How is Data Science Shaping the Manufacturing Industry?

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How is Data Science Shaping the Manufacturing Industry?

Over the years, the manufacturing industry has gone through a massive transformation. Since its inception, there have been four revolutions in this industry, and now we are seeing a technology revolution that is designated to transform traditional manufacturing practices, and cut hidden costs with ongoing automation by intelligent technology. As technology has touched and evolved every industry in some manner, manufacturing is no exception. The Big Data Analytics services in the Manufacturing Industry is expected to grow rapidly up to USD 4.55 billion by 2025, with 30.9% CAGR between 2020 – 2025.

What is Data Science?

Data Science is the modern innovation tool in the arsenal for industries. It is the mashup of huge volume, variety, and velocity of data from which insights and intel can be derived in a meaningful and methodical manner. It’s a multidisciplinary field where the cocktail of statistics, math, and business acumen creates multiple variations of data visualization. With an appropriate & vast amount of data fed into the data model, it can cultivate powerful insights for any industry, especially manufacturing. And that’s where manufacturing analytics solutions steps in!

What is the impact of Data Science in Manufacturing?

Data Science for modern manufacturing is the backbone for the decision-making process. Within a very short period, Data analytics has become an integral part of the manufacturing industry. As per Forrester, data-driven organizations have gone ahead of the curve from their traditional competitors and on average, report around 30% growth annually along with better profitability and client retention and acquisition. In 2019, in the US alone Data Analytics for manufacturing was clocking a market share of 904 million USD. According to Grand View Research, by 2025, global smart manufacturing is poised to be estimated approx. USD 395.24 billion, with a CAGR of 10.7%. 

What are the opportunities that data science and ML provide in manufacturing?

Data Science and ML go hand in hand in manufacturing industry transformation. Data Analytics services provide ample benefits in manufacturing.  A few of them are: 

  • Quality assurance, performance, and loophole tracking
  • Predictive and conditional maintenance of machines and tools
  • Demand and throughput forecasting
  • Supply chain Optimization
  • Ongoing Automation and new and innovative designing 
  • product development cycles and application and testing of new production techniques
  • Sustainability and achieving energy efficiency

Future Scope for Data Science in Manufacturing

The journey to embracing data science in the industry has just begun. Data Science for manufacturing will grow exponentially over the next 5 years. According to PwC, data science applications and utility will grow double-digit on an average in all the respective sub-fields of manufacturing – be it predictive maintenance, integrated planning, or transfer of production parameters. What we are witnessing here is a full-scale industrial revolution in digital terms.

Predicting the future of ML technologies in the manufacturing industry
Source: Mobidev

Data Science Applications in the Manufacturing Industry

Data Science has brought a paradigm shift in the manufacturing industry. Data-driven manufacturing is the next pivotal catalyst of change in manufacturing operations to drive more efficiency and responsiveness in the production systems. Manufacturers finally adapted to taking data-driven decisions in a productive and meaningful manner.

Predictive Analytics for Real-Time Performance & Quality Check

For manufacturers, the capability of generating a quick and timely response to issues has a direct impact on the downtime cost and productivity. Predictive analysis in the manufacturing industry can be leveraged in multiple ways – monitoring machine performance, prior identification of machine downtime, accurate prediction of the nature of yield gain, scrap reduction, and also any considerable influence of external change. Predictive analytics takes care of everything!

One of the most important KPI or Key Performance Indicators where data science and analysis impact heavily is the Overall Equipment Effectiveness or OEE. It collects data from all machines and operators to create a set of KPIs. In case of any discrepancy or deviation, it enables the management to do root cause analysis of downtime and scrap and its impact on productivity. Data Science as a unit offers a proactive and responsive approach to tackle machine optimization and maintenance, cost management, and quality improvement.

Predictive Maintenance Along With Fault Prediction

Unplanned downtime is the biggest threat to the manufacturing industry. Machine breakdown and unplanned downtime are the biggest contributors to increasing overhead costs in manufacturing. On average, over the last 3 years, it has cost businesses approximately a staggering $2 million! The biggest headache for the management is, it has maintained its pace with time and grown significantly.

For example, in 2014, downtime cost on average used to be $164,000 per hour which went up by 59% in just 2 years to $260,000 per hour. The industry has incorporated new-age technologies such as prior detection and condition-oriented monitoring with the help of techniques like PCA-T2, autoencoders, neural networks, regression models, survival analysis, classification models and one-class SVM. It helps in the following – 

  • Prior Detection of anomalies
  • Prior Failure mode detection
  • Accurate Time to Failure (TTF) detection
  • Optimal maintenance time detection

Price Optimization

In this fiercely competitive market, every organization wants to stay ahead of its competitors. The manufacturing industry is also crawling with competition from domestic and foreign entities. In such a case, to stay ahead of the game, data science becomes a crucial tool and rather an element of surprise for the manufacturers. What used to be a completely manual, boring, and labor-intensive process, can be done with a snap of a finger. From raw material purchase to distribution cost, all the factors are taken into consideration while calculating the final cost of the product. The cost also needs to look attractive to buyers to be successful. 

