Data Science in Manufacturing

Data Science in Manufacturing

Manufacturing, simply put, is the act of transforming raw materials into finished goods on a large scale using labor, tools, machines, chemical/biological processes, or formulation. A necessity in today’s consumerist world where a man’s needs far exceed just food and shelter. The motive of manufacturers in a free market or mixed economy is to fulfill these needs at the lowest possible price, enabling the consumer to cater to more of his needs, thus, improving revenue. Optimizing manufacturing operations, thus, not only helps the business owner make more profit, but leads to better productivity. Data Science in manufacturing can achieve all the above mentioned goals.

Metrics of Manufacturing Operations

“If you can’t describe what you are doing as a process, you don’t know what you’re doing.” – William Edward Deming

Before we understand the importance of data science in manufacturing, it is imperative that we know the metrics of a manufacturing unit used to gauge its performance.

There are four dimensions of operational performance:

  • Cost – Indicates the efficiency of the operation
  • Quality – This is further broken into:
    • Performance Quality – measures how good of a product or service we provide
    • Conformance Quality – captures to what extent we’re able to deliver on the promise that we have made to the customer
  • Variety – measures the flexibility of an operation to provide goods and services to a heterogeneous customer base
  • Time – Our ability to provide a quick response to demand

These four dimensions are important for two reasons. First of all, they are the goals that we strive for in an operation. And so they will guide what type of performance measures we track. And then, they’re really also at the heart of defining the business strategy. These four dimensions give us the opportunity to differentiate our operations from others, thereby potentially providing us with a competitive advantage.

Use of Analytics in Manufacturing

Since the advent of the Lean manufacturing paradigm developed at Toyota in 1988, factory floors have never been the same. Essentially, lean manufacturing is a systematic method to reduce waste in a manufacturing system. The goals are:

  • Improve quality: To stay competitive in today’s marketplace, a company must understand its customers’ wants and needs and design processes to meet their expectations and requirements.
  • Eliminate waste: Waste is any activity that consumes time, resources, or space but does not add any value to the product or service.
  • Reduce time: Reducing the time it takes to finish an activity from start to finish is one of the most effective ways to eliminate waste and lower costs.
  • Reduce total costs: To minimize costs, a company must produce only to customer demand. Overproduction increases a company’s inventory costs because of storage needs.

Working from the perspective of the client who consumes a product or service, “value” is any action or process that a customer would be willing to pay for. Essentially, lean is centered on making obvious what adds value by reducing everything else.


In today’s world, Lean manufacturing is Data-Driven manufacturing and is utilized by every manufacturer in some form or the other. The ability to anticipate downtime gives managers the opportunity to plan ahead so the capacity of all machines and labor can be utilized to the maximum. This downtime could be due to a bottleneck resource or due to falling in market demand, both of which could be identified beforehand with the employment of predictive analytics. Predicting expected sales also helps in directing productivity towards parts that are going to be in high demand.

Furthermore, continuously analyzing and monitoring the performance metrics of resources, lines, vendors and plants can help Product managers with optimizing operations, factory scheduling, maintenance, and labor deployment. Data on the usage of the finished product by customers can give key insights into improving design and manufacturing as well.

Evolution of Manufacturing


With the boom of information technology, various advancements have revolutionized the extent to which data could be leveraged for operational efficiency. Some of these advancements include:

  • The number of sensors per machine has increased which has automated a lot of manual data gathering and provides previously unavailable data.
  • These larger datasets are easier to get from the machine to a central database because the machine is connected to the network.
  • These larger datasets are being aggregated by the companies that make the machines allowing for anonymous cross-company sharing of machine performance data.
  • High-performance computing platforms like Spark and cloud services like Amazon or Microsoft’s Azure allow businesses of any size to store, mine, and analyze these large datasets.

Present-Day Data Science in Manufacturing Analytics Scenario in the Industry

The aforementioned technological advancements have led to the amalgamation of production systems with planning toolkits, forecasting, real-time data streams of sensor data from plant floors, and much more. Some of the most beneficial innovations have led to:

  • Industrial Internet of Things: Cost-effective high-performance sensors are now utilized to gather data for the purpose of quality assurance. Sensors these days can be used to assimilate high velocity and high volume data, tracking various metrics previously thought impossible without slowing production. For example, Jet engine manufacturers utilize more than 50,000 sensors on a single engine that measure temperature, pressure, and vibrations on various parts of the engine to measure possible failure points in the design that lead to insights about the product’s durability and longevity. Thus, it gives them the ability to calculate the profitable warranty period they can offer to their customers as well as predict maintenance costs.
  • Retrace problems for better resolution: Records of common failures, either from customers or from quality checks, can help identify design flaws in products.
  • Supplier Selection: Analysis of Defect Rates and on-time delivery helps in selecting suppliers and deciding on order sizes which optimize inventory stock, cash flow, and supplier fault risk mitigation
  • Increase ROI on data stored: Almost every aspect of the business can benefit from cutting-edge data analysis such as operations, finance, human resources, marketing, sales, customer service et cetera. This makes the returns on adding a new stream of data ingestion almost intangible as it benefits more than one business function leading to a compounded improvement in company performance.

The Big Data era has only just begun. The mathematical tools and algorithms designed to explain the universe have always remained in textbooks, and for decades only ever mentioned in academia. With revolutionary advancements in data storage and computation technology, the time has come when they can finally be put to the test in real-world optimization problems and make their value known.

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