COLL 300 Outline essay

COLL 300 1

-Logistics has long been a data-driven field,utilizing bar codes, SKUs, demand forecasting tools, and later RFIDand other similar technologies. These techniques have been employedfor the tracking, movement and delivery of goods in a manner thatreduces costs of inventory holding. Recently, attention has beenshifted to address two major concepts in logistics which are big dataand the cloud.

-As a result, this study seeks to understand thestate of things in businesses and more especially in SME retailersand their regular use (or lack thereof) of big data in running theirlogistics management. The use of big data is growing steadily amongSMEs as it has been proven essential to improving supply chainmanagement and development of better managerial decision-makingskills.

BODY

-Big data are exceptionally large data sets thatare capable of computerized analysis to reveal patterns and trendsespecially relating to human interactions and behaviour. No detailedresearch or survey has been done explaining big data use amongbusiness enterprises. Similarly, the benefits of the two concepts andextent to which they have brought transformation to logisticoperations among enterprises are still unknown.

-Retailers are at the forefront in using thesedata sets as they work with thousands upon thousands of SKUs,therefore, need to manage logistics using massive amounts of data.Therefore, small and medium-sized enterprises are updated with thesetwo concepts and have employed them in the management of logistics.

-Large scale enterprises cannot accurately revealthe extent to which big data is being used.

  1. They possess remarkable economies of scale which allows them to adopt the best technologies available in the market.

  2. Big data is expensive too, and few SMEs can afford the data sets

-More research and surveys need to be conductedamong SMEs to reveal the extent to which big data is being utilizedby enterprises.

-SMEs should fully embrace big data. Data trendschange each day, and success and profitability of SMEs are embeddedin big data.

-The cost of big data needs to be reduced bytechnology companies to encourage more SMEsto use the data sets.

-The benefits of bigdata in logistic operations are threesome increased volume,velocity, and variety of data usage.

  1. Volume- More data is being recorded than previously. Instead of registering a unit of sale and location of the sale, the time and amount of inventory during the sale are also captured.

  2. Velocity- More value is extracted from the data as it moves in a quicker form. Quick data movement creates opportunities for faster and improved decisions among enterprises.

  3. Variety- More sensitive is variety which describes the assortment of data. Data is sorted according to sources such as internet sales, direct sales, and competitor sales.

-Big data needs to be embraced by all enterprisesregardless of business size as it is capable of:

  1. Boosting profits and enhancing business growth.

  2. Creating value for SME products and services

  3. Enhance customer retaining

CONCLUSION

-Use of big data is a new concept among smallerenterprises in the business environment. Most SMEs still do not usebig data as it continues to be embraced by large enterprises.

-Big data offers volume, variety and velocity ofinformation for enterprises. More value is added to SME products andservices as a result of big data use. Its use is slowly but steadilygrowing due to its improvements in supply chain management andmanagerial decision making.

References

Kethikidis, P., Koh, S., Dimitriadis, N.,Gunesekaran, A. &ampKehajova, M. (2008). The use of informationsystems for logistics and supply chain management in South EastEurope: Current status and future direction. WhiteRose Research Online. Retrieved April14, 2016 fromhttp://eprints.whiterose.ac.uk/3550/1/Ketikidis1_-_final.pdf

Mawhinney, L. &amp Self, R. (2015). . ISpractices for SME success series.Universityof Derby. Retrieved April 14, 2016 fromhttp://commerce3.derby.ac.uk/ojs/index.php/itpsme/article/viewFile/91/68

Prajogo, D. &ampOlhager, J. (2012). Supply chainintegration and performance: The effects of long-term relationships,information technology and sharing, and logistics integration.International Journal of ProductionEconomics. Vol. 135 (1) 514-522.

Waller, M. &amp Fawcett, S. (2013). Datascience, predictive analytics, and big data: A revolution that willtransform supply chain design and management. Journalof Business Logistics. Vol. 34 (2)77-84.

Wang, G., Gunesakaran, A., Ngai, E. &ampPapadopoulos,T. (2016). Big data analytics in logisticsand supply chain management: Certain investigations for research andapplications. International Journal ofProduction Economics. Vol. 176 (2016)98-110.

Wu, Y., Cegielski, C., Hazen, B. &amp Hall, D.(2013). Cloud computing in support of supply chain informationsystem management: Understanding when to go to the cloud. Journalof Supply Chain Management. Vol. 49 (3)25-41.

Zhong, R., Huang, G., Lan, S., Dai, Q., Xu, C. &ampZhang, T. (2015). A big data approach forlogistics trajectory discovery from RFID-enabled production data.International Journal of ProductionEconomics. Vol. 175.