Significance Of Big Data For Supply Chain And Operation Management

Data collection and Storage

1.What kind of data should be collected and how.

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2.What are the requirements to the storage and how to achieve it.

3.What is Consumer-Centric Product Design and how to do it.

4.What is Recommendation System.

5.How online Business can Survive in case of Power outage or other disasters? 

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The paper mainly focuses on the significance of big data for supply chain and operation management. It is stated by Hazen et al. (2014), that big data is one of the terms that help in illustrating the large volume f structured as well as unstructured data that generally inundates the entire business on day-to-day basis. It is mainly used in order to manage the operations as well as supply chain of the organizations. On the other hand, it is argued by Waller and Fawcett (2013), that big data assists in making the entire organization much more optimized as well as efficient for increasing the bottom line of the organization. The big data use within the organization helps in improving responsiveness, inventory planning, demand planning as well development for giving continua advantages inn the field of supply chain management. In addition to this, it is identified that with the adoption of big data, different operations of the organization are generally managed appropriately within the organization (Schoenherr and Speier 2015). It is found that with the appropriate application of big data, different types of operations are planned, supervised as well as organized in context to manufacturing as well as production.

The report generally illustrates the significance of big data in terms of operation and supply chain management. The assignment mainly elaborates data storage, collection as well as significance of data in action. The paper also assists in providing proper recommendations for solving the problems of online business in case of disasters and power outrage.

1.Data Collection System

Data collection system is defined as one of the computer application that helps in facilitating the entire procedure by allowing specific as well as structured information that need to be gathered in a very much systematic fashion for enabling the entire data analysis. The modern procedure of data collection is considered dependent on different types of advanced technology for analyzing large amount of data effectively (Wang et al. 2016). It is identified that big data helps in playing a significant role in collecting various kinds of data by following different methods as well as techniques. The various types of data that are generally collected are:

Data Collection System

Marketing data: The marketers generally utilize big data in order to collect business related information. The marketing related information   associated with browsing behavior, social media interaction and purchasing must be gathered appropriately from various organizations (Tan et al. 2015).The information that  are collected by utilizing big data must be integrated appropriately by applying effective marketing  strategy that generally helps in creating proper impact on customer retention, marketing performance as well as on customer engagement. The marketing related information is generally collected by following methods that include surveys, internet as well as different government agencies.

Operation management data: The data as well as information that are associated with the operation of the organization must be gathered properly for enhancing efficiency of the entire organization in terms of operation and expenditure (Chae 2015).  Big data is mainly utilized by most of the companies in order to collect as well as analyze data appropriately for raising profitability. The data as well as information that are associated with the organization’s operation mainly assists in planning, organizing as well as supervising in terms of production as well as manufacturing.

Figure 1: Data collection System

(Source: Schoenherr and Speier 2015, pp.121)

Supply chain data: The business organizations are facing number of issues as well as challenges due to absence of appropriate supply chain data. In order to improve both efficiency as well as service of the organization, it is quite important to collect information associated with supply chain management effectively (Giannakis et al. 2016). Due to the growth of different types of digital technologies, it is analyzed that the organizations can be able to gather appropriate amount of information as well as data by utilizing powerful techniques or methods. It is identified that appropriate data analysis of the customer is quite useful as it mainly helps in generating proper insights on labor optimization, operational risk management, and product placement as well as on pricing strategy (Waller and Fawcett 2013). Some of the additional key benefits that can be gained by utilizing big data in context to supply chain helps includes enhancing efficiency, improving productivity as well as edge with different competitors.

Financial data: Big data analytics mainly involves in gathering as well as identifying different type of information and data. Data are generally collected from different organization including shopping centre, bank and more for storing the information properly within the database. The information as well as data that are generally collected must be analyzed in different ways. It is identified that different types of complex system and algorithms are generally collected for the same procedure (Lee, Kao and Yang 2014). The information that are analyzed generally assists in predicting different future trends for determining the prices and for calculating the type of risks. The information associated with banks, credit card unions as well as credit card assists in determining the risk level.