In such a volatile scenario, finding the best possible quotation is the game-changer, and data science in manufacturing does the trick. Data science tools perform analysis and aggregation of data, costing from internal sources, market competitors into main consideration and model around product efficiency and maximization of profit to provide the best possible price in market attractive terms. Change in consumer behavior, fluctuations in internal and external factors, along with present time market competition all are the key factors of data collection for price optimization. 

Supply Chain Optimization

The supply chain is the backbone for the manufacturing industry and managing it is a herculean task. With the modern-day complexity, it becomes even tougher due to increased complexity and unpredictability. Data scientists here come to the rescue. From collating the inputs from shipping and fuel expenses, the difference in pricing to scarcity in the market to local tariffs all the pointers are thoroughly gathered, segregated, and analyzed with the right data science model.

Data Science along with Machine learning and Artificial Intelligence empowers supply chain management in an intelligent manner. Modern-day supply chain management systems powered by Machine Learning, can analyze vast sets of data from multiple fields such as – material inventory, work-in-processes, present market trends, inbound shipments, consumer sentiments, and behavior along with weather forecasts and take data-oriented decisions to find out the optimal solutions. 

Data Science Can Be Leveraged in the Following Areas for Supply Chain Management

Demand Forecasting

Manufacturing revolves around the Just-in-Time (JIT) method to produce the right quantity at the right time. Demand forecasting is a crucial business strategy for predicting the business growth momentum. Time-series analysis, NLP techniques, and ML forecasting models are used explicitly to understand and analyze customer behavior and sentiments, buying patterns, and market trends. Manufacturers empowered with accurate data, can make data-driven forecasting to design new products, do logistics optimizations, and improve manufacturing processes. 

Adidas has leveraged ML algorithms to have accurate demand forecasting. Understanding the buyers’ sentiments and buying patterns, the organization has optimized manufacturing and delivery parts effectively. 

Transportation & Logistic Route Optimization

With deep learning and machine learning algorithms, the manufacturers can assess all the shipments and deliverables and can find out the fastest possible route for logistics operations.

Warehouse control

With a data science-based system, stock shortages and excesses can be detected seamlessly. The system can automatically track the stock at any point of time passing through the supply chain.

HR Planning and Supply Chain Security

With machine learning algorithms, the complete production data can be collected and analyzed to accurately understand the requirement of employees for a certain task.  It gives the management the accurate prediction for human resource planning and budget allocation.

Also, the same data can be assessed to understand what type of information is needed for each situation at a specified time frame to analyze the risk factors. With such cognitive ability, a supply chain management system can ensure data security and the prevention of any hacking. 

Case Studies of Applications of Data Science for Modern Manufacturing

The use of data analytics consulting embedded with modern technology has provided incredible benefits to multiple manufacturing companies to streamline their manufacturing processes or supply chain operations. Few such examples of data science applications in the manufacturing industry are –

Rolls Royce

Rolls Royce is famous worldwide for its quest for excellence. It uses data analytics extensively to analyze terabytes of data for product designing and prototype testing. They can accurately understand from the data pattern whether a prototype is at per the excellence yardstick or not.


BMW is a world-renowned name in the car manufacturing industry. They use data science and analytics extensively to understand and analyze loopholes and faults in prototype designing. In many cases, they have saved millions of dollars by identifying the glitches at the right time and rectifying it before the final product design. 

Pharma Industry

The Pharma industry goes through multiple validations and quality checks to ensure the efficacy and quality of the manufactured drugs. With data science and analytics being put in place, 9 parameters have been introduced and that has improved the yield of vaccines by 50%!

What Is Big Data in the Context of Manufacturing?

The manufacturing industry is going through a complete technology overhaul right now. Demand forecasting or supply chain optimization, with data analytics services, manufacturing companies are making the decision in a data-driven manner and driving the efficiency to a new height. In addition, incorporating big data has helped maintain quality metrics and KPIs for better performance measurement and identification of future potential risks.  Manufacturers now achieve their long-desired business goals with the least amount of money and time spent. 


The manufacturing industry is undergoing a significant change and revamping process due to the initiation of big data and data analytics. In this article, we have portrayed the importance of data science in the manufacturing industry and mentioned a few use cases from usage perspectives. It is highly recommended that manufacturing companies should connect with a data analytics consulting company for integrating the above-mentioned points in their manufacturing operations to leverage the benefits of data-driven processes. 

Data Science & Analytics

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