COVID19: Top ROI Driven Analytics Use Cases In Manufacturing Domain Going Beyond IIoT

Manufacturing contributes about 16% to worldwide GDP and is one of the sectors which saw an early hit. As Q2 ends, the industry is coming back to 75% of previous year’s production levels. While oil & gas, garments and automobiles still are on the recovery path, there is a positive impact on pharma, medical equipment and bi-cycle industry. 

Digitization with Industry 4.0 transformation was on an accelerated path across the industry, but after the pandemic, there is expected a change in approach and possible speed up in adoption. Industry 4.0 has four key areas – 

  • Connectivity: Building the IIoT connection with machines and software-enabled with cloud 
  • Human-Machine Interaction, Use of VR, AR and Robotics for Automation
  • Advanced Engineering: Use of Additive Manufacturing and advanced project planning methods
  • Analytics & Intelligence: Smarter business and operational decisions enabled by data and algorithms

Manufacturing Industry is now looking at Digital Solutions to help them with challenges related to:

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  • Changing customer preferences and need for newer products
  • Managing supply chain bottlenecks
  • Efficient workforce planning and management
  • Responsive financial forecasting and cash management
  • Realtime visibility into production and supply chain
  • Effective negotiation with suppliers and risk mitigation

“We are relieved due to bounce back.  There is guarded optimism about future projection which depends on how customer sentiment moves. There are a vaccine upside and a second wave downside”

– CMO of Top Auto OEM

While fundamental technology is readily available, with travel restrictions and capex deferments, some projects are impacted, and business leaders are looking at initiatives which can give them ROI in less than a year; and this is where Analytics & AI can help. 

This article will talk about the top use cases beyond IIoT which can help companies get returns from their investments in less than a year. 

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Top 5 Analytics applications in Manufacturing: 

Efficiently implemented analytics solutions can help industries in not just cushioning the adverse impact, but also manifesting a turnaround by improving financials.

“Challenges related to Changing and Re-Changing Demand, disruptions in Sourcing, Operational challenges in managing a workforce and finally distribution to the consumers.”

Analytics has the potential to connect all the dots and prove to be a latent blessing. Companies can deploy analytics solutions; not just limited to the shop floor but to the entire value chain.

Some of the key areas where analytics can help and act as an enabler are:

  1. Demand & Sales Forecasting

Traditional methods of demand forecasting were crafted to be historical and intuition driven processes meant to help production planning. But the current situation of rapid change in demand and inability to predict or react to the change in consumer preferences has impacted production planning and resulted in models and products in demand to go out of stock.

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  • There are new product segments which have emerged with new customer needs(e.g. medical equipment, sanitizers)
  • In many areas, while overall sales volumes are reaching 80%, consumers have moved to economical alternatives (e.g. vehicles, clothes). 
  • Demand has emerged from new geographic segments

With the use of data gathered from customers’ decision-making process, from intent to buying from brick-and-click sources and use of strong demand forecasting models, the sales and demand forecasting accuracy can be improved and shifts in the patterns can be observed and quantified. This requires a strong customer graph by stitching the data signals from online and offline sources and putting it to use for prediction. Also, the running frequency of these models can be made easily up to daily refresh, giving greater visibility into intricate business trends.

  1. Spend Analytics

Procurement team’s primary objective in manufacturing is usually to optimize external spend with suppliers—commonly 40 to 70 percent of a company’s total cost— ( varies by industry) and realize a source of competitive advantage in terms of cost, quality, availability, and (increasingly) sustainability.

Spend Analytics can help in optimising procurement cycle; also digging into every dollar spent can help in Supplier Management, Strategic Global Sourcing of materials from sites with fewer operational restrictions and Commodity Management which in return will help achieve business objectives of cost reduction, effective risk management and cash flow improvements.

Immediate task for spend analytics in the pandemic is to provide transparency and insights into where cash is spent, and thus identify avenues for savings”

This begins with achieving visibility into external spend, making it easier for the organization to recognize opportunities to reduce spending across supply markets (even across multiple categories and subcategories), suppliers, locations, as well as volumes and prices.

Key challenges faced by procurement teams where Spend Analytics can help – 

  • Reduce supply chain disruptions due to logistics issues and suppliers business risk
  • Responsive supply chain with changing demand pattern
  • Minimize pricing impact due to tensions in commodity pricing affected by currency and oil price fluctuation

To execute Spend Analytics, data primarily is available in the company’s ERP systems and by better organizing it across the purchase life cycle, business leaders and department leaders can make data-driven decisions. 

“Spend analytics is one of the top use case for any manufacturing company and those who have adopted it have seen a 1-2% saving in purchasing, resulting in a strong bottom line impact.”