Storage System

2.Storage System

 It is identified that traditionally data are mainly collected to store them in the form of written documents, which can be managed, effectively with the help management as well as marketing team of the organization. The data that are stored in the form of documents are not considered secure as they can be hacked at anytime. For resolving this type of challenges, the organizations introduced big data. According to Huang and Handfield (2015), the business owners as well as organizations are receiving huge capacity for data storage so that all the information related with supply chain as well as operation of the organization must be stored properly. After proper implementation of big data analytics, different types of rules as well as guidelines are generally set by the organizations (Wamba and Akter 2015). It is identified that before the implementation of modern procedure of data collection, manual procedures are generally utilized for storing information related with supply chain management, customer relationship management and more.

After proper implementation of big data tools, information associated with operation as well as supply chain management of the organization are generally stored in any of the storage devices as well as per the requirement. The procedure of automatic collection of data is quite effective as well as advantageous as compared to manual data collection procedure. In order to support, different business related information, it is very much important to incorporate proper rules in terms of different technological components (Hofmann 2017). For supporting various requirements as well as needs of the business organization, proper information is gathered by utilizing proper data collection techniques. It is identified that big data mainly assists in giving appropriate amount of storage to various organizations as it is analyzed that big data analytics utilization, the data collection procedure became much simpler as well as advanced in both the perspective of consumer as well as businesses.

The data related with operation as well as supply chain off the business organization must be accessed as fast as possible. If the organizations are unable to access proper information and data with appropriate time then the entire thing became useless. It is identified that in manual data management system, the entire data as well as information are accessed properly therefore the use of big data is considered advantageous. The supply chain management generally helps in holding number of technical approaches that include information technology, finance, and logistics as information technology (Tachizawa et al. 2015). In order to manage both the network as well as relation between various units including buyers, suppliers as well as facility providers, big data is considered one of the important tools. Big data helps in providing both type of service including backward service as well as forward service. The main task before the management authority is to strength the immunity of the interface by removing different types of external attacks.

3.Consumer centric product design

 It is identified that design of consumer centric product is required by the organization in order to drive the entire business properly. Therefore, it is quite significant to make proper modification within the existing system as per the requirement of the consumers. Different types of analytical technologies are needed in order to improve the entire storage capacity by giving proper security to the data and information that are generally associated with the operation as well as supply chain of the organization (Christopher and Ryals 2014). The various types of components that are generally needed include innovation, data driven technologies, consumer centric products as well as collaboration. It is identified that centricity of the consumers not only assists in giving appropriate service to the customer  in order to provide greater offerings on the experience of the consumers (Kwon,  Lee and Shin 2014). This is generally possible due to the methods of post purchasing as well as purchasing. The different approach of product of product design generally assists the consumer to be in the first priority and the rest in the next level of priority list. The centricity of the consumer can be improved by incorporating components that include consumer-focused relationship, experience designing, front line empowerment and more.

Figure 2: Customer centricity

(Source: Waller and Fawcett 2013, pp.250)

It is very much significant to consider engagement, interest as well as behavior of the people for helping them properly. It also assists in identifying different types of opportunities for developing proper services as well as products that is quite helpful in attracting more number of customers. The lifetime value of the consumer must be considered properly by the management authority of the organization for customer segmentation (Demirkan and Delen 2013). It is identified that big data mainly helps in organizing, gathering as well as storing different types of information within the server in a quite effective manner so that the information can be utilized properly. In order to address the information as well as data, it is quite significant to comprehend the scenes quite effectively. Big data analytics have appropriate ability that helps in connecting large number of consumers within the business by considering different needs as well as requirement properly (Li et al. 2015). It is identified that proper support is provided to the consumers as big data utilization is considered as one of the important need for the organization. It is identified that proper level of management is generally needed in order to mitigate different challenges. It also assists in serving the goal of the organization. Proper level of knowledge is needed in order to overcome various challenges that are faced by the organization due to improper use of big data analytics (Hahn and Packowski 2015). Therefore it is identified big data analytics lays an important role within the business organization.

