Operations Management in Service and Industrial Sectors
Management of operations in service and industrial sectors has evolved over the last few decades, owing to changes in the market requirements. Markets are becoming increasingly global now, thus forcing enterprises to change their operations management strategies. Globalized markets, environmental concerns, advancement in technology are some of the factors that affect operations management at a firm. Moreover, in the past one or two decades, the internet has completely transformed how businesses acquire and manage the available resources and data. Operations management or OM has shifted its foundations from a policy of mass production to that of mass customization (Gunasekaran & Ngai, 2011). As a matter of fact, big data is believed to be the guiding light, as far as operations management is concerned.
Big data may be defined as a huge volume of data, in unstructured or structured formats, which can be used to inundate daily business operations. The 3Vs policy is often used to describe what big data actually is – the sheer volume of data involved, the variety of the data and finally the velocity or the speed at which the data is processed (John Walker, 2014). In a business, such volume of data can be obtained from an array of sources, which include sales records, results from surveys and experiments and so on. The data in question may be unstructured (like document files), structured (like SQL databases) or even semistructured. All of this data is expected to be ingested, correlated and analyzed by human analysts, so as to understand the core essence of such data; the velocity or the speed at which this data is processed an analyzed forms an integral aspect of big data analytics. The terminology, “big data” is comparatively new, yet, the process of data acquisition and analysis is centuries old. The term garnered international attention only in the early 2000s, when Doug Laney, industrial analyst, introduced the concept which was to revolutionize business altogether (Gandomi & Haider, 2015).
In the twenty first century, with growing consumerism and advancement in technology, the quantity of data that is being generated and accumulated is unimaginable; this means, that there is tremendous potential for utilizing this data to optimize business operations. However, the truth remains, only a fraction of this data is actually analyzed (Chen, Mao & Liu, 2014). The paper below studies the intricacies of big data and its role in the 4th industrial revolution that is ongoing. Furthermore, the paper also analyzes how big data analytics could prove to be beneficial for business operations, by increasing productivity. A case study on Ford Motor Company has also been provided to study the effects of big data in the industrial sector.
The Definition of Big Data
The twenty first century saw the dawn of what is known as Industry 4.0, or the fourth industrial revolution, as it is known in common parlance; this industrial revolution has seen some groundbreaking innovations which include the Internet of Things, artificial intelligence, quantum computing, cyber physical system and so on (Rüßmann et al., 2015). As a matter of fact, big data too is an essential component of Industry 4.0 (Lee, Kao & Yang, 2014). With significant growth in technology, companies (which include ecommerce businesses and even credit card companies) gather information about their customers (including buying preferences and tendencies which could be used to enhance customer satisfaction levels in the future) and store them in databases (Wang et al., 2016). It would be suffice to say that big data would be playing a vital role in the ongoing industrial revolution; some analysts have even claimed that the sole foundation of Industry 4.0 is big data. For example, in the manufacturing sector, big data is expected to improve efficiency and consequently reel in billions of dollars in profits (Stock & Seliger, 2016). Similarly, in the financial industry, big data is what steers and drives the industry today.
As a matter of fact, big data is associated with the other aspect of Industry 4.0, namely Internet of Things or IoT. In the IoT, the various machines and devices are connected to the world wide web, through some channel or the other. Along with that, the use of artificial intelligence makes lives easier and paves the way for newer business opportunities as well.
Businesses today require timely and efficient analysis of available data, so that the information obtained can be used to optimize business processes; in the competitive sector that is business, it is important for enterprises to utilize big data and integrate them into the production processes. A simple example will help strengthen the point; in the manufacturing industry, the implementation of Industry 4.0 is expected to change the production processes. It is assumed that the machines would no longer be dependent on manual labor, instead, they would be self learning devices that can self diagnose and reduce disruptions in the production proves (Lee et al., 2013). With the birth of such smart, innovative technologies, the amount of data and the kind of data generated has undergone a change; this is where big data analytics comes into play (Dubey et al., 2016). The installation of big data analytics and optimum utilization of the same would require a sound knowledge of IT domains and data science so as to decipher the complex programming models and infrastructures.
