Data Collection System
This report depicts the importance of big data in supply chain management system. The key changes for organizational development and analysis in operations is referred to as the trust of employees and consumers in the results those are generated from the machines mainly from the industrial environment (Hazen et al. 2014). The end to end communication between organizational owners or managers to the employees and consumers are served with the help of advanced technologies such as big data and information system. However in order to meet the requirement of the consumers it is necessary to adopt proper tools and technologies in terms of supply chain management, customer relationship management etc.
The combined application of big data and supply chain management is increasing rapidly in most of the business organizations to increase their revenue model. The role of big data analytics in supply chain management includes many positive factors such as enhanced visibility, real time operation, accuracy in estimation etc. the data storage process, the data collection system, actionable data and business continuity approaches are elaborated in this report. This report illustrates the role of big data analytics in business organizations for improving the existing supply chain management system. However, certain issues are identified during the application of big data those are needed to be minimized, are also illustrated in terms of recommendations in this report.
In order to gain expected outcome form the business after the implementation of big data in supply chain management system, it is necessary to collect proper data through accurate data collection technique (Waller and Fawcett 2013). The different sources those are used for gathering data include procurement strategy, contract management, data driven supplier’s management and procurement assessment data assessment etc. The data those should be collected includes the following types:
Marketing data: Marketing data is referred to as one of the most vital and diverse tools from that support the business assertions. It is necessary to store all the information properly as empirical evidence (Schoenherr and Speier?Pero 2015). Marketingcharts.com is a data resource that is widely used for collection marketing data. For supporting the content of the enterprise comprehensive marketing data are found to be very useful. Even different data regarding the Business to Business (B2B) and Business to Consumers (B2C) are very much effective for developing different marketing strategies (Tan et al. 2015). This resource is much convenient than other marketing data. However, business scope, other complexity related to time and budget could be also minimized if big data analytics tool is used to collect marketing data from different resources.
Supply chain data: It has been found that most of the business organizations are facing high level issues due to lack of supply chain oriented data. The supply chain data includes both the data of suppliers and buyers (Monczka et al. 2015). The supplier’s data are required to be collected to minimize the supplier level risks and for making an appreciating co ordination (Dubey et al. 2016). The core transactional data and the internal systems data should be collected for making a better approach for the supply chain management system.
Data Storage System
Financial data: Financial are the other set of information those are required to be stored in the data server for making the supply chain management system much effective and efficient as well from the business perspectives. Whether the business is running and growing efficiently or not could be determined with the help of big data analytics tolls and financial data (Demirkan and Delen 2013). However the financial data are needed to be secured from the unwanted external access big data gives that level of security to the information of supply chain management system.
RFID data: Radio frequency Identifying device associated data are required to be stored in the data server to make the business operation much efficient and effective as well. Both the RFID and GPS big data helps to positioning and warehousing the real time inventories (Kwon, Lee and Shin 2014). In order to monitor the performance of the suppliers and buyers and for managing the risks in a proper manner, supplier’s big data are helpful.
In traditional days are data are used to store in the written documents those are managed by the management and marketing team of the business organizations. Written documents are not at all safe because those might be destroyed or hijacked easily thus, big data must be introduces to the supply chain management system to resolve this kind of issues for the businesses (Yin and Kaynak 2015). An unlimited amount of storage becomes available to the business owners, for manual data management. In addition to this, different rules and guidelines are also become available to the businesses after implementing big data analytics tools. It has been found that before the implementation of modern data collection approaches the data regarding supply chain management, customer relationship management and other confidential information are used to store through manual process (Papadopoulos et al. 2017). After the development of big data analytics tools and information management system, the supply chain oriented data could be stored in both the virtual storage and other storage devices also as per the user requirement. Automatic data collection is much effective than the traditional manual data collection (Zhong et al. 2017). In order to support the business needs it is necessary to add values in terms of technological components like data collection, storage and utilization is very much necessary. Even for major big data relevant applications, Google, Facebook, Walmart and other extensive number of servers are sent everywhere throughout the world. For supporting the business needs proper data are required to be collected through accurate data collection techniques. Big data provides enough storage to the businesses for With the help of big data analytics the data collection process has become much advanced from both the consumer’s and business revenue perspectives (Chae, Olson and Sheu 2014). Information might get hacked and found to be incorrect in case of manual data collection.
