The right MEDS (Maturity in Eco-systems & Data Science) to unlock value from digital!

Abstract

With the rise of Industry 4.0 and other technologies and advent of post Covid new normal, all types of organizations have started talking big on digital, and have started doing digital as well. Through this article, I share my understanding of the maturity in India and a potential approach to progress the right way. Some questions that could get answered are:

1. What is Industry 4.0? Why is analytics important to it? (Part 1)
2. How can analytics drive efficiencies and beyond? (Part 2) 
3. How can analytics be a foundation for business evolution? (Part 3)        

This is also available as short read-outs in three parts:

Click here for Part 1 / Click here for Part 2?/?Click here for Part 3

What is Industry 4.0?

The term “Industry 4.0” is not new anymore, but why 4.0? The first Industrial revolution, driven by steam mechanization, eased labor-intensive work. Electricity and motorized automation changed lives of people forever, speeding up life and manufacturing operations in the second. Through Information Technology, Industry 3.0 enhanced operational efficiencies with SCADAs and PLCs enabling closed loop control systems and ERPs providing tight control over business operations across the procure to pay and order to cash life cycles. While these enabled tremendous progress in the personal and business world, from micro-processors to space crafts & satellites, Industry 4.0 took everyone by surprise. What it offered was not an assistance to manual labour, it wasn’t speeding up operations, it wasn’t bringing new systems in life – it changed things through a simple deal – to connect anything with everything – man & machine, strings & numbers, voice & text, and you name it all. The boundaries between systems – manufacturing or operating technology and information technology were finally breached.

How do Industry 4.0 technologies fit into operations?

It offers ways and means to study machines like never-before. The cost of sensors, GPS, RFIDs, etc. shrunk many times. Any size of data can now be processed at a reasonable speed. And this enabled each leg of operations to be digitized and connected. Vehicle tracking helps in timely communication of order deliveries, while RFIDs help in seamless and speedy tracking of movable products. Robotic Process Automation and Neuro Linguistic Programming ease out communication across devices and systems. IoT enables the entire plant operations to be captured and tracked real time on a screen. Block chain, as a technology, helps in securing and tightening contracts across parties. Drones and Video Analytics enable remote operations and offer improved quality and surveillance, especially in large industrial sites. However, only few technologies like GPS have been embraced yet, especially in India. They need investments - both monetary and in terms of time, as the RoI for such investments are still not to desired level. Most Indian organizations have not been able to embrace all this yet, as they look for opportunities and initiatives that have a short pay-back period. They key therefore, lies in something that can connect all these, with whatever already existed – Analytics.

How can analytics drive efficiency, effectiveness and beyond?

Manufacturing:?Analytics can drive almost anything and everything in manufacturing operations. It can help in deciding “what, where and when to buy” in your procurement function. It can help in recommending an optimal raw material mix and operating parameters. It can help you predict when things can go wrong. It can also help in identifying and quantifying instances of energy cost leakages and operational losses. Techniques like Artificial Neural Networks and Genetic algorithms have enabled advanced planning and execution, providing real time schedules based on pending orders and current production.

Supply Chain:?Supply chain analytics has also evolved today – to provide the right balance between cost to serve and speed to serve. It can help in optimizing the distribution network, proving recommendations on the network design. Dynamic order prioritization and allocation helps in identifying the right source and mode for logistics at lowest cost. It also enables agile efficient operations through load building, order clubbing, etc. improving capacity utilizations and reduced order processing times. The timely alerts help in reducing interruptions, thereby reducing turn-around-time (TAT) and improving asset utilization.

Sales & Marketing:?The most critical application of analytics though has been sales. Most sales organizations are driving various decisions around planning, execution and governance through analytics. Execution teams have been strengthened with devices and applications that provide all relevant information for a sales call in a single click and collects necessary market intelligence. These are then used to create logical micro-level stockist and counter level plans – both from volume and pricing perspective. Exception based analytics helps in bridging the gap between plan to actual to achieve the targeted performance. Social listening and analytics helps brands understand their status better. And finally, the efficacy of investments have been improved through marketing spend optimization analysis.

Why do we need an incremental maturity approach to adopt them?

While several initiatives have been successful as Proof of Concepts across sectors, they would take time to mature. It has the potential to offer early benefits to organizations before embarking on the transformation journey. The only imperative for this success would be data quality – Analytics offers guided decision support to workers but can only work with right inputs. If there is no discipline with respect to data, these solutions are bound to fail. This would not just impact specific initiatives but put breaks on the digital journey of organizations and industry as a whole. Success can instead be driven and derived from an incremental analytics and eco-system maturity adoption.

What is the recommended incremental maturity approach?

While several initiatives have been successful as Proof of Concepts across sectors, they would take time to mature. It has the potential to offer early benefits to organizations before embarking on the transformation journey. The only imperative for this success would be data quality – Analytics offers guided decision support to workers but can only work with right inputs. If there is no discipline with respect to data, these solutions are bound to fail. This would not just impact specific initiatives but put breaks on the digital journey of organizations and industry as a whole. Success can instead be driven and derived from an incremental analytics and eco-system maturity adoption.

“Anything that is measured gets improved" - we can start applying rule based descriptive decision science from existing data to provide actionable inputs. This in-itself would create substantial business value, which can be used to digitize operations and enable better data collection. These can then be scaled up to perform causal analytics, and subsequently to predictive models as data volume goes up. This stage would help users perform sensitivity analysis and business validation, laying the foundation for trusting analytics at various levels. Once these are set in place, advanced analytics can come in to optimize operations and provide the value missed out from the preceding models.

Apart from driving efficiencies and effectiveness, companies are looking at customer experience as a key focus area, and several businesses are transforming their operations through a customer-centric approach. They are upgrading from a sales approach to an eco-system approach – creating a metaverse-in-reality across supplier, organization, customers, influencers, and consumers. As the maturity increases, complementing alliances partners can be included in the eco-system for missing products and services, creating a single integrated platform for the consumer. A scenario, where an organization includes its competition in its own eco-system, may find increasing adoption in all sectors.

Conclusion:

At the current moment, analytics opportunities are still initiatives, the key however would be to adopt a strategy around analytics. Innovative business models like ‘Uber’, have changed the dynamics of the automotive sector. ‘Swiggy’ has revolutionized food and restaurant businesses. ‘Insta-mart and Zepto’ are?re-defining retail operations. The ‘urban claps’ have brought everyone to our door-step in the click of a button. More importantly, ‘Google’, which aspires to be the omnipresent, omniscient and omnipotent ??for consumers, has started investing in business graduates, as B2B and B2C have started merging into a single?eco-system.

It shall only be a matter of time for analytics driven strategy and operations to take precedence. Over time, supply chains across businesses including competitors, shall get integrated into common platforms – a concept several market leaders across sectors have started exploring. After-all, the concept of “Vasudeva Kudumbakam” is not just about geographical connect, its across everything from machines to businesses. As an after-thought, Industry 4.0, may be the physical manifestation of this concept, more than anything else that has occurred or may occur ever.

This is also available as short read-outs in three parts:

Click here for Part 1 / Click here for Part 2?/?Click here for Part 3

Disclaimer: ?This article represents my thoughts/views on the subject based on personal and professional experience across organizations and is an upgraded version of my article for the student community of a management institute (Well-known Institute of Management in North India) last year and does not act as a research support or as a professional recommendation to any organization or from any organization. Special thanks to MS Power Point Icon and Image Search Feature for the nice images.