Figure 3:  Customer-centric supply chain

(Source: Wamba and Akter 2015, pp.64)

 The supply chain as well as operation management of the organization can be improved with the help of big data analytics. As per the research, it is identified that both the workers as well as consumers are considered on the centre of focus. In addition to this, it is quite important to store information as well as data related with suppliers as well as buyers with the data server of the organization. The customers can reach to their required services as well as products with the help of the website.

The supply chain management faces number of challenge due to improper utilization of big data software. However, the current system is helpful enough to mitigate the challenges as well as issues that are associated with traditional data collection method.  In the current system, different types of documents as well as files are stored properly for providing appropriate security from external attackers (Papadopoulos et al. 2017). For resolving the existing problems of supply chain, it is quite necessary to use Haddop, Cloudera as well as MongoDB.

The applications that are mainly associated with the tools of big data helps in saving both money as well as time of the organization. The different types of business related dxcinsights can be exposed for providing benefits to the organization in context to supply chain and operation management of the organization. In addition to this, it also assists in giving appropriate future layout for the consumers who are generally working within the business. Big data must be adopted in order to improve the relationship that exists between the supplies as well as buyers of the organization (Waters and Rinsler 2014). Big data also helps in providing number of advantages n context to mining, data storage, extraction and more. It is identified that big data is one of the analytic tool that is generally utilized for handling different type of concurrent tasks as well as limitless jobs within the entire business organization.

For the distributed storage, open source software is considered as one of the prior requirement. It is identified that the supply chain of different organization helps in holding distributed nature of data.  Big data tools works very much efficiently for managing the operation as well as supply chain of the organization. It is found that numbers of system are generally recommended that are very much helpful in developing the consumer centric model including the role of customers, technology, suppliers and more (Lu et al. 2013). It is analyzed that even after the implementation of big data; it is found that supply chain strategy can be developed. The model that us mainly recommended must consist of data based asset, strategic approaches of risk mitigation and supply chain. It is quite important both product requirement as well as data driven innovation can be served by considering different types of sustainable factors including designing of the product (Zhong et al. 2015).  Appropriate procedure of process management as well as product improvement must be developed properly. The other significant features of big data are as follows:

Real time operation: The use of big data analytics is very much helpful in making different types of real time operation simple. The data server that is related with B2C as well as B2B business models is quite easier after the implementation of big data analytics in the real time operation (Ng et al. 2015). Big data also assists in incorporating values to management procedure as well as supply chain as well as operation management. In addition to this, big data assists in taking appropriate decision by applying number of useful techniques.

Enhanced Visibility: Big data analytics is quite helpful in enhancing the entire visibility of demand, manufacturing as well as inventory level of the business. Therefore, big data utilization is considered advantageous as it assists in enhancing the production level within the organization.

 Business continuity is generally referred as one of the processes that is very much helpful in continuing the approach of business procedure delivery in terms of improvement. In order to measure the sustainable revenue as well as success of the organization, it is quite significant to plan the entire business improvement by different stakeholders of the organization for deploying the entire plan of improvement effectively.

It is identified that numerous number of phases are generally utilized within the organization for improving both the operation as well as supply chain management of the business. The phases are:

Section of process: This is considered as one of the initial phase for improving the entire business continuously. Different types of technology related processes generally affect the entire business either negatively or positively (Da, He and Li 2014). In spite of this, it is found that proper selection of proper model for service requirement of the consumer is required. Implementation as well as selection of proper procedure assists in generating the scope for business organizations.

Standardization and evaluation process: It is quite significant to select proper procedure for implementation. Big data tools are considered important tool that helps in improving the big data analytics due to its power accessibility and security related approach. Scope for continuous improvement is mainly generated by selecting as well as implementing accurate procedure.

