Bullwhip effect in supply chain management

Executive summary

The purpose of the report is to bring about an analysis of the Bullwhip effect that is persistent in the supply chain management of organizations. The report will draw attention to the importance of information in the stages of the supply chain network. Further, the report will talk about the bullwhip effect and the way it can be mitigated through GDPR principles. This relationship will be studied through literary sources. Data minimization will also be focussed on in this context. In the last section, the report will talk about the usage of data analytics in extracting useful information from the customer data. Finally, the report will suggest recommendations based on the present research about monitoring customer data in organizations to protect the privacy and confidentiality of the datasets.

Table of content

Introduction

The purpose of the report is to highlight certain essential elements of supply chain management. The report will focus on the Bullwhip effect, which is a common phenomenon that is noticed in the process of the supply chain. A brief description of the usage of information will be given in the context of the supply chain with its specific utility in the operations planning stage. Further, suitable literature will be studied based on four critical elements of the supply chain, that is, bullwhip effect, seven principles of GDPR, lawfulness, fairness, and transparency of information and data minimization. The current trends applied in data analytics would be examined for monitoring the requirement of customer data in organizations. In the purview of this analysis, a judgment will be provided regarding the utility of new legislation in minimizing the potential abuse of customer data. Suitable conclusions will be drawn from all the observations.

Discussion A brief overview of the importance of information in the supply chain

Information is an essential element that is required in every step of the supply chain process. The entire chain or network of the supply chain is dependent on the flow of information for its effective functioning. The decision-making process of the supply chain is dependent on the collection and analysis of information. Appropriate selection and examination of data are required for the performance improvement of the supply chain. For using relevant information in the supply chain process, suitable software is used that captures accurate data through advanced technology (Bania 2018). Information should have specific characteristics for fitting suitably in a supply chain. It should be reliable, accessible, shared, and of the right kind. All the three levels of supply chain process that is a strategy, planning, and operations, require information for their functioning. Along with this, the supply chain drivers, that is, facilities, transportation, sourcing, inventory, and pricing. Among all the levels, the operational level requires the maximum usage of information, which will be elaborated in the next section.

Operation planning is a critical task in any supply chain process. This is because operation planning seeks to create a framework for developing and implementing operational procedures for the supply chain process. In a general sense, functional analysis requires three significant steps, that is, scheduling the establishment, collecting data and information, and analyzing gaps. The most crucial step in this process is the collection of data and information. Types of data depend entirely upon the kind of operations that will be performed by the companies (Rustad 2019). The planners apply the appropriate mode of data collection following the chosen activities. Data collection helps the experts to have a probable idea of the actual performance of their companies.

Literature review

Bullwhip effect

Many literary studies have been conducted for analyzing the impact of the bullwhip effect in the process of supply chains. According to Ojha, Sahin, Shockley, and Sridharan, bullwhip effect has an indirect relationship with the role of information sharing in supply chain management. It has been observed that the companies that use the information for coordinating the orders of the supply chain tend to face fewer consequences of the Bullwhip effect (Ojha et al. 2019).  In other situations, when the firms do not rely on sharing information, there is a significant trade-off between order fulfillment and negative impacts of the Bullwhip effect.

Seven principles of GDPR

Scholars have extensively researched the seven fundamental principles that are laid down by the General Data Protection Regulation (GDPR). These principles have set up a framework for protecting data and setting up compliance methods for adhering to regulations of data protection. According to Dasgupta, GDPR principles are required for managing supply chains as these principles provide a direction for accessing quality data through private and secured platforms (Dasgupta et al. 2019, July). Researchers have produced a 4I model to understand the exact application of GDPR on supply chain management. The 4I model improvises four components, that is, identification, insulation, inspection, and improvement.

Lawfulness, fairness, and transparency

Many researchers have researched the seven principles of GDPR concerning the protection of customer information, especially in supply chain management. According to Eriksson, one of such significant laws is lawfulness, fairness, and transparency of information in the process of the supply chain. The criteria of lawfulness relate to the protection of personal data of customers data such that it is not leaked by any third party (Eriksson 2019). The fundamental rights of the customers are governed by law. Transparency relates to the awareness of the data subjects concerning the processing of their data and information such that they can receive the results of such activities.

Data minimization

Data minimization is a principle that has been proposed in one of the guidelines of GDPR. According to this principle, the collected and processed data cannot be further used for any other purpose (Pastore et al. 2020). The data can only be used if it is required for specific essential reasons, which are stipulated in the guideline of data privacy. According to GDPR, data minimization is defined as data that is adequate, relevant, and limited only to the purposes for which they have been collected and processed. At regular intervals, the data minimization principle is evaluated and strengthened by GDPR such that customers can be protected from risks and security threats of their private data and information.

Current trends in the use of data analytics and predict future practices as government steps for monitoring the use of customer data in organizations

Data analytics is a fundamental process by which the analytics involved in supply chain management can make decisions based on qualitative and quantitative data. This technique is commonly referred to as supply chain analytics. The analytics are capable of analyzing the datasets for coming up with good ideas or options for building up the supply chain (Singh et al. 2018). The examination of data is performed visually through appropriate charts, graphs, and other visuals. The analytics also use this data for revealing patterns of a particular segment and getting insights about the division. Currently, organizations have developed many ways of using data in the supply chain process. Some of these techniques can be elaborated below.