The information could not be accessed fast which is very much important for the organizational supply chain management. If the data could not be accessed on a timely manner then those data will become useless. Again in manual data management system the changed information could not be caught by the employees and consumers as well. The supply chain management research domain is vast and holds lots of technical approaches such as logistics, finance, information technology, and sourcing and operation management (Zhong et al. 2015). In order to manage the network and relationship between different business units like suppliers, buyers, facility providers, marketing managers and interdependent enterprises big data analytics is referred to as one of the most important tools. Even for forward and backward service and material flow big data is very much helpful. For transferring information from the actual producer to the final consumer with proper profit, addition of values for satisfying the consumers is very much important (Chae, Olson and Sheu 2014). In order to strengthen the interface immunity by removing the external attacks and mass hacking specialized storage outlining is required to be done by the management authority.
Business Continuity
In order to drive business with a repeated consumer loyalty and benefit as well, it is necessary to design consumer centric product for the business organization. However, based on the demand of the consumers it is necessary to modify the existing system (Holweg and Helo 2014). Advanced analytical technologies are required to be adopted such as big data analytics to improve the existing storage capacity and security of the information stored in the server related to supply chain management system. The components those should be considered include the consumer’s centric components, innovation, collaboration, data driven technologies etc. However customer centricity is not just about good customer services rather it deals with great offerings on consumers experiences for stage awareness (Groves et al. 2016). This become possible through the purchasing process and post purchasing processes.
This kind of product design approach puts the consumers as a first priority and the rest of the business components as the next one (Kozlenkova et al. 2015). In order to improve the consumer’s centricity the components those are strictly required to be considered include:
- Metrics that matter
- Front line empowerment
- Experience designing
- Understanding the consumers
- Consumer focused relationship
- Invoice for continuous improvement
In order to help the consumers the consumers data their behavior, demand, interest and engagement are required to be considered for identifying the opportunities to develop the products and services to grab more number of consumers from the competitive market. Not only this but also for customer segmentation the consumer’s lifetime value are required to be considered by the management authority. During the economic downturns power shiftage took place between the consumers and brand value. Big data helps to gather, organize and store data in the server in an accurate manner to utilize the information properly (Elgendy and Elragal 2014). In order to characterize and address the information firstly the scenes are needed to be comprehended properly maintaining the accurate manner. Big data analytics has the ability to connect huge number of consumers to the business considering their needs and requirements properly. In order to help the consumers by providing extreme level of support and openings towards instigation it is necessary to utilize the big data tools properly. Temporary workers are needed to serve the actual goal of the business organization and to resolve the difficulties. Extensive level of management knowledge and skills are required to overcome significant challenges that companies are facing throughout due to improper usage of the big data analytics tools (Holweg and Helo 2014). In the business organizations, through accurate application of big data different challenges could be minimized or diminished absolutely.
For this particular analysis the role of big data for improving the existing supply chain management of business organizations are defined. For the selected field of research, the consumers and the employees who are working in the business organization are the center of focus. Depending on the requirement of the consumers the information regarding suppliers and buyers are gathered and stored in the data server (Waller and Fawcett 2013). The consumers could search for their desired products and services through different websites supports and those are also developed based on the requirement of the consumers. Most of the large to medium business organizations are using the advanced big data analytics tool or bog data software for storing and securing data from the external attackers.
After analyzing the current supply chain management system it has been found that most of the business organizations are facing major level issues due to lack of expertise and improper utilization of big data software tools those are useful for improving the Supply Chain Management (SCM) system. The issues of traditional analytics tools could be minimized after the utilization of big data analytics tools in SCM (Schoenherr and Speier?Pero 2015). The complex file set could be stored securely in the server to secure them from the external attackers after the implementation of the big data tools. MongoDB, Hadoop, Cloudera are certain big data tools these are needed to be utilized by the management authorities to resolve the issues of supply chain management (Tan et al. 2015).
The application of all the big data analytics tools used to save time and money at the same time. The business insights those have never been revealed before could be exposed after the utilization of big data in the supply chain management. In addition to this, it provides a possible and strategic future layout to the consumers and to those who are working in the business organizations (Demirkan and Delen 2013). Among different tools accurate big data tool is needed to be adopted for improving the relationship between the suppliers and buyers of the business organization. In the area of data storage, extraction, cleaning, mining, analyzing, visualizing and integrating big data is very much helpful (Kwon, Lee and Shin 2014). This is one of the most widely used analytics tools that provide handling power of virtually limitless jobs and concurrent tasks in a business organization.
