1 Big Data in Logistics and Supply Chain Management- A rethinking step *Dr.D.Ghosh Abstract Today s business is highly turbulent due to high-end competition. Every company wants to survive and continue to strive for higher market share. Companies are continuously trying to meet the expectations of the customers. Customers needs and wants are fast changing. Companies are investing heavily in creating Information Technology support and platform so as to facilitate the businesses. Logistics and Supply Chain are the vital wings of any business. Big Data in different aspect is helping the businesses in the areas of Logistics and Supply Chain. Through Big Data businesses are in a position to collect, update, store, use versatile set of data relating to different processes and activities of the business. Timely, fast and effective decisions can be taken in businesses with the help of Big Data. Key Words: Big Data, Supply Chain Management, Logistics Management, Business Process and Competition. I.INTRODUCTION: The success of any business stands on the robustness as well as efficiency of its supply chain and logistics management. Supply Chain Management is responsible for creating and maintaining the links of different entities in a business which are responsible for procurement of raw materials to ultimate end user delivery of the product. Whereas Logistics management ensures different supports like transportation management, warehouse management, inventory management, packaging and order tracking. In the era of turbulent business environment where product innovations are the key component of survival strategy to win the customers, the companies are trying hard in the area of product innovation. The continuous upward revenue trend of the companies like Google, Amazon, Facebook and ebay gives the indication of a fourth production factor in today s turbulent business operations. In addition to resources, labor and capital there is no doubt that Information and Data have become the key element of differentiation in competitive market. The Data play a very critical role on the different decisions related to supply chain and logistics operations of the business. To understand the needs and wants of the customers companies are relying on Data. The Data that are used now-a-days in logistics and supply chain management are voluminous, versatile, rapid as well as sensitive. These types of data are called Big Data. *Associate Professor, Department of Business Administration, Assam University, Silchar,
2 II.BIG DATA: Companies rely on huge set of extensive versatile and rapid data for their prompt steps in the zone of supply chain and logistics management. In case of E-Commerce giants like Amazon, Flipkart, Sneapdeal etc need to gather lot data related to customers, orders, inventory etc. The success of the E-Commerce companies depend a lot on how those companies capture, store and utilize those data in an efficient manner. Big Data utility in general: It is not like that only big business houses use Big Data, other than business houses different government and non-government firms/ houses use Big Data for different purposes. It is found that large volumes of information are being used to fight terrorism, large set of data and facts are used to cure deceases like Cancer as well as to predict Ebola. Big Data are thus being used to transform medical practice, modernize public policy and inform business decision making (Mayer-Schonberger and Cukier 2013). Generally large datasets on variety of things from demographics to weather and consumer spending habits are sometimes available freely online . Big Data utility in Small Businesses: Companies which engage themselves in food delivery may use the flavor of Big Data, also the Taxi services like Uber also uses the flavor of Big Data in their business. To capture the data sets they use Apps which allow the customers / passenger to place order/ preference directly from their smartphones to their homes/ locations, thus it allow the businesses to form and prepare complex matrix like how far way their customers live? how much they spend? Also the Taxi services like Uber takes care of the flight and weather information to plan as to where their resources will be appropriately needed . Singh and Reddy in 2014, discussed the different platforms available for performing big data analytics. They also discussed the different hardware platforms for big data and the detailed description of the software framework within the task support. Sagiroglu, Ankara, Turkey and Sinanc in 2013 highlighted characteristics of Big Data. They also elaborated the importance of Big Data that provides useful information for companies or organizations. Further they mentioned the overview of big data's content, scope, samples, methods, advantages and challenges and privacy concern on it. Han Hu, Yonggang, Tat-Seng and Xuelong in 2014 discussed as literature survey and system tutorial for big data analytics platforms, and provided an overall picture for non-expert readers. They presented the definition of big data and discussed big data challenges. Next, they presented a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. Nemanja Trifunovic, Veljko Milutinovic, Jakob Salom, Anton Kos in 2015 mentioned the shift in the computing paradigm and the programming model for Big Data problems and applications.  See, The Strategist, Business Standard, Big Data for Small Businesses, May 4, 2015, Page No 3.  See, The Strategist, Business Standard, Big Data for Small Businesses, May 4, 2015, Page No 3.
