Data science in manufacturing enables companies to remain competitive in a technologically advanced world. This practice involves quantifying data in order to make production run more efficiently. In order to make the most of production processes, manufacturing companies must analyze internal equipment and factories as well as external market factors and logistics. Professionals who provide these companies with expert research and recommendations are called data scientists. These trained professionals combine expertise in traditional management with knowledge of the latest innovations in big data. A look at the most important applications of data science in manufacturing can demonstrate the essential ways that big data is transforming this business sector.
One of the primary benefits of data science in manufacturing is the potential to raise productivity. Also known as optimization, this method of increasing productivity allows companies to produce error-free goods at a lower cost. Given the cost of goods, optimizing production can result in more value from initial investment and ultimately increase the bottom line. For example, data scientists can analyze the number of products produced for each employee and every given part of a factory. As a result, this method can help save floor space and determine which employee tasks raise productivity. By maximizing the use of floor space, companies can produce more products.
In addition, using data can help quantify behavioral activities within a factory. If specific assignments create too much lag time, managers can redirect these activities to make factories or plants operate more efficiently. Furthermore, data scientists can record activities at workstations to identify waste in resources ranging from time to electric energy. Therefore, this reduction in inefficiency can also eliminate unnecessary costs and ultimately improve the bottom line.
Machine and Asset Sensors
Companies also use data science in manufacturing to raise productivity through innovation. For example, one of the most exciting innovations in modern manufacturing is the broad-scale implementation of sensors. These advanced technological tools provide quantitative data on which activities and machines produce the right amount of goods. Sensors can monitor every aspect of factory production, including machine temperature, decibel controls, infrared sensors, pressure and humidity controls, and vibration sensors. In addition to equipment, motion sensors can prove invaluable for security and clearance. These tools can help companies save money on additional steps to protecting physical assets. In addition to the machines themselves, sensors also play a role in keeping facilities running smoothly. For example, specialized optical sensors can monitor electric circuits, and larger scanners can monitor overall operations.
Real-Time Logistical Data
In terms of logistics, data science in manufacturing can help companies manage and reduce overhead costs. Manufacturers have to monitor a host of factors in addition to production, including the cost of inventory, machine maintenance, and personnel management. In the past, managers kept logged records of this logistical data, and then released data in quarterly, biannual, or annual reports. Unfortunately, the occurrence of change between reports could quickly make such information appear dated.
With the use of real-time data, however, companies can monitor logistical requirements without waiting for traditional reports. Managers can then make practical decisions that can improve the company at any given point of time. For example, purchasing managers can generate daily reports to decide how to eliminate excess internal and administrative costs. According to a study by the European Institute of Business Administration (INSEAD), use of real-time data gives manufacturers a competitive edge over other companies. Instead of focusing on administration, manufacturers can use the larger annual reports to focus on business profit and growth.
Data science in manufacturing also includes the integration of machines with computer software to follow instructions more efficiently. For instance, factories that use levers can use coding that allows them to cut parts to precision far greater than the human eye. Known colloquially as the “Internet of Things,” the integration of software with traditional machinery can create automation in business services. Software integration also has numerous practical applications for traditional equipment. For example, data scientists can program plant temperature settings to corroborate with weather forecasts and adjust heating and cooling in real time. In addition, scientists can program tractors and farm equipment to work with satellite big data and determine sustainable use of fertilizer or pesticide. Notably, companies interested in corporate responsibility can use software integration to prevent environmental waste and preserve natural resources under their control.
Specialized data science in manufacturing allows companies to create models to predict the construction and success of a new product. According to Forbes, predictive analysis of a product’s demand and production is the most important way that big data can improve the economic performance of manufacturing companies. As a result, skilled data scientists observe existing market conditions and can help tweak products to match an existing consumer need. These professionals also analyze the types of new technology that can help factories and plants produce fault-free products and determine what execution methods get products to customers faster. Based on the results, predictive analysis can also forecast which new products customers are likely to support in the future.
Manufacturer Quality Control
Data science in manufacturing can play a tremendous role in product quality control. This ranges from the first purchase of raw materials, the standardization and quality of products, and acquisition of feedback from customers. Specifically, data scientists can develop methods to help detect problems or defects in manufactured goods before reaching the market. These professionals can also test products during product to catch errors and avoid potential product recall. This process is also known as positive yield optimization.
In order to improve positive yield without slowing down the manufacturing process, companies may hire data scientists to design sophisticated robotics or to create computer vision in factory equipment. In addition, data scientists and statisticians may use machine learning to auto-correct potential issues at the production level. In this way, data scientists create data-driven or “smart” manufacturing. The ability of data scientists to create smart manufacturing helps to automate quality control. As a result, data scientists are expected to play a crucial role in the future of competitive manufacturing.
Supply Chain Management
Supply chain management is one of the most important ways companies can benefit from data science in manufacturing. Primarily, data science helps ensure consistency in the production of large volumes of items. Programming can input directives that can direct machines to complete precise tasks by the millisecond. From there, managers can assign skilled workers to complete assignments that require human oversight. Data programming can also monitor the output required to fulfill customer needs and sales goals. This input-output process helps companies run more smoothly, prevents product inconsistency, and ensures that companies get the most for their investment.
Within the factory, computerized supply chain tools can prevent costly occurrences such as downtime and backlogs. Data science in the manufacturing process an also automate repair schedules to prevent breakdown in equipment. Outside of the factory, data science tools can also prevent logistical problems such as delays in delivery. Data science has many practical applications in delivery of manufactured goods, including use of fuel, oil consumption and maintenance, and fleet operation. Furthermore, some manufacturers lease their factory machines to other companies or create additional facilities or franchises. Improved supply chain management ensures consistent quality in each franchise location. In addition, data scientists can use cloud computing to connect these separate factories through virtual networks.
Consumer Data Collection
Data scientists use research methods and computer technology to keep track of customer orders. From warehouses and wholesalers to direct consumers, companies can use data science in manufacturing tools to permanently store transitions and print records at any time. Also known as “set data,” these reports can provide analytics on everything from product demand to consumer demographic. Manufacturers can then create products that appeal to consumer markets worth billions. To capitalize on this lucrative opportunity, manufacturers rely on data scientists to automate data collection and create smart sales and marketing systems.
Custom Product Creation
Data science in manufacturing also plays a critical role in product development. For companies interested in innovation or improving products overall, data science enables developers to create and test models for testing. This allows management to review products before investing money into manpower or machinery. Companies interested in custom product creation can also opt to join the National Network for Manufacturing Innovation (NNMI). This government initiative focuses on creating and commercializing new consumer products in order to stimulate the economy. According to the NNMI, companies that specialize in digital manufacturing, machine learning, and next-generation energy can expect to see the most growth and funding capital. As a result, manufacturers are likely to hire data scientists to spearhead custom product creation.
While manufacturing may not seem like the most obvious source of cutting-edge technology, data science in manufacturing currently produces some of the most promising innovations in the field. According to the National Additive Manufacturing Innovation Institute (NAMII), the development of smart manufacturing is essential to sustaining the United States as a leader in the global economy. Therefore, professionals can expect to see data science in manufacturing at the forefront of the new business and industrial sector.
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