  1. Inventory & Logistics

The importance of inventory management and logistics for the manufacturing sector cannot be emphasized enough. Accurate assessment of inventory and efficient logistics framework can mean the difference between profit and loss as it impacts the costs, efficiency, utilization and quality. With the changing manufacturing ecosystem, executives are concerned about: 

  • Assessment with regards to the Suppliers, Products & Sites at Risk – vendors and suppliers are likely to struggle on the operational and financial front
  • Bullwhip Effect – inefficient demand forecasting due to fluctuations in demand magnifying the supply chain inefficiencies
  • Real time awareness about Supply Chain – especially for critical materials and components because of the weak links pertaining in the regions impacted by COVID

The projection for 2021 of global logistics market size is estimated to go down by over 10-15% as compared to the pre-COVID-19 estimation.”

Inventory Optimization helps in visibility into inventory value & quantity as per the stock type in the plant and its trends. The solution identifies and categorizes inventory in value that is slow-moving, non-moving and dead inventory which will in return help to reduce the cost and cash flow issues. Real-time Supply Chain Visibility can help in tracking of the products and machinery. This helps in analysing the price points and lead time changes also help in assessing the current supplier partnership.

While Inventory data requires traceability of raw material, work in progress and finished goods; this serves as a data source coupled with telemetry data from machines and transportation vehicles to build analytics solutions on top of it. 

  1. Financial Planning

For all organizations across industries, maintaining financial health is the top priority as it ensures a reasonable balance between cash flows, helps in the long-run survival and reduces the impact of uncertainties. The current Black Swan event has alerted CFOs and finance teams across all industries. Manufacturing sector experiencing the highest impact of the turmoil is looking for efficient ways to plan & control their finances that address:

  • Liquidity Planning – keeping into account customers demand, vendors supply and operational bottlenecks
  • Scenario Planning – building responsive, optimistic, pessimistic, current forecasts and budget cases for assessment
  • Zero based budgeting – helps scrutinize expenses in greater detail and manage them effectively keeping in mind top level strategies

Advanced Analytics in Financial Planning can help in addressing the changing budget requirements by understanding the sensitivity of various drivers- internal or external that are impacting the business plan. Automated Continuous Auditing & Monitoring can help establish a more risk-based control environment and increased value through improved financial and operating controls.

Cash Flow Analytics leveraging statistical modeling can help in real-time analysis of the factors contributing towards assessing financial stability and can help visualize the KPIs to track the growth/degrowth of the organization.

  1. People Analytics

While Manufacturing is moving towards higher degree of automation, the need for strong talent at shop floor & top floor with appropriate digital skills to handle the complex processes and machinery is increasing in both white and blue-collar jobs. 

Key Challenges faced by organizations during this time where Analytics can help:

  • Workforce health risk management by effective contact tracing 
  • Effective workforce in every shift matching the demand and availability
  • Effective utilization of resources both in direct and indirect workforce matching the demand
  • Reskilling in digital & analytical skills to improve problem solving & provide real time visibility into operations 

Workforce data that is available in HR and ERP systems complemented with the augmented systems of contact tracing can enable strong People Analytics solutions. 

In Closing …

With the pandemic, industries across all sectors are going through a shift – with focus on adopting new technologies and solutions to better position themselves than the competitors and there will be a faster adoption rate of Industry 4.0 technology workstreams – especially AI & Analytics across the board.  

“IDC predicts that by 2025 there will be 55.9B connected devices worldwide, 75% of which will be connected to an IoT platform. IDC estimates data generated from connected IoT devices to be 79.4 ZB by 2025”

With the adoption of Industrial Internet of Things, there will be zettabytes data getting generated across the enterprise from traditional and IoT systems. With the new normal, business leaders are expected to take a business impact-driven approach to look at each business case with clear ROI and a balanced approach to unlock business value. 

In the next article, we will talk about how IIoT Analytics use cases can bring transformations in the sector.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Saurabh Agrawal
Saurabh Agrawal is currently the “Head – Digital & Analytics” transformation initiatives at Motherson Group ( ) in Technology firm MothersonSumi INfotech & Designs Limited ( As USD 11.7+ billion (approx.) Motherson group is leading Auto component manufacturing company with over 270+ factories across the world, his charter is to drive projects across Manufacturing process like operations, procurement and audit analytics leveraging IOT, real time and cloud. Before Motherson he has worked in American Express and Tata iQ where he has driven marketing and digital analytics transformations. Saurabh is an MBA from IIFT Delhi and Mechanical Engineering from Delhi College of Engineering.

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