to move quickly and resolve issues locally before they get escalated across regions and organizational levels and end up impacting the customer experience. In the age of speed, companies that organize for opportunity cost (versus transactional cost) are the ones that thrive. This means established mechanisms for reverse flow of information are important. Toyota introduced the concept of Gemba on factory floors, so that leaders can have a first-hand understanding of the reality on the ground. In globally distributed businesses, modern technology can play an equivalent role in providing the right visibility and pulse to leadership from the ground level.

6. Culture

Peter Drucker famously said, “Culture eats strategy for breakfast”. Charlie Munger, the legendary investor, frames it in a different way, “Show me incentives and I will show you the outcome”. Juxtaposing these two quotes provides a powerful mental model of what culture is, and its importance in the modern business environment. Culture is the unwritten or quasi-written code of how a collection of people operate in the game they are set up to play.

Digital transformation inherently introduces disruption and change to the routines and models that teams are used to. However, history is full of both success stories and failures of companies attempting large transformations. This is where technology can play an influential role in building trust and camaraderie in times of change, along with org-level compassion, communication, and empathy.

The first order of any big change is to take employees into confidence and to help them visualize the future. Successful companies start early innovation on the edges of their business, deliver a few wins, and then gradually build their way into rest of the organization. All successful transformations need employees to enjoy a strong sense of purpose and mission for the change. Seeing the metrics change in real-time often boosts their confidence and preempts incorrect perceptions.

The First Order of Things

As philosopher Lao Tzu said, “The journey of a thousand miles begins with one step.” In today’s world, data-driven initiatives like digital transformation and Industry 4.0 begin with having the right cloud data infrastructure in place. With the right tools in hand, and with the right people, processes, incentives, and culture, companies can enjoy positive change, orderly transitions, and sustainable growth.

View More