Big Data and Industry 4.0
The very term big data refers to a massive quantity of data which cannot be assessed using traditional or conventional databases and tools. The term does not simply mean the data in question, it also entails the resources or the tools required to store and analyze this data. Unfortunately, most companies today lack the resources or infrastructure required to process such big data (Cukier & Mayer-Schoenberger, 2013). The past few years have seen what is now known as data explosion; the amount of data available increased from 150 exabytes to 1200 exabytes in a period of five years from 2005 to 2010 (Schrage, 2016). As a matter of fact, the amount of big data is estimated to increase at a startling rate of forty per cent annually, which is much more as compared to the rise in population. It is not sufficient to simply gather this data; big data, in reality, involves curation, search, storage, transfer, sharing, visualization and finally analysis (Michael & Miller, 2013). This data revolution has been propelled by the change in pattern of data generation; earlier, it was a simplistic system where the customer was the consumer of data and the service provider was the source of it. However, now since most of the devices are connected to the internet in some form or the other, both the customer and the service provider have become source or consumer of data (Ward & Barker, 2013).
The need for big data is not simply because of the sheer magnitude of available data, but also because of how it can be put to use. The main purpose of data analysis is multifaceted – time reduction, cost reduction, optimized production and development of new products (Provost & Fawcett, 2013). The key benefits of big data analytics are:
- It would help determine the root cause of issues, defects and failures in almost real time.
- It would analyze the buying habits of the customer and generate coupons on the basis of that.
- Calculation or risk becomes easier with big data analysis.
- Fraudulent behavior can be identified and eliminated before it causes irreparable damage.
Using big data can have a number of benefits for a company. Some of them have been discussed below:
Reduction of maintenance costs
Earlier, the process of replacement of machinery and systems in an organization or factory was entirely based on estimation and assumptions. For example, it was assumed that a particular system would wear out after a fixed period of time, and thus it would accordingly be replaced, even if its life cycle was not over. This was not only inconvenient but also a costly affair. The implementation of big data would help eliminate such impractical and costly practices (Lee, Kao & Yang, 2014).
Benefits of Big Data Analytics for Businesses
Customers today are quite tech savvy and have critical opinions of their own; they have their own set of expectations and demands and are not willing to compromise. They would like to weigh all their options, try out a few brands before settling on a particular company. As a result, brand loyalty is equally fickle; customers today would not hesitate to change brands in the blink of an eye. As such, it can be difficult to live up to customer expectations. However, the use of big data analytics would study customer behavior patterns and would reveal information about each individual customer’s requirements, buying patterns, expectations and so on. This data can then be used by companies to customize their products to fit the needs of their target customer base (Spiess et al., 2014).
Similarly, it is equally important to ensure that the products offered by a particular company are up to the mark; it is imperative for companies to study how their product is perceived by the market and what could be done to improve it. For example, surveys may be carried out by the company to determine customer perception about a product. If the results of such a survey, as analyzed by big data tools, indicate room for improvement, the company would be able to make the most of information available to them and reinvent their product, or their marketing and advertising strategies, as the need be.
It is a well known fact that any business bears a certain level of risk; the success of an organization would depend on how a company is able to estimate the risks associated with a particular strategy or project and adopt adequate measures of eliminating them. Big data enables predictive analysis, which scrutinizes all available information, to shed light on the latest developments and trends prevailing in the industry.
Big data would allow an organization to map their entire database, which would reveal any threats that have pervaded the system, internally or externally. Moreover, such tools would enable a company to identify sensitive information and the potential threat to their security; this could go a long way in securing all important data and protect the company from internal or external breaches, like credit card fraud (Crawford & Schultz, 2014).
The insights that a company would obtain through big data tools and analytics would not only help in increasing productivity and performance levels but also generate newer revenue streams. For example, companies could sell the information as non personalized data to the large players in the same industry.
