+ 44 20 8144 3706Introduction:
Unilever is the one of the largest FMCG Company in the world. Its target is to double its revenue without doubling the cost. It is believed that in order to achieve this target firm need to focus on knowledge management information system. So that information access and knowledge access can be equitable among employees. This will improve efficiency levels of the supply chain of Unilever. The main objective of the report is to identify ways in which KMIS can assist firm to improve its efficiency level in its business. In this regard, in the report, first of all overview of Unilever in terms of objective, corporate structure and business model is given. Threat and opportunity as well as strength and weakness of the firm are measured using models like PESTLE, Porter five forces and SWOT. Further, varied knowledge management, information systems are discussed that can be used by Unilever. Thereafter, KMIS (BI) is recommended for Unilever and ways in which it will assist firm to improve its supply chain operations is discussed in detail. At end of the report, challenges that Unilever may face in implementation of BI technology is also explained in detail.
Organization Overview of the Unilever:
Unilever is the one of the largest FMCG or fast moving consumer goods company in the world. It produces varied products in respect to personal and home care. The company mainly operates in the Asia, Africa, central Europe and also operates in the America. It is basically a British, Dutch company headquarters in the London (Unilever., 2020). The company currently owns 400 brands and its thirteen brands alone have sales of more than one billion Euros. Mentioned company has a large supply chain network across the globe and for making prudent decisions and to cost varied sort of information is required by the company employees. For this better knowledge management, IT infra is required by the firm.
Corporate objective:
The company’s major corporate objective is to remain in leading position in the industry and to double revenue without doubling the cost by generating economies of scale in the business. In this regard, the firm intends to adopt advanced technology by using which effective utilization of resources can be done in the business which will lead to a less elevation in production cost.
Corporate structure:
Unilever is operating at a global level in many nations and due to this reason it has strong and very supportive corporate structure in the business. In the company, there are geographic divisions which can be called as strategic business units. These SBU has their own management, which has the freedom to take most of decisions at their own level. Apart from this, for each product there is a separate department (Young., 2018). Each department has its own corporate executive team which looks after innovation and performance of the entire product line. Varied product line head directly report to the company CEO. In this way, entire structure works in the Unilever.
Implementation challenges:
Three implementation challenges are given below.
• Inaccurate data collection: In implementation of BI technology important thing to ensure is that the data obtained is error free. In supply chain entire data is stored in spreadsheets which probably remain inaccurate. In this regard IOT is used in the supply chain to track and authenticate products and shipments using GPS and to get accurate data for other supply chain operations (Heisig, 2015). Unilever is operating globally and it is hard for it to use an IOT on a large scale across globe to track its supply chain operations. Ensuring that data in the spreadsheet is accurate and use of IOT on a large scale is a key challenge in implementation of BI in Unilever.
• Data integration problem: Data in respect to a particular product on a particular day is obtained from multiple sources. While doing data modeling BI expert need to prepare data architecture to identify ways in which data integration will take place in the business. For this deep understanding of Unilever business operations is required as a company is operating at large scale across globe. Thus, identifying an appropriate way for data integration is one of the major challenge in implementation of BI in Unilever.
• Space issue with server: Effective working of BI system in the company depend on space that Unilever take on the cloud as entire data stored on cloud services like Amazon Cloud. If space remains less on a cloud storage, BI system does not work properly (Salama,. 2017). Unilever is operating globally and large quantity of data is generated in the business. In case space remains less Unilever need to remove some data from Materialized view that is on SQL database software. Thus, if space remains low then long period data cannot be obtained by BI expert to make decisions. It is hard task to estimate size of space that Unilever need to take on cloud for its BI related operations. Thus, it is one of implementation challenge for Unilever in respect to BI.
Reference:
Books and journals:
Alyoubi, B. A. 2015. Decision support system and knowledge-based strategic management. Procedia Computer Science. 65. 278-284.
Bah, E. H.. and Fang, L. 2015. Impact of the business environment on output and productivity in Africa. Journal of Development Economics. 114. 159-171.
Cerchione, R.. and Esposito, E. 2016. A systematic review of supply chain knowledge management research: State of the art and research opportunities. International Journal of Production Economics. 182. 276-292.
Heisig, P. 2015. Future research in knowledge management: results from the global knowledge research network study. In Advances in knowledge management (pp. 151-182). Springer, Cham.
Jennex, M. E. 2015. Knowledge management. Wiley Encyclopedia of Management. 1-6.
Lee, M. C. 2016. Knowledge management and innovation management: best practices in knowledge sharing and knowledge value chain. International Journal of Innovation and Learning. 19(2). 206-226.
Lim, M. K. and et.al., 2017. Knowledge management in sustainable supply chain management: Improving performance through an interpretive structural modelling approach. Journal of cleaner production. 162. 806-816.
Lorange, P.. and Rembiszewski, J. 2016. From Great to gone: why FMCG companies are losing the race for customers. Routledge.
Obeidat, B., Al-dalahmeh, M.. and Masa’deh, R. 2015. The role of knowledge management infrastructure in enhancing innovation at mobile telecommunication companies in Jordan. European Journal of Social Sciences. 50(3). 313-330.
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