3 They compared the DataFlow and ControlFlow programming models through their quantity and quality aspects. Zuech, Khoshgoftaar and Wald in 2015 mentioned in their work the problem of heterogeneous data and in particular Big Heterogeneous Data. They discussed the issues related to Data Fusion, heterogeneous Intrusion Detection Architecture and Security Information and Event Management (SIEM) systems. Weinberg, Davis and Berger in 2013 in their research explored the meaning of Big Data and identified important paths of research on Big Data. They find it helpful to think of Big Data as a term that represents a period of time or era, a process and data that are from a variety of sources, of various structures or forms and variety of locations. McAfee and Brynjolfsson in 2012 highlighted that the differences between big data and analytics are a matter of volume, velocity and variety as more data now cross the internet every second than were stored in the entire internet 20 years ago. They discussed two success stories to elaborate how the companies are using Big Data. Fan and Bifet in 2012 mentioned in their work the Big Data mining issues. They mentioned new mining techniques are necessary due to the volume, variability and velocity of such data. They highlighted that Big Data challenge is becoming one of the most exciting opportunities for the years to come. Kaisler, Armour, Espinosa and Money in 2013, highlighted that Big Data refers to data volumes in the range of exabytes (1018) and beyond. Such volumes exceed the capacity of current on-line storage systems and processing systems. They analyzed the issues and challenges of methodologies for Big Data analysis and Design. II.a) SITUATION WITHOUT DATA: Without data Businesses and Society cannot run. Thus there is huge requirement of data in businesses and in the society. Following situations may occur if suddenly we find that there is no data available at all or data suddenly crash: In the dimensions of Communication and Social Media: 1.75 billion Smartphone owners can no longer use their phones, users worldwide can t send and receive 182 billion messages, 4.9 million Skype users unable to spend two billion minutes using the IM and video calling apps, online networks can t share over 500 million tweets, 864 million Facebook users can t spend an average 39 minutes on the network.  In the dimension of Entertainment sector: million paytv subscribers worldwide would lose access.  In the dimension of E Commerce: E-Commerce retailers would lose $2 billion in sales.  In the dimension of Health Sector: 78% of office based physicians and 59% of hospitals in the US cannot access patients electronics records. Thus there is greater demand of Data in our regular businesses and day-to-day activities.  ,,, See, a) NetApp, b)the Strategist, Business Standard, Kolkata, 18 May Page no 5.
4 II.b) GROWING DEMAND OF DATA: The world is accumulating data every seconds of every day and it is not decreasing. Approximately 2.5 zettabytes of data were generated in 2012, and the trend shows that the data will be growing in the subsequent years, as the volume of businesses and customers are growing rapidly. Data in zettabytes (ZB) Data Source: Oracle, 2012 In addition to the above shown growth curve of data, two features of data have substantially changed: Firstly, data is continuously pouring in. The technology oriented embedded devices such as cars, smartphones, RFID readers, webcams and sensor devices accumulates huge set of data without human interventions. The accumulations of data are faster. Secondly, data is extremely varied in nature. The large sets of data originates from camera images, CCTV, e-commerce catalogs etc. These unstructured data sources contribute to a much higher variety. III.BIG DATA AND EVOLUTION: It is the real time for the businesses to understand and implement the Big Data operations in their business process. Thus here, business process indicates the set of activities that business tries to create in order to impart value to their products. Big Data generally refers as the set of three Vs as volume, variety and velocity of structured, unstructured and semi-structured data gathering though IT infrastructures and networks into storage devices and conversation of the same into useful information for business and society. Thus Volume refers to the large extensive set of data. The size of data is simply huge in numbers. The variety of data implies that there are different types of data, nature of data that can be well generated for the purpose of the businesses. The velocity of data implies that the rates of collection and processing of data for different applications are very fast. For example, in retail sector, big data well analyze the in-store purchase behavior in near real time. With fast insight into demand change, stores can adjust products availability, inventory levels and promotional schemes to optimize sales.
5 According to A.T.Kearney analysis  the evolution of Big Data occurred in three different phases: Phase 1: Mainframes- Basic data storage: The computing timeline was Pre-relational (1970s and before). Here the data type was primitive and structured. The focus areas were Data generation and storage. Phase 2: Relational Databases- Data intensive applications: The computing timeline was Relational (1980s and 1990s). The data type was complex relational. The focus areas were Data Utilization. Phase 3: Structured Data Unstructured Data-Multimedia: The computing timeline is Relational + (2000s and beyond). The data type is very complex and unstructured. The focus area is data driven. IV.DRIVERS OF BIG DATA: As the role of Big Data cannot be underestimated in today s business, what matters more for the businesses is to pin point the basic drivers of Big Data in business. Business in their operations may identify few important drives of Big Data and these are: Real Time analysis and predictions about customers choices, buying behavior, inventory positions etc. Increase Logistics and Supply Chain efficiencies in delivering, procuring, tracking of products. Aggregation of Information to predict better decisions in business. Rapidly increasing consumer data (for example mobile) obtained through variety of sources. The above mentioned points have given boost to the businesses to move towards the big data. Big Data is really of strategic importance to the business firms. It is the business firm which can strategically differentiate it from its competitors by what it can do with the data it has. V.BIG DATA IN LOGISTICS AND SUPPLY CHAIN OPERATIONS: Logistics and Supply Chain are the very essential part of any business. When we talk about the manufacturing, distribution, warehousing, transportation, packaging, tracking etc. of products, it is the supply chain and logistics that comes into the picture. Big Data helps a lot in these domains. Big Data solves problems in a variety of business domains, but sales and operations are in the lead. The study conducted by Forrester Research Inc. On How Forrester Clients are Using Big Data, September Indicated some of the key points about business domains where Big Data has the greater influences. This view is indicated in the table 1.  See, IT Innovation Spurs Renewed Growth , See, S.K.Shivakumar, Big Data-A Big game changer, CSI Communications, 9-10, April  See, Pierre Audoin Consultants (PAC), Big Data Worldwide,2012.