Reduction of Maintenance Costs
At present, all companies, big or small will have some kind of online presence; the availability of big data tools would provide information which could then be used to enhance website performance. That way, it would leave a lasting impression on the customers visiting the website. Also, the customer’s personal information, which forms a major portion of big data, could be used to present tailor mad recommendations which would consequently improve customer satisfaction levels.
Availability of such information at their fingertips has paved the way for speedy decision making and problem solving by the companies. For example, a manufacturing company that is perfectly aware of what their customer expects of them would be able to better meet the requirements. Similarly, the ability to take decisions faster would hasten the production process as well.
There has been a lot of hue and cry about the benefits of social media and how it has revolutionized the industry; but the truth remains, only a handful of companies have been able to successfully implement social media and ensuing marketing strategies. Big data makes that easier by allowing companies to track the activity and progress made on social media sites. Big data tools especially designed for this purpose would help companies get a clearer picture of what their customers really want.
Thus, it can be said that big data is certainly the future of the industrial sector; it forms the pillars of Industry 4.0. As has been mentioned earlier, the amount of big data is expected to increase in the upcoming years. The need of the hour is for companies to devise strategies and employ big data tools which can translate the information into critical intelligence. Without big data, the company would be inviting risks like losing relevancy or missing out on the latest developments. However, using such big data tools would require some adjustments in terms of IT infrastructures as well. That could be the reason behind the failure of small scale firms in employment of big data tools (Jagadish et al., 2014).
According to Michael Cavaretta, the director of analytics at Ford Motor Company, big data is too heavy to be processed through traditional systems. The need for driverless vehicles and the extensive driving research has fuelled the need for big data in the automobile industry. Ford Australia, Ford Motor Company, is the largest manufacturer and producer of commercial vehicles, SUVs and passenger cars in Australia (“Ford Australia”, 2018). In the early 1900s, Ford had been the frontrunner as far as the vehicle industry was concerned. But in 2007, the company faced severe obstacles; by the end of the financial year in 2006, the company had incurred loss of more than 12 billion dollars. It was during this time that the company employed big data analytics to revive its operations and change the way automobiles were manufactured and marketed to the world. By 2009, the company began to see the light of day once again as they finally began to make profits and even launched twenty five new vehicles, selling over 2 million cars in on year (Vlasic, 2011).
Customer Behavior Analysis
The executive team at Ford was aware of what their clients really wanted – they wanted the right car, with the perfect engine and the exact right features. To cater to each individual client, Ford came up with a system of Smart Inventory Management System, also known as SIMS which includes data on the cars sold, the cars manufactured, data on the cars most searched on company websites, employment rates and so on (Wee & Wu, 2009). Through a shrewd manipulation of the data obtained, Ford is able to customize their car specifications and dealership stock. As a result, Ford is able to produce fine tuned cars, offering multiple options to the customers.
Ford also used big data analytics to monitor and scrutinize posts on social media in order to understand their customer’s perception about their models. According to Cavaretta, text mining algorithms were being used which take it one step further than conventional market research. For instance, manufacturers were in a dilemma as to whether to include a power liftgate or whether a flip glass system would be used in the new model. The use of social media tools enabled Ford to simultaneously reduce manufacture costs and improve customer satisfaction (Bayou & De Korvin, 2008).
Earlier, the cost of manufacturing a prototype exceeded 250,000 dollars; yet, Ford was releasing nearly 200 prototypes in the case of each product. However, these prototypes only served the purpose of testing variants, and it was deemed an unnecessary expense by the company. Thus, they devised the Prototype Optimization Model, which could test the most number of iterations; this means that the most number of variants could be tested on the least number of vehicles. The use of such big data tools helped in saving more than 250 million dollars.
Ford also uses cloud computing, a big data tool which has profoundly changed the way industries function. Ford had started out with 99 per cent hardware, but has now optimized its systems to 60 per cent hardware and 40 per cent software (Whaiduzzaman et al., 2014). In addition, the vehicles manufactured by Ford are now connected to the IoT, Internet of Things, which has become quite omnipresent now. Cloud computing allows the company to obtain the data and then store and analyze it, with the permission of the customer. This data would then be used to determine how a particular vehicle could be improved or how the customer was utilizing the vehicle.
