Today, the majority of customers are not up to how things are completed or why firms are evenly doing it, but what they are concerned of is the consistency of the quality of products and services they could get. Walmart, leader in retail industry, has seen improvement in its operations and profit for the past years of operations. However, with the increasing competition in the market, a sound operational approach should be made by them. This is because they usually face different challenges that hinder their smooth flowing business. One challenge for Walmart is how to improve their system regarding the transferring of supply from one country to another. The transfer of supplies is affected by trade policies of other countries, but it can be greatly affected by the lack of proper system which the company will use to manage their supplies. The transfer of supplies will not only be the problem, but also the people who would handle the products that will be produced and transferred to different places and countries. The stakeholders of the company such as suppliers, the people who will work on the raw materials and the workers who will distribute it to the customers must be appropriately trained and managed to avoid confusion within the management.
Logistics and data management are one of the most important operations of any business enterprise. It is referred to as the process of planning, implementation, and control of the efficient, effective, forward and reverse flow and storage of goods, services, and related information between the point of origin and the point of consumption in order to meet new customer requirements. In order to solve these challenges, Walmart employed Big Data Analytics for the management of data. However, with the increased realization of the importance of holistic approach in business enterprise, such as physical distribution, material management, procurement and supply, product flow, supply chain management, and demand chain management; hence, big data faced big problems. With the vast scope of data in the field of agriculture, healthcare, manufacturing, and public sector, Baldwin asserts that big data “collects useless data that helps people develop false and inaccurate assumptions [15th par, 2014].” Comparatively, it is getting harder to Walmart to determine the accuracy of data collected based on the exchanges of information from a heterogeneous system.
With the determination of the objectives related to the development and implementation of big data analytics management, the perception of the developers about these systems could be the significant step to be known for the retail giant aside from the viewpoints to be formulated by the users and managers of the firms [Pham 2016]. In particular, the views of the developers of big data analytics are crucial in the determination of the outcomes of enterprise system activities. Developers are expected to be involved in the making of decisions related to the design and performance of big data as it is integrated within the operations of Walmart.
A problem that can be encountered by information technology consultants in the field of healthcare, public sector etc. in the implementation of big data analytics would be the different interpretations associated by the technical terms used in information systems and technologies. The lack of pertinent performance dealings has recognized as one of the major problems in big data analytics management. Because of the different views on what should constitute the process improvement that may impact on service delivery and supply chain performance, analysts of Walmart’s employment of big data find it complicated to practice the ultimate performance. The major contributing factor to this problem is the costs that may be appropriated and the lack of in-depth knowledge on how to optimize the process improvement of its operations.
Reference
Baldwin, Howard. [2014]. Big Data’s Big Impact Across Industries. Retrieved October 22, 2016 from Forbes.
Pham, Peter. [2016] The Impacts Of Big Data That You May Not Have Heard Of. Retrieved in Forbes dated October 20, 2016.
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