+ Digital and Analytics Transformations: Myth vs. Reality  – CII Blog

Digital and analytics is top-of-mind for companies across all sectors, with 90% of CEOs believing that it will transform their industry. Yet only ~17% of CEOs have sponsored and launched digital and analytics initiatives. What is holding them back? Surveys and experience working with manufacturers across the globe show that there is still a lot of misunderstanding about what digital and analytics is and how it works. The myths are far from reality. 

Myth: Digital and analytics is only about data collection and dashboards  

Reality: Digital and analytics transformations rethink how to increase, improve and accelerate value creation end-to-end. They create value by optimizing processes, deploying new technologies such as AI/ML, natural language processing, visual analytics, and moving beyond descriptive analytics (dashboards) to prescriptive and predictive analytics (AI/ML models). A full understanding of the business and its pain points is required to enable a company to invest in digital and analytics where it matters.  

Myth: Digital and analytics will displace workers  

Reality: The new technologies used in digital and analytics transformations can create jobs and eliminate repetitive tasks, enabling the workforce to learn new skills. However, companies need to retrain their people and invest in new capabilities, e.g., data scientists, data engineers/architects, technology leaders, digital and analytics translators, and product owners. Some companies are partnering with post-secondary educational institutions to develop training programs and new ways of working.  

A case in point: Instead of hiring pure data scientists, Tata Steel Europe trains its domain experts in data science at its Advanced Analytics Academy. At its IJmuiden plant in the Netherlands, Tata built the digital skills of its site team to improve productivity, cost and quality.  

Myth: Digital and analytics requires greenfield sites  

Reality: Digital and analytics transformation does require new equipment – but this does not always mean brand-new, “greenfield” sites capable of fully automated “lights out” manufacturing. Digital and analytics creates a lot of value by improving brownfield sites and by connecting and optimizing existing infrastructure and augmenting it with new, e.g., sensors, apps, and connectivity to collect and convert data into insights.  

A case in point: Procter & Gamble leveraged digital and analytics to reduce rework and complaints by 50%, lower the incidence of scrap generation and quality inspections, and cut throughput time by 24 hours at its 150-year-old Rakona plant in the Czech Republic. It rolled out an in-process quality-control system in its legacy systems to address issues in manual sampling and subsequent delays in product releases. Sensors now monitor product characteristics and produce data to help operators determine batch quality for release or stop the line if a deviation occurs.  

Myth: You need to be 100% ready to go digital  

Reality: Many companies spend too much time planning. Setting up a digital TO to oversee pilots and guide the organization through a “fail fast, succeed big” process accelerates implementation. The TO is the execution engine to achieve scale through proven methodologies, best practices, and a holistic vision. Agile methods of working enable companies to move in sprints, iterate fast and learn from their failures.  

A case in point: Petkim embarked on its digital and analytics transformation journey with just one digital and analytics use case that delivered substantial value. It proved the merit of digital and analytics and mobilized the organization to pursue it at scale.  

Myth: Continuous improvement through digital and analytics is expensive  

Reality: Traditional methodologies are often too incremental to deal with the challenges and threats manufacturers face in the global digital economy. Digital and analytics transformations use big data, real-time insights and agile ways of thinking and working to improve operations continuously. WEF lighthouses show that the savings unlocked by digital and analytics initiatives far outweigh the costs; optimizing rather than replacing existing infrastructure can achieve 2-4x RoI compared to 10-50% for capex initiatives. As transformations evolve and escalate, savings and bottom-line impact grow.  

A case in point: A global electronics manufacturer made a limited investment in digital and analytics to obtain a holistic view of operations across dozens of production facilities and more than 25,000 employees. Replacing the production planning system would have been a massive, time-consuming and costly undertaking. Instead, the company installed sensors on its production lines to capture critical real-time data, such as equipment efficiency and line productivity. An IIoT platform processes the data, serving as a remote performance dashboard and providing real-time access across all facilities. This transparency enabled the manufacturer to bring all its facilities up to the same level of productivity—and raise productivity by 10+% in the first year.  

Myth: Digital and analytics is not feasible in emerging economies  

Reality: Conversely, companies in developing regions are often well positioned to implement digital and analytics because they are less encumbered with brownfield facilities and legacy systems – as witnessed by WEF lighthouses in emerging economies. Of the 29 new lighthouses added to the WEF Global Lighthouse Network in 2022, 23 are in emerging economies – China (13), India (5), Brazil (2), Thailand, Philippines and Turkey (1 each) – and 22 are in Asia.  

A case in point: Tata Steel set up a greenfield plant in Kalinganagar, India, to run at full capacity in far less time than the industry standard. It invested in digital and analytics solutions and to develop the capabilities of its relatively inexperienced team. Applying advanced analytics at scale has enhanced plant performance by improving raw material usage, uptime, and quality. 

The Way Forward 

Indian MSMEs are likely to drive rapid growth in the country over the next decade and must act now to resolve critical challenges around cost of production, supply chain, skilled labour and sustainability.  

Digital and analytics could unlock the next S-curve of productivity for MSMEs by enabling MSME’s to tackle some of these challenges and creating impact across the value chain. It isn’t easy to embark on a digital and analytics transformation journey, but those players who have bet on digital and analytics have achieved real impact and offer a transformation blueprint for others in the industry.  