- It can thus be understood that such big data and optimum utilization of big data tools can help inform design; Ford is able to analyze the existing technologies along with the ones they expect will make a mark in the future; that is followed by an analysis of the benefits, the costs involved and other logistics. Such information can then be used to modify and enhance design of vehicles.
- Ford used social media to understand why the three blink signal feature in Ford Fiesta was not sitting well with customers. Research into social media responses from customers showed that the problem rested with the fact that the turn signal was located on the steering column, which could be fixed easily.
- Ford used big data analytics not simply to reduce costs and improve profit generation but also to increase the value. For example, the use of tools like Hadoop, has enabled the company to store data in a cheap and time effective manner. This creates an innovative data environment where the right information would be accessible at the right time.
Product Improvement
Conclusion:
In conclusion, it can be said that big data has wholly altered the way industries function in the present day; big data refers to the wide variety of data that is massive not only in terms of volume, but also velocity. In manufacturing or other industries, this data would be pertaining to the customers and minor details about them. For example, it would include a customer’s personal details, his or her buying preferences, behavioral patterns and so on. All of this data must be stored and analyzed using adequate tools so as to obtain information about a customer and to consequently customize service based on the available data. However, the sheer magnitude of this data makes it impossible for conventional computer systems to store them, let alone process them. This is where big data tools come into being; such big data analytics and tools, like social media algorithms or cloud computing would not only analyze this data but also reduce costs involved in manufacture and production and improve productivity and performance levels. As was seen in the case of Ford Motor Company, it was the use of big data tools that rescued the company while it was on the cusp of decline. Utilizing big data proved to be extremely beneficial for the company; not only was Ford able to manufacture vehicles or reinvent them using client information but was also able to include new features, fine tune their vehicles, increase profit generation and reduce prototype costs. However, it must also be remembered that including big data in operations management would require a significant shift in infrastructure, which many companies might not be able to afford.
References:
Bayou, M. E., & De Korvin, A. (2008). Measuring the leanness of manufacturing systems—a case study of Ford Motor Company and General Motors. Journal of Engineering and Technology Management, 25(4), 287-304.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209.
Crawford, K., & Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. BCL Rev., 55, 93.
Cukier, K., & Mayer-Schoenberger, V. (2013). The rise of big data: How it’s changing the way we think about the world. Foreign Aff., 92, 28.
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology, 84(1-4), 631-645.
Ford Australia. (2018). Retrieved from https://www.ford.com.au/
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Gunasekaran, A., & Ngai, E. (2011). The future of operations management: An outlook and analysis. International Journal Of Production Economics. doi: 10.1016/j.ijpe.2011.11.002
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86-94.
John Walker, S. (2014). Big data: A revolution that will transform how we live, work, and think.
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp, 16, 3-8.
Lee, J., Lapira, E., Bagheri, B., & Kao, H. A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38-41.
Michael, K., & Miller, K. W. (2013). Big data: New opportunities and new challenges [guest editors’ introduction]. Computer, 46(6), 22-24.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group, 9.
Schrage, M. (2016). How the big data explosion has changed decision making. Harvard Business Review.
Spiess, J., T’Joens, Y., Dragnea, R., Spencer, P., & Philippart, L. (2014). Using big data to improve customer experience and business performance. Bell labs technical journal, 18(4), 3-17.
Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia Cirp, 40, 536-541.
Vlasic, B. (2011). Once Upon a Car: The Fall and Resurrection of America’s Big Three Auto Makers–GM, Ford, and Chrysler. William Morrow.
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158-168.
Ward, J. S., & Barker, A. (2013). Undefined by data: a survey of big data definitions. arXiv preprint arXiv:1309.5821.
Wee, H. M., & Wu, S. (2009). Lean supply chain and its effect on product cost and quality: a case study on Ford Motor Company. Supply Chain Management: An International Journal, 14(5), 335-341.
Whaiduzzaman, M., Sookhak, M., Gani, A., & Buyya, R. (2014). A survey on vehicular cloud computing. Journal of Network and Computer Applications, 40, 325-344.
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