6 Table 1. Business Domain Big Data Contribution (%) Marketing 45 Operations 43 Sales 38 Risk Management 35 IT Analytics 33 Finance 32 Product Development 32 Customer Service 30 Logistics 22 HR 12 Other 12 Brand Management 8 Source: Forrester Research Inc. How Forrester Clients are Using Big Data, September More over study conducted by Experton Group in 2012, revealed very prominent issues related to business/ government sectors which may be transformed through Big Data. Table 2 Sectors Industrial Mobility & Logistics Professional Services Finance & Insurance Healthcare Government Education Utilities IT/ TELCO/ Media Retail/ Wholesale Transformation potential of Big Data Business Model Medium High High Medium Source: Experton Group,2012
7 Through Supply Chain and Logistics management organizations are in a position to offer required value to their customers. Big Data helps the organization to generate vast, variety and rapid data for offering better value to their customers. There are different data analysis tools and techniques like Business Intelligence Systems (BIs), Data Mining and Predictive analysis used for Big Data. These tools basically help to optimize both supply chain costs and pricing to maximize profits. Tracking in real time system consisting of both Big Data hardware/ software tools like bar-codes, radio frequency identification (RFID) tags, global positioning systems (GPS) etc. These tools capture all real time data related to products, vehicles, customers as well as suppliers. Wagner and Kuckelhaus in 2013, illustrated different issues related to Big Data in Logistics and these are: Optimization: Large scale logistics and supply chain operations need data to run efficiently. Thus, advanced predictive techniques as well as real time processing give a new direction to business operations. Tangibility of goods and customers: In worldwide millions of customer touch points a day create an opportunity for business intelligence, customers response as well as customers feedback. Big Data provide good amount of insight about different situations (Wagner and Kuckelhaus 2013). In synchronization with customer business: The strong levels of integration with customer operations provide the logistics service providers a good insight into their customers operations. The application of analytic methodology in this domain reveals supply chain risks and provides enough measures to mitigate the supply chain risks. Network of Information: Network data provide valuable information related to global flow of goods. Global Coverage and Local Presence: The chain of vehicles moving across the country to automatically collect local information along the transport routes. VI. CONCLUSION: The growth of social media and Internet of things will accelerate exponential growth in data in days to come. Big Data is really a game changer for the organizations. Organizations are keen to empower themselves with the right analytical infrastructure to make decisions mostly data driven. Logistics and Supply Chain are the vital wings of any business. Big Data in different aspect is helping the businesses in the areas of Logistics and Supply Chain. Through Big Data businesses are in a position to collect, update, store, use versatile set of data relating to different processes and activities of the business. Timely, fast and effective decisions can be taken in businesses with the help of Big Data. Organizations can make better decision with the help of accurate analysis of data. Thus, better decision means greater operational efficiencies, cost reductions and reduced risk.
8 References: Fan, W, Bifet, A, 2012, Mining big data: current status, and forecast to the future, ACM SIGKDD Explorations, Newsletter, Vol-14, Issue-2. Han Hu, Yonggang Wen, Tat-Seng Chua and Xuelong Li, 2014, Toward Scalable Systems for Big Data Analytics: A Technology Tutorial, IEEE Vol-2, Kaisler,S Armour, F Espinosa, J,A and Money,W, 2013, Big Data: Issues and Challenges Moving Forward, Systems Sciences (HICSS), 2013, 46 th Hawaii International Conference on, 7-10 Jan 2013, Mayer-Schonberger, V, and Cukier, K Big Data: A Revolution That Will Transform How We Live, Work, and Think. New York: Houghton Mifflin Harcourt Publishing Company. McAfee, A, Brynjolfsson,E, 2012, Big Data: the Management revolution, Harvard Business Review, Nemanja Trifunovic, Veljko Milutinovic, Jakob Salom, Anton Kos, 2015, Paradigm Shift in Big Data Super Computing: Data Flow vs Control Flow, Journal of Big Data 2015, 2-4. Sagiroglu, S. Ankara, Turkey ;, 2013, Big data: A review, Collaboration Technologies and Systems (CTS), 2013, International Conference, Singh,D and Reddy, C,K, 2014, A survey on platforms for big data analytics, Journal of Big Data 2014, 2:8. Wagner,M,Kuckelhaus, M, 2013, Big Data in Logistics, A DHL Perspective on how to move beyond the hype, Weinberg, B, D, Davis, L, Berger, P D, 2013, Perspectives on Big Data, Journal of Marketing Analytics, Zuech,R, Khoshgoftaar, T, M and Wald R, 2015, Intrusion detection and Big Heterogeneous Data: a survey, Journal of Big Data,2-3
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