Digital and analytics is top-of-mind for companies across all sectors, with 90% of CEOs believing that it will transform their industry. Yet only ~17% of CEOs have sponsored and launched digital and analytics initiatives. What is holding them back? Surveys and experience working with manufacturers across the globe show that there is still a lot of misunderstanding about what digital and analytics is and how it works. The myths are far from reality. 

Myth: Digital and analytics is only about data collection and dashboards  

Reality: Digital and analytics transformations rethink how to increase, improve and accelerate value creation end-to-end. They create value by optimizing processes, deploying new technologies such as AI/ML, natural language processing, visual analytics, and moving beyond descriptive analytics (dashboards) to prescriptive and predictive analytics (AI/ML models). A full understanding of the business and its pain points is required to enable a company to invest in digital and analytics where it matters.  

Myth: Digital and analytics will displace workers  

Reality: The new technologies used in digital and analytics transformations can create jobs and eliminate repetitive tasks, enabling the workforce to learn new skills. However, companies need to retrain their people and invest in new capabilities, e.g., data scientists, data engineers/architects, technology leaders, digital and analytics translators, and product owners. Some companies are partnering with post-secondary educational institutions to develop training programs and new ways of working.  

A case in point: Instead of hiring pure data scientists, Tata Steel Europe trains its domain experts in data science at its Advanced Analytics Academy. At its IJmuiden plant in the Netherlands, Tata built the digital skills of its site team to improve productivity, cost and quality.  

Myth: Digital and analytics requires greenfield sites  

Reality: Digital and analytics transformation does require new equipment – but this does not always mean brand-new, “greenfield” sites capable of fully automated “lights out” manufacturing. Digital and analytics creates a lot of value by improving brownfield sites and by connecting and optimizing existing infrastructure and augmenting it with new, e.g., sensors, apps, and connectivity to collect and convert data into insights.  

A case in point: Procter & Gamble leveraged digital and analytics to reduce rework and complaints by 50%, lower the incidence of scrap generation and quality inspections, and cut throughput time by 24 hours at its 150-year-old Rakona plant in the Czech Republic. It rolled out an in-process quality-control system in its legacy systems to address issues in manual sampling and subsequent delays in product releases. Sensors now monitor product characteristics and produce data to help operators determine batch quality for release or stop the line if a deviation occurs.  

Myth: You need to be 100% ready to go digital  

Reality: Many companies spend too much time planning. Setting up a digital TO to oversee pilots and guide the organization through a “fail fast, succeed big” process accelerates implementation. The TO is the execution engine to achieve scale through proven methodologies, best practices, and a holistic vision. Agile methods of working enable companies to move in sprints, iterate fast and learn from their failures.  

A case in point: Petkim embarked on its digital and analytics transformation journey with just one digital and analytics use case that delivered substantial value. It proved the merit of digital and analytics and mobilized the organization to pursue it at scale.  

Myth: Continuous improvement through digital and analytics is expensive  

Reality: Traditional methodologies are often too incremental to deal with the challenges and threats manufacturers face in the global digital economy. Digital and analytics transformations use big data, real-time insights and agile ways of thinking and working to improve operations continuously. WEF lighthouses show that the savings unlocked by digital and analytics initiatives far outweigh the costs; optimizing rather than replacing existing infrastructure can achieve 2-4x RoI compared to 10-50% for capex initiatives. As transformations evolve and escalate, savings and bottom-line impact grow.  

A case in point: A global electronics manufacturer made a limited investment in digital and analytics to obtain a holistic view of operations across dozens of production facilities and more than 25,000 employees. Replacing the production planning system would have been a massive, time-consuming and costly undertaking. Instead, the company installed sensors on its production lines to capture critical real-time data, such as equipment efficiency and line productivity. An IIoT platform processes the data, serving as a remote performance dashboard and providing real-time access across all facilities. This transparency enabled the manufacturer to bring all its facilities up to the same level of productivity—and raise productivity by 10+% in the first year.  

Myth: Digital and analytics is not feasible in emerging economies  

Reality: Conversely, companies in developing regions are often well positioned to implement digital and analytics because they are less encumbered with brownfield facilities and legacy systems – as witnessed by WEF lighthouses in emerging economies. Of the 29 new lighthouses added to the WEF Global Lighthouse Network in 2022, 23 are in emerging economies – China (13), India (5), Brazil (2), Thailand, Philippines and Turkey (1 each) – and 22 are in Asia.  

A case in point: Tata Steel set up a greenfield plant in Kalinganagar, India, to run at full capacity in far less time than the industry standard. It invested in digital and analytics solutions and to develop the capabilities of its relatively inexperienced team. Applying advanced analytics at scale has enhanced plant performance by improving raw material usage, uptime, and quality. 

The Way Forward 

Indian MSMEs are likely to drive rapid growth in the country over the next decade and must act now to resolve critical challenges around cost of production, supply chain, skilled labour and sustainability.  

Digital and analytics could unlock the next S-curve of productivity for MSMEs by enabling MSME’s to tackle some of these challenges and creating impact across the value chain. It isn’t easy to embark on a digital and analytics transformation journey, but those players who have bet on digital and analytics have achieved real impact and offer a transformation blueprint for others in the industry.  

This article is from the latest CII Report on “Transforming India’s chemical sector through digital and analytics”.