Figure 4: Phases for improving operation of the organization

(Source: Hahn and Packowski 2015, pp.47)

Procedure improvement: The procedure must be identified accurately before its implementation. The evaluated process should be implemented by considering various aspects of security as well as improvement plan (Demirkan and Delen 2013). In order to improve the entire supply chain management, it is very much necessary to identify different issues associated with supply chain. In order to mitigate the challenges, it is very much important to apply proper tools as well as techniques.

Conclusion

It can be concluded that big data is quite advantageous in business organization as it helps in managing both operation as well as supply chain of the business organization. Big data helps in shaping the entire supply chain as well as operations associated with the organization. The volumes of data as well as information create impact on supply chain or operation of the organization either negatively or positively. The different types of supply chain components include forecasting, distributing, scheduling as well as inventory planning. Big data analytics assists in playing an important role within various business organizations in context with supply chain as well as operation management related with business. The use of big data creates number of challenges for various business organization, which must be resolved appropriately. The methods as well as techniques that are mainly used for mitigating the challenges are as follows:

Privacy problems: Privacy is considered as one of the important issues that must be mitigated appropriately. It is found that effective decryption as well as encryption must be developed in order to make the server much more secured from different attackers. The social networking sites are generally analyzed for adopting effective policies that helps in resolving the challenges as well as issues that are associated with data access. It is identified that both authorization as well as authentication must be adopted appropriately by different external attackers.

Security implementation in different channels of data transmission: Within the data transmission channel, it is quite important to implement proper security. The data that are generally transmitted between different suppliers must be kept way from various unauthorized external attackers.

References

Chae, B.K., 2015. Insights from hashtag# supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research. International Journal of Production Economics, 165, pp.247-259.

Christopher, M. and Ryals, L.J., 2014. The supply chain becomes the demand chain. Journal of Business Logistics, 35(1), pp.29-35.

Da Xu, L., He, W. and Li, S., 2014. Internet of things in industries: A survey. IEEE Transactions on industrial informatics, 10(4), pp.2233-2243.

Demirkan, H. and Delen, D., 2013. Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), pp.412-421.

Giannakis, M., Giannakis, M., Louis, M. and Louis, M., 2016. A multi-agent based system with big data processing for enhanced supply chain agility. Journal of Enterprise Information Management, 29(5), pp.706-727.

Hahn, G.J. and Packowski, J., 2015. A perspective on applications of in-memory analytics in supply chain management. Decision Support Systems, 76, pp.45-52.

Hazen, B.T., Boone, C.A., Ezell, J.D. and Jones-Farmer, L.A., 2014. Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, pp.72-80.

Hofmann, E., 2017. Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), pp.5108-5126.

Huang, Y.Y. and Handfield, R.B., 2015. Measuring the benefits of ERP on supply management maturity model: a “big data” method. International Journal of Operations & Production Management, 35(1), pp.2-25.

Kwon, O., Lee, N. and Shin, B., 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), pp.387-394.

Lee, J., Kao, H.A. and Yang, S., 2014. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp, 16, pp.3-8.

Li, J., Tao, F., Cheng, Y. and Zhao, L., 2015. Big data in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 81(1-4), pp.667-684.

Lu, T., Guo, X., Xu, B., Zhao, L., Peng, Y. and Yang, H., 2013, September. Next big thing in big data: the security of the ICT supply chain. In Social Computing (SocialCom), 2013 International Conference on (pp. 1066-1073). IEEE.

Ng, I., Scharf, K., Pogrebna, G. and Maull, R., 2015. Contextual variety, Internet-of-Things and the choice of tailoring over platform: Mass customisation strategy in supply chain management. International Journal of Production Economics, 159, pp.76-87.

Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S.J. and Fosso-Wamba, S., 2017. The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, pp.1108-1118.

Schoenherr, T. and Speier?Pero, C., 2015. Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), pp.120-132.

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