• Descriptive analytics – This analysis is used for a single source of data across the entire supply chain. The data provides visibility of a single source of information that is applied in the internal and external systems of the supply chain.
• Predictive analytics – This analysis is based on a futuristic scenario, where the organization tries to investigate the most likely result or outcome of a particular data or information (Zhang et al. 2016). Through this forecast, the organization can develop strategies for mitigating risks and improving its b supply chain process.
• Prescriptive analytics – This kind of data analysis helps in solving the issues and problems of an organization. This analysis helps in providing the maximum business value to the organizations (Newhouse and Weeks 2016). Through this analysis, the logistics organizations can associate with the partners for minimizing the disruptions through reduced time and effort.
• Cognitive analytics – This analysis is based on the cognitive ability of an individual. Through this process, the analysts can answer complex questions in natural languages, which can be easily deciphered by the team (Tzolov 2018, September). Through this analysis, the companies can take up complex issues to solve and optimize the elements of their process.

The advanced form of supply chain analytics can be used exclusively for the optimization of the supply chain process. Through this process, the companies use large amounts of data for performing a large number of activities related to their supply chain process (Colcelli 2019).  Forecasting, identification of loopholes, better response to customer demands, and deploying innovation can be some of the objectives achieved through the process of supply chain analytics.

The government of many countries can easily take up several steps for monitoring the use of customer data in organizations. A survey can be conducted that will focus on the techniques applied by the companies for the collection and usage of data. The data provided by the consumers on the internet should also be thoroughly investigated to understand their behavioral aspects (Haugseggen and Fauske 2019). The government can also take necessary measures to indicate a price on data. This can be done through the process of bidding, where the consumers are required to bid for the protection of their data. The companies are required to understand the worth of data to the customers. Accordingly, they can commensurate value in return for the data. This exchange activity can be made transparent to build up trust and loyalty among the customers. Another way to monitor data is by providing it a swapping value. This value represents the price that the customers can pay for swapping their data with the company.

Importance of legislation for curbing down the gathering and potential abuse of customer data

Following the above steps, data security can be improved up to a greater extent. Along with the above steps, the government should take up regulations related to the compliance activities and safeguarding of consumer data (Nelson Corengia and Moreschi 2019).  The companies can enlist the datasets of the customers to have a more comprehensive picture of them. The insights of the data market should also be collected at regular intervals, and the government must also take initiatives for deploying the latest fraud management techniques.

Conclusion

From the above report, it can be stated that consumer data and information are critical elements of any supply chain management process. In this context, the bullwhip effect reduces the privacy and confidentiality of the datasets of the consumers. Therefore, the principles of GDPR should be extensively used for protecting the rights of the consumers. Literary articles have been studied to understand these principles and to come up with suitable measures for enhancing the data protection of the consumers. In this way, the potential abuse of data can also be curbed down up to a more significant extent.

References

Eriksson, D., 2019. The GDPR’s lawful basis of legitimate interest: Advice and review regarding the balancing operation as of GDPR Article 6.1 (f).

Ojha, D., Sahin, F., Shockley, J. and Sridharan, S.V., 2019. Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type. International Journal of Production Economics, 208, pp.529-543.

Pastore, E., Alfieri, A., Zotteri, G. and Boylan, J.E., 2020. The impact of demand parameter uncertainty on the bullwhip effect. European Journal of Operational Research, 283(1), pp.94-107.

Singh, A., Shukla, N. and Mishra, N., 2018. Social media data analytics to improve supply chain management in food industries. Transportation Research Part E: Logistics and Transportation Review, 114, pp.398-415.

Zhang, F., Johnson, D., Johnson, M., Watkins, D., Froese, R. and Wang, J., 2016. Decision support system integrating GIS with simulation and optimisation for a biofuel supply chain. Renewable Energy, 85, pp.740-748.

Colcelli, V., 2019. Joint Controller Agreement under GDPR. EU and comparative law issues and challenges series (ECLIC), 3, pp.1030-1047.

Haugseggen, L.A. and Fauske, H.G., 2019. The Impact of Digital Vulnerabilities on Organizational Resilience: A case study of different perceptions in a supply chain (Master’s thesis, Universitetet i Agder; University of Agder).

Nelson Corengia, E. and Moreschi, G., 2019. Blockchain in Supply Chain.

Newhouse, W. and Weeks, S., 2016. SECURING NON-CREDIT CARD, SENSITIVE CONSUMER DATA.

Rustad, M.L., 2019. How the EU’s General Data Protection Regulation Will Protect Consumers Using Smart Devices. Suffolk UL Rev., 52, p.227.

Bania, K., 2018. The role of consumer data in the enforcement of EU competition law. European Competition Journal, 14(1), pp.38-80.

Tzolov, T., 2018, September. One Model For Implementation GDPR Based On ISO Standards. In 2018 International Conference on Information Technologies (InfoTech) (pp. 1-3). IEEE.

Dasgupta, A., Gill, A.Q. and Hussain, F., 2019, July. A Review of General Data Protection Regulation for Supply Chain Ecosystem. In International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (pp. 456-465). Springer, Cham.