One of the biggest sectors that leverages Data Science and functions on its basis is Manufacturing. Whether you are interested in Q&A or the production department, Data Science in Manufacturing is something you will definitely deal with.
Data Science in Manufacturing: Is it Worth it?
Shunning the age-old traditions and adopting techno-friendly trends, the Manufacturing sector is on the path to major transformation.
Whether the aim is to speed up the processes or automate large-scale ones, Data Science is set to revolutionize the sector.
Changes are already evident. Picture this-
According to a report, if the quality testing process with the use of machine learning, is automated, the rate of defect detection increases by up to 90%.
How Manufacturing Sector is Flourishing with Data Science?
Together with focusing on the defects and their solutions, the manufacturing sector can now focus on increasing the targeted production.
Who knows that with the use of Data Science and advanced machinery you can employ such tools that can direct unused heat (generated in your machine plant) and put it to some use?
For this, you need to have real-time data that warns you of potential dangers and keeps you updated for any required changes.
Now, what are these applications that show the might of Data Science in the Manufacturing sector?
1. Demand Forecasting and Inventory Management
Sharing a close bond with inventory management, the process of demand forecasting is a complex one. It includes data analysis and massive inputs from specialists and accountants. To understand this association, picture this fact- this is no news that the supply chain’s data is used in demand forecasting.
Demand forecasting is crucial as it allows you to keep a check on your inventory and eliminate the chance of storing useless products in large quantities.
Data thus collected through online inventory management software proves to be useful in an in-depth analysis.
Planning thus comes handy…
Historical data or dark data is hard to be analyzed but the data that’s continuously being generated and updated can be easily analyzed; data for demand forecasting is one such data.
One of the most effective and worthy benefits of Data Science in this sphere is maintaining the optimum balance in demand and supply. Forecasting makes this possible and leaves no room for the demand and supply gap.
2. Lowering Supply Chain Risk
According to a report, one-third of Manufacturing supply chains will use analytics-driven cognitive capabilities by 2020.
On the basis of the above research, it can be safely said that though risky, supply chain risk is of strategic priority. But these risks can be reduced to a greater extent with the use of data-based tools and processes.
With the introduction of cloud-based data networks in various processes like logistics management, supply chain planning and partner association are expected to be done in a real-time environment. This will be possible through wireless technologies and sensors that record data for each stage in a product life cycle.
Computer modeling will generate reports on crucial points like order’s shipping and receiving time and recognizing risks and lag in the fulfillment process.
3. Predictive Analytics
The process of analyzing current data to predict and avoid issues is termed as predictive analytics.
Manufacturers aim at regulating various processes in an organization and ensuring their robust performance. With predictive analytics, the manufacturers can come up with ways to restrict issues, surpass obstructions or prevent these situations.
Wastes, in terms of time, logistics, overproduction etc. can be effectively dealt with.In this case, the mighty data will not only reduce costs in various stages of production but also increase efficiency and production.
As a manufacturer, you will have enough room to innovate and scale your business higher.
4. Supply Chain Optimization
Regulating risk in the supply chain is an intricate process.
Data Science makes use of input values like tariffs, local weather, shipping costs and fuel to manage different data points. Expect to save more than incur huge costs through a Data Science model that forecasts changes in the market and thus reduces risk.
Do you know what forms the base of supply chain management?
Forecasting serves as a binding link that ensures all the required material and parts are manufactured, delivered, stocked and kept ready to be assembled.
Data scientists are entrusted with the responsibility to rule out the risk of stock scarcity or late delivery.
This saves the sudden increase in expenditure and keeps the costs in check.
5. Price Optimization
Data science plays a key role in price optimization.
As the prices may rise and fall anytime, depending on many factors, manufacturers rely on Data Science to decide the best price. After all, this price will define profit.
A global marketplace for goods and services is taken into consideration to determine the best-suited price of a good or service. It will indeed be a good bet to know the expected change in industry pricing, beforehand. This will be a good option for informed manufacturers to maximize their profit.
A supply chain management that is data-driven, uses the same information to realize the maximum profit for their business.
6. Fault Prediction and Preventive Maintenance
When will a machine breakdown?
This forecast is known as fault prediction and the steps taken to either prevent this or decrease such instances is known as preventive maintenance.
The availability of a number of predictive modeling techniques has made this forecast possible. Any machinery that has been in use for many years, is most likely to wear and tear, fail or stop all of a sudden.
Preventive maintenance is the key here. It will not only make the forecast but also provide sufficient time to repair it or install a new one. When a manufacturer has timely information about future breakdown in machine, a significant amount of failures and delays can be avoided.
Data science aids and boosts preventive maintenance.
7. Perfecting Quality as a Competitive Advantage
As many as 92% manufacturers are of the opinion that a quality product is something that defines success in their customer’s eyes. With an aim to enhance product quality and reduce waste, the manufacturers are always in search of techniques to improve quality and efficiency.
In the coming years, data analytics in manufacturing will combine compliance management and quality. This will make room for the manufacturers to adopt a predictive approach towards quality, instead of a reactive approach.
Manufacturers will continually invest at the machine level in capturing data and then storing and analyzing this data in the integrated systems of their organization.
The introduction of data analytics in Manufacturing will make the assessment of quality affecting processes, workflows and factors, easy.
Precise predictions will not be a thing of the past because of the extensive use of data analytics in Manufacturing. Quality will improve and so will the complete operations of an organization.
8. Product Development
Targeted product development, bridging the demand-supply gap, meeting specific customer demands, etc. are the results of Data Science in Manufacturing.
For example, an Electronics Manufacturing company reviews its sales and market demands and finds that the demand for 56-inch television has dipped. The study also reveals that customers wanted a television with an even bigger screen. The manufacturer, after carefully studying the demand, rolled out 75-inch television.
This scenario reveals that the data they collected was useful in reviewing the existing products and rolling out new ones.
When a new product is developed after careful study of data, manufacturers get increased customer value and decrease the risk that comes with rolling out a new product in the market.
The data collected through different mediums and shared with product marketers can work wonders when generating ideas. The crux of the matter is that with the use of Data Science in manufacturing, a product that is both profitable for the business and useful for the customer can be developed.
9. Post-sales Service Improvement
Every Manufacturing attempt is customer-focused.
Manufacturers have realized that post-sales is also important as pre or mid sales. This will directly affect the company’s revenue. According to a study, 27% of the total revenue of Manufacturing organizations was from service. Another report detailed that post-sales service could result in 39% average gross revenue.
Top-quality post-sales service will automatically result in high monetary gains. Predictive analytics will come into the picture and help the manufacturers in optimizing the post-sales service.
So, next time you take your car for service and the dealer is falling short on the required auto-part, his system will send a trigger and the dealer will immediately order it. This means your visit to the dealer will not go in vain.
Predictive analytics will improve customer loyalty; optimally utilize customer’s time and decrease costs incurred.
The Manufacturing sector is gaining in sales and revenues with the introduction, adoption and implementation of Data Science.
Be it predictive analytics in price customization or supply chain forecasting, the Manufacturing sector is progressing and adopting the latest technologies.
Here is a fact to show the might of Data Science in Manufacturing:
According to a McKinsey report, machine learning will decrease supply chain forecasting errors by 50% and bring down lost sales by 60% by ensuring that better products are available.