The open source software is useful for the distributed storage. The supply chain management of any business organization holds distributed nature of data. If no such kind of hardware failure occurs, then big data could work very much effectively. In the recommended system the components those are identified and needed to be improved and focused most for developing and improving a consumer’s centric model include role of customer and suppliers, technology, finance and tax and property and assets (Chae 2015). The supply chain data strategy can be developed after the implementation of the bug data analytics tools. The supply chain operating model, data based assets, data driven supply chain, strategic risk resolving approaches are involved in the recommended system. The data driven innovation and the product requirements could be easily served considering the ethical and sustainability consulting for designing the products, after the implementation of the big data analytics tools. Products could be developed with process management and product improvement approaches by considering the big data analytics tools (Chae, Olson and Sheu 2014). The other features those are possible though big data are as follows:
Enhanced visibility: Big data analytics help to increase the overall visibility of inventory level, the demands, the manufacturing and the capacity of the business organizations (Zhong et al. 2015). Thus, it can be said that big data analytics helps to increase the production level, schedule of distribution as well.
Real time operation: With the help of big data analytics the real time operations become easier. The data server for both the B2B and B2C business models has become easier after the implementation of big data analytics tools in the real time operation (Holweg and Helo 2014). Not only this but also big data helps to add values to supply chain processes and process evaluation. The sources, delivery and implementation approach everything become easier after the implementation of big data in the business organization. In addition to this big data also helps to take proper decision making technique to the owners.
Accuracy in estimation: Estimation of both time and budget become easier after the implementation of big data in supply chain management of any business organization (Elgendy and Elragal 2014). The transparencies at the SKU level become possible with advanced delivery approach and real time system optimization as well.
Business continuity is referred to as a process or capability to organize and continue the delivery approach of business procedure in terms of improvement (Groves et al. 2014). In order to gain measurable success and sustainable revenue from the competitive market it is necessary to plan proper continuous business improvement plan for the business organizations. There are certain common phases those are strictly needed to be implemented by the business owners to deploy the improvement plan.
For improving the supply chain model of business organizations the common phases those are needed to be exposed in an enterprise are as follows:
Selection of process: This is the initial phase for continuous business improvement. Different technology based processes are there those might effectively impact either positively or negatively the business organizations (Elgendy and Elragal 2014). However, based on the product and services and requirement of the consumers proper model is needed to be selected. Selection and implementation of accurate process will generate scope for continuous improvement.
Evaluation of process and standardization: It is very much necessary to select the accurate process and implement those in the real world. For supply chain management improvement the most effective and beneficial tools is the application of big data analytics due to its timeliness, trustworthiness, security approach and accessibility power.
Process improvement: After identifying the accurate process it must be implemented properly. The evaluated process should be implemented considering all other aspects of security and improvement plan as well. In order to improve the supply chain management system, very initially the issues are needed to be identified. Then certain challenging processes and tools will be applied on it to mitigate the preliminarily identified problems (Kozlenkova et al. 2015). For this particular phase the lifecycle event that is generally followed called as PDCA cycle (planning, do, check and act).
Conclusion
The concept of big data needs three different layers such as more data, processing system and analytics before its real field application. Big data analytics tool helps to shape supply chain of different business organizations. The volumes of data are interrelated to each other and might affect the cost and time management either positively or negatively. The components of supply chain include scheduling, delivering, demand forecasting, and warehousing, distributing, inventory planning and inventory management. This role of big data for improving the supply chain management system in a business organizations are elaborated in this report considering the data types those are needed to be stored.
Removal of meaningless points: Often it has been found that certain meaningless points are added up to the system. The analysts are needed to work hard for improving the current state of supply chain management. In order to resolve this particular issue the business organizations are required to hire expert team members.
Privacy problems: Privacy is referred to as one of the most important issues that are strictly required to be considered for resolving the privacy level issues and instance scenes also. Proper encryption and decryption approaches are needed to be developed to make the server secured from the external attackers. After analyzing the social network it is necessary to adopt accurate privacy for mitigating the issues of data access. Authentication and authorization are the others two approaches those are needed to be adopted so that none of the external attackers could come and hijack data from the data server.
Security implementation in data transmission channel: It is necessary to implement proper security within the data transmission channel. The information those are transmitted between the suppliers and buyers it is necessary to keep the transmission channel free from external unauthenticated access.
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