Scope of Business Analytics in Manufacturing

Alka Singh
5 min readSep 9, 2018


Manufacturing Industry is amongst the most data-intensive business vertical today. The industry is evolving and leveraging the available tools and technology like IIoT and Cloud that allow them to connect with the supply chain and customers like never before. Each and every connection and communication are delivering access to massive volumes of data, i.e., big data. Big data uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.

I cannot always control what goes on outside.
But I can always control what goes on inside

Why Data Analytics Needed for Manufacturing Industry?

Manufacturing is predominantly process-driven industry. These various processes involved in manufacturing create innumerable data-points that can be used for effective reporting and decision support systems. Historically, manual analysis (audits and statistical reports) was relied upon by decision-makers for various purposes. Today, business analysts use software and tools for real-time data analysis and historical trend help in making a decision for an organization.


The manufacturing sector, like any other industry, also has its share of challenges that it needs to contend with — day-in and day-out. Although not all the problems could be addressed easily, a good number of those may be overcome with due application of technology wherever possible.

The advantage of Data Analytics

Big data is a collection of structured and unstructured data having different formats, being collected from several sources using a different type of database. Business analysts and policymakers perform data mining to get the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the data can be monitored at a glance.

Big data analysis is primarily used to find correlations, trends, outliers (anomalies), patterns, and business conditions in data. Below we run through a series of use cases where business analysis / visual analysis performs through historical data and in some cases real-time data to enables businesses to address the existing bottlenecks and achieve a competitive edge.

1. Improved Inventory Management

Pretty much every entity in manufacturing has inventory based on the nature of the business.

• Raw Materials
• Work-In-Progress (WIP)
• Finished Goods
• Packing Material, Etc

The amount of profit a business obtain is considerably based on how well the inventory is managed, from procurement of inventory to inventory control & warehouse management to the shipping of goods and finally the post-sales activities, such as customer service and returns management. Whether an inventory is located in one warehouse or multiple locations, businesses should keep a tight inventory control.

Data analytics allows you to understand how much inventory you have as well as understand trends in your use. Accurate inventory can enhance ordering and lower loss, making it possible to optimize your inventory expenses fully.

2. Optimized Staffing for Efficient Workforce Management

Resources, like inventory, can pose some challenges that impact the production, supply, and quality of the goods. A staff shortage implies longer hold up times and delayed production, delivery as well as might be compromised quality with the produced products. Being overstaffed means increased overhead.

Looking at historical data for resource planning and different seasons can make for better forecasting and scheduling of staff. Whereas, real-time data allows managers to move the right people to the right shifts and roles — even more efficiently than possible when analyzing past trends. With real-time data, managers have a birds-eye view of staffing across departments and functions, enabling rapid response to unexpected circumstances such as call-offs.

3. Enhanced Supply Chain Practices


Supply chain builds building blocks for manufacturers. Connecting people, process and supply chains provide end-to-end visibility and control over superior quality and optimized utilization of resources.

Big data of this particular business process helps here for predictive analysis to plan better and set the right expectations internally and externally. Real-time data analysis enables you to determine the materials required for a master production schedule, where you easily track when what and how much to buy and make.

With changing customer expectations, you can still deliver world-class performance over shorter order lead times, last-minute order changes, custom products orders, and perfect order deliveries. Connected supply chain practices influence your financial and operational plans thus enables growth and prioritizes investments.

4. More Effective Fault Prediction and Preventive Maintenance

Machines continuously generate data using sensors. Traditionally, this data was used mostly for signaling alerts to prevent misfortune. The rest of the data was ignored. Predictive analytics uses all the data the machine can ever generate. With big data tools, it is possible to extract essential correlations between fault diagnostics and operational parameters.

It provides a better understanding of the system’s complexities. The insight can help plan highly accurate predictive models to reduce or eliminate downtime from maintenance. For example: If a system has already crossed certain operational thresholds, there is not much lead time left to perform preventive maintenance. Predictive analytics here enable you to perform pre-emptive maintenance that helps reduce or eliminate catastrophic failures.

5. Cost Modelling and Forecasting


“What price should we quote our product to ensure profits and generate a winning bid?” Cost modeling and forecasting makes a difference in manufacturing a product. Cost modeling helps businesses to systematically capture all factor input costs including material, labor, production, administration, sales, and research and development essential in estimating the cost of the final manufactured product.

Cost modeling helps manufacturers to be confident in their ability to predict costs and enable them to get better at developing quotes for winning bids that ensure long-term profitability.

Pulling together all the data of every department costs including significant indexes, process parameters, etc. which is highly structured and help develop a useful cost model and forecast.



Alka Singh

Technical Writer & Content Strategist | Active Since 2011 | Worked at OnGraph | Working On |