Impact of Big Data in Manufacturing

Big Data analytics holds importance in every industry because of its ability to provide insights for decision-making in every facet of the business (Davis et al., 2012). Future manufacturing is essentially connected with Big Data. With the fourth industrial revolution reaching greater heights, manufacturing can be based on the data from I.o.T. elements and analytics along with the customer (Big) data leading to predictive approach for the future (Lee et al., 2013). Improvements in innovative processes, impact on the environment and operational efficiency are some potential benefits of intelligent manufacturing (O’Donovan et al., 2015). It is well known that advances in information technology have played a major role in the fourth industrial revolution. The rise of Big Data and software analytics had a huge influence on the contemporary industries and arguably helped in reshaping the manufacturing sector (Babiceanu & Seker, 2016). Big Data entails having a table of information about the manufacturing company in terms of product quality, total number of products in production, raw materials availability, total number of finished products, client information and orders, purchase details, and details of the manufacturing equipment, etc. (Paakkonen & Pakkala, 2015). Companies have begun to realise that disruptive technologies like Big Data and the internet of things can provide a competitive advantage and effectively meet the business needs globally (Tao et al., 2018). Therefore, manufacturers use the industrial internet of things and Big Data to capture and make use of external and internal data to gain the competitive edge by using the insights in decision-making right from the product development stage to financial and supply chain decisions. Figure 3.3 illustrates the various data generated in manufacturing and their corresponding usage.

Let’s have a closer look at how Big Data has had a positive impact on the manufacturing sector in the following subsections.

i) Faster Cost estimation. It is always necessary for the production team to have the cost estimation available for sales and product teams. With shorter life-cycles today and more difficult market dynamics, accurate and quicker cost estimation is needed. With I.o.T.s and Big Data analytics the data is retrieved in real-time and processed at greater speeds so that necessary information is available to make the cost estimations (Lee et al., 2013).

Generation of data and its usage in manufacturing

FIGURE 3.3 Generation of data and its usage in manufacturing.

ii) Quality production. Big Data enables manufacturers to implement Lean and Six-Sigma programs which eventually reduce wastes and variability in production. By using advanced Big Data analytics key performance indicators can be analysed and the necessary corrections can be made (Kang et al., 2016). Since most of the data can be easily collected, product quality can be improved significantly, and the quality costs can be reduced drastically. Though every single product may require a huge number of quality tests, with Big Data analytics, pattern recognition and predictive analysis can lessen the number of tests. Production line quality can be improved significantly with the help of sensor data analysis detecting manufacturing defects (Zhang et al., 2017). Through early detection, time and costs related to adjusting the production processes can be saved.

iii) Custom product design and production. Traditionally, manufacturers focused on production at scale for cost efficiency. Even the introduction of flexible manufacturing systems did not cover mass customisation (Zhong et al. 2017). But w'ith Big Data and the I.o.T.s, the forecast for customised product demand can be achieved precisely. Manufacturers can gain more lead time and have the opportunity to produce customised products efficiently, similar to mass and batch production. Big Data analytics enable product engineers to access real-time data on customer preferences so that even design changes can be done at any time to meet order requirements (Wang et al., 2016).

iv) Better demand forecast. Using Big Data, manufacturers can anticipate the product demand accurately and more quickly, which prevents companies from wasting time and resources on inventory (Zikopoulos & Eaton, 2011). The better forecasting capabilities offer the stakeholders to step towards future business arrangements. The ability to generate better forecast reports will lead to fewer stock outs, less idle inventory and, importantly, satisfy more customers (Diebold, 2003). Manufacturers and retailers can also use social media tools to measure customer feedback and sentiments for their products and plan the product specifications accordingly.

v) Enhanced Supply Chain Risk Management. In today’s global supply chains, the interruptions or any possible disruptions are costly and affects the manufacturer’s relationships with retailers and customers (Chae, 2015). Big Data provides an opportunity for manufacturers to minimise the risks in materials delivery for production and proactively develop contingency plans for any such disruptions. Further, utilisation of Big Data analytics assists in assuring the supplier quality by assessing the performance data needed for sourcing decisions. I.o.T.-enabled systems help in efficient monitoring of inventory, location tracking and reporting of products throughout the supply chain (Zhong et al., 2015). Big Data can also deliver critical inputs into product lifecycle management (P.L.M.) and enterprise resource planning (E.R.P.) systems of the organisations (Zhong et al., 2016).

vi) Safe work environment. Analysing data about short- and long-term absenteeism of employees, injury, illness rates and any accident data are key performance indicators for the health and safety of the work environment. Manufacturing companies now use sensors to ensure the safety of products and labour as a part of preventive maintenance by ensuring conformity with safety regulatory requirements (Lee et al., 2013). Moreover, Big Data is the heart of Industry 4.0 and smart factories. Hence, the human workforce is replaced with robots to work under difficult and hazardous working conditions. Any disruptive changes can be sensed by, and predictive and proactive safety measures are made possible with, Big Data analytics (Loebbecke & Picot, 2015).

vii) Proactive, predictive and preventive maintenance. The internet of things and sophisticated sensors enable manufacturers to collect and analyse realtime data from all machinery and even from customer products (Le et al. 2013). This helps the manufacturers to act proactively by reading the patterns of data received. With operational data, predictive maintenance can be used to prevent downtime, cost-related maintenance and prolonging the lifespan of machines by preventing any permanent damage (Kang et al., 2016). Customer products nowadays are tracked and any changes in behaviour of the device are notified to the customers predicting the optimal maintenance, thereby creating better user experience and reducing maintenance and warranty costs.

viii) Faster service and support to customers. Big Data is mainly oriented towards analysis and prediction of acquired data. Manufacturers are looking at more complex products needing an operating system to manage the sensors onboard. Analysing real-time data from those sensors can help in customer service (Opresnik & Taisch, 2015). Even before receiving any complaints from customers. Big Data can help with framing strategies to deal with any predicted defects. Therefore, when some consumer files a complaint the company can address the issue by immediately offering action steps to deal with it.

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