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Data Engineering and Data Science for the Manufacturing Industry


The rise of the digital age indicates a potential boost for the applications of data engineering and data science in most industrial areas. Especially in the manufacturing industry, data engineering technologies and tools based on data science, including data warehousing, are being employed widely.

Despite the current slowdown in the economy, the manufacturing industry is preparing itself for the future. The industry is embracing data engineering and data science processes, mainly for reducing risks and maximizing profits. Evidently, the coming years will indicate the survival to revival journey of the manufacturing landscape with ongoing rise in adoption of next-generation data science technologies and data engineering equipment.

While the global socioeconomic environment is currently going through a difficult patch, most businesses – including the manufacturing industry – are struggling to amp up the operations. However, once the negative impacts of this pandemic will begin to ebb and the economy will start to gain ground, the manufacturing industry will be among the first industries to witness an upward spiral.

A majority of global Original Equipment Manufacturers (OEMs) have been scrambling to find alternative solutions to traditional manufacturing supply chains and speed up the production cycles. In order to make the best out of this situation, manufacturing businesses will soon have to equip themselves with advanced data engineering and data science tools, such as data warehousing, to thrive in the new decade of the information age.

With the help of data engineering and advanced data science techniques, the manufacturing industry can attain significant improvements in its operations such as maintenance, optimization of inventories, and management of complex supply chains. This article further highlights seven most prominent areas where the future of data engineering and data science will play a workhorse of the industry in the coming years.

1. Data Science to Make Preventive and Predictive Maintenance More Efficient

Cutting manufacturing costs will remain the prime focus for businesses, and unplanned downtimes are one of the major contributors to manufacturing overhead costs. To counter this, OEMs are implementing data engineering and data science technologies in applications such as condition monitoring and preventive/predictive maintenance.

Various data science tools are used to procure data from data warehouses where sensor data is stored by data engineers. These tools can help in preventing any critical failure and improving asset management by inspection and servicing of assets at regular intervals.

Preventive and predictive maintenance mechanisms can further be made more efficient by eliminating unnecessary timely inspections and prompting interventions only when necessary. Condition-based or predictive maintenance techniques backed with data engineering technologies support continuous monitoring and analysis of sensor data to detect potential failures. As a result, data engineering and data science will enable the manufacturing industry to improve maintenance and reduce manufacturing costs due to unplanned shutdowns.

2. Smart Manufacturing to Get a Push with Real-Time Data Analytics

Achieving real-time awareness in manufacturing processes with data science tools and technologies can boost the pace of operations and improve productivity. Real-time monitoring is emerging as an indispensable tool for reducing costs and troubleshooting product quality problems. This has laid the foundation for a responsive, proactive system wherein issues are resolved quickly, thereby preventing costly downtimes.

Real-time monitoring can be achieved through advanced data engineering and data science technologies, which ultimately can provide better and unique insights that are useful in determining the performance and lifespan of assets. Also, with the ability to respond quickly, data engineering and data science can also enhance overall asset management.

3. Data-Driven Demand Forecasting and Inventory Optimization to Get More Accurate

The advent of data engineering and data science in the manufacturing industry will prove immensely helpful in organizing the inventory better by eliminating the storage of huge amounts of unnecessary data. Organized data warehouses help in speeding up order fulfilment by preventing both – shortage and overproduction of goods.

By taking into account numerous factors like the economy, market, availability of raw material, etc, data engineering and data science technologies can help manufacturing businesses to forecast future changes in the demand of modern consumers. After studying these forecasts and analyzing massive archives of data, strategies can be devised to improve the accuracy of inventory maintenance, thereby enhancing the productivity of manufacturing processes.

4. Data Science & Data Engineering to Reshape Price Optimization

In the coming years, setting the right price of consumer goods will emerge as a prime challenge for manufacturing businesses after the negative impacts of this pandemic begin to recede. In the digital age, comparing prices will only get simpler for consumers, and this makes it inevitable for businesses to set the right price of their goods in order to gain a significant market share.

OEMs need to devise a better pricing strategy to maximize profitability on each unit sold, and this will require data-based analytics tools that integrate advanced business intelligence technologies, such as Artificial Intelligence and Machine Learning. With the help of data engineering and data science tools, manufacturing companies can make the right move by taking into consideration various parameters such as market positioning, production costs and distribution costs, and market competition.

Furthermore, data technologies such as machine learning can also help in creating algorithms that can assess the impact of economic decisions on KPIs by learning patterns from data. This can ultimately lead to creating predictive pricing models to ensure that each product or service is offered in the current market condition at a competitive price. This way, data engineering and data science technologies can boost manufacturing businesses in the area of price optimization.

5. Data Analytics and Data Engineering to Simplify Complex Supply Chain Operations

Managing the supply chain can be quite a complicated task, attributing to its complexity and unpredictability. With an effective analysis of various important supply chain parameters such as shipping and fuel costs, customer needs, tariffs, pricing differences, and weather, data science techniques can significantly boost the efficiency of operations with reduction in costs.

Furthermore, the emergence of just-in-time manufacturing has meant that orders are based on tight timelines, resulting in tighter supply chains. Data science models have also assisted manufacturers in predicting market trends and effectively capitalising on them. In such a scenario, it becomes necessary for manufacturers to be able to predict possible delays and calculate the probabilities of potential risks.

Consequently, adopting data engineering and data science to analyze large volumes of historical data will prove a useful strategy for business in developing tools and technologies to minimize these risks and thereby, improve risk assessment in supply chains.

6. Data-Driven Business Intelligence to Pave the Way for Product Development

Data engineering and data science can help manufacturers to understand their customers better and provide them with the tools and technologies to satisfy the needs of customers. Thus, data can be leveraged by manufacturers to plan and design new products or upgrade existing products with increased value for customers. 

Data science tools  can not only help in determining the right product to be produced but also help to determine the best way of production, considering the necessary specifications to meet customer needs. Leveraging data science can thus aid manufacturers in understanding their customers as well as broader market trends, and thereby encouraging the manufacturing of new products.

7. Data Engineering and Data Science to Revolutionize Manufacturing Safety

The introduction of data science in manufacturing has been a revolution in regards to workers’ safety. Robots are utilized today to perform tasks that are difficult or dangerous for humans to perform. Data science tools are also being used to track a workers’ health conditions such as heart rate, body temperature, to ensure their safety. Hazards involved in high-risk operations can also be identified and prevented with the help of data science. 

In the coming years, manufacturing companies will have to remain extremely careful not only about the quality of operations and products, but also about ensuring the safety of the workforce. Once the economy begins to gain momentum, the manufacturing industry will have to get ‘intelligent’ help from data engineering and data science technologies to tap on the opportunities to expand their operations into hazardous environments and expand production capabilities to meet the changing consumer demand in the coming future.



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