ResearchProposal
COLL300 B003
AmericanPublic University
17April 2016
Abstract
Competitionin business has made every organization to be on the lookout for anyadvantage that will help an organization edge out another. Technologyhas led to the use of big data techniques in businesses that dealwith large data of information. It helps the organization to be ableto predict market patterns, thus enabling the organization to make avery effective decision in their logistic and supply chainmanagement. In the past, the only organizations which could affordthis technology were big corporations due to economies of scale. But,today through analysis of journal like InternationalJournal of Production Economics andJournal of Business Logistics, youunderstand that technology and innovation have made its accessibilityand application in the SMEs cost effective. With all theseadvantages, SMEs, in general, seem to adopt the use of big dataslowly. Is it financial, technological, technical or environmentalproblems that derail the adoption? These are the question which thisresearch seeks to answer. Also, to find out possible solutions thatcan be recommended. After carrying out the research with the use ofquestionnaires, it was discovered that SMEs that use big data had amean of 0.111 while those that did not use it, had a mean of 0.88.The data confirmed my thesis statement that the adoption of big datain SMEs is still at its dawn. The paper recommends SMEs to equiptheir personnel with knowledge on how to use big data, as it aids anorganization to make innovative decisions which can help organizationpredict future market trends.
Keywords:Bigdata, SMEs, Logistic Management, Supply Chain,
Withthe rise in technology and the need to make on point decisions, theutilization of big data analysis in business has become a crucialundertaking. Business need to keep track or rather understand bettertheir market and operations (Wang, Gunesakaran, Ngai, &Papadopoulos, 2016). Also, they want to be competitive, productiveand efficient, and how best can they achieve this goal, exceptthrough the adoption and utilization of big data technology in theirlogistics management. It will enable them to predict the likelihoodof an event and make timely decisions (Kethikidis, Koh, Dimitriadis,Gunesekaran, & Kehajova, 2008). The question remains, are thesebenefits to be enjoyed by just a few players in the market? And ifnot so, can small and medium-sized enterprises (SMEs) tap in thebenefits of big data analysis in their logistic management?
Theuse of big data techniques is becoming the game changer in thecompetitive world of business, where only the best enjoy while othersstruggle to survive, and their benefits cannot be overlooked (Wang etal.,2016). But still with all these positive factors associated withbig data, there seems to be a slow pace of its adoption in SMEs.
Statementof the Problem
Thereis a slow adoption of big data use in SMEs despite its immenseadvantages and improvement in technology which has made itsapplication cheap.
Purposeof the Study
Theresearch paper will seek to investigate why there is a slow rate orlevel of use of big data in SMEs’ logistics management and supplychains. Also, it will discuss the importance that can be derived fromtheir implementation. The study will be carried out through samplequestionnaires and surveys of SMEs and big business corporationsrespectively, that have adopted the use of big data. The reason forsampling big companies is to identify what are the potentialadvantages and problems related to big data and provide possiblesolutions and recommendations on my research to a similar problemthat might be faced by SMEs.
Scopeof the Study
Theresearch will be limited to finding out why SMEs are adopting bigdata technology in their logistic management and supply chains at aslow pace and the potential problems of its use.
Assumptionsof the Research
•Respondentswill give objective data
• Allsensitive information will be provided
• Mostqualitative data can be converted in quantitative, for analysis
Objectivesof the Research
1.To find out why a good number of SMEs have not adopted big data intheir organizations.
2.To investigate potential problems associated with handling andanalyzing big data.
ResearchQuestions
1.Why have SMEs not adopted big data in their logistics management andsupply chain?
2.What are the quantitative and technical problems associated withanalyzing big data?
LiteratureReview
Thebig data revolution is being driven by the availability of large datasets, and by technology capable of gathering, storing and processingsuch data sets. Logistics has long been a data-driven field,utilizing bar codes, SKUs, demand forecasting tools, and later RFIDand other similar technologies to order, track, move, warehouse anddeliver goods in a manner that reduces inventory holding costs,lowers lead times and aligns supply with demand as closely aspossible. The use of information systems to run logistics network isthus fairly established practice. Studies on the subject range fromthose examining the current usage of technologies in different partsof the world (Ketikidis et al., 2008) to those considering the mostpractical applications such as the prevalence of information sharingamong supply chain partners (Prajogo & Olhager, 2012).
Inthe past few years, two concepts have received a lot of attention inlogistics studies, big data and the cloud. Waller and Fawcett (2013)make the case that big data helps to advance logistics managementbecause, with many more data points, managerial decision-making isgoing to be more robust. They argue that teaching big data and itsapplications is essential to the education of future supply chainleaders (Ibid). Going along with big data is knowing when to use thecloud, with its advantages in remote access and its disadvantages inbeing able to handle the massive amounts of data that big dataapplications can generate (Wu, et al., 2013).
Researchin SME’s big data application at present remains exploratory. Partof this is simply because the technology is relatively new, and it isevolving rapidly. The rapid change not only challenges the businesscommunity on keeping up with the technology but also makes itdifficult for firms to adopt. Many authors are just at this pointseeking to understand the technologies that are in the marketplaceand advocate for its greater uptake across the business spectrum(Wang et al., 2016). Still other studies are beginning toinvestigate the integration of big data with other aspects oflogistics technology, such as RFID as a source of data gathering(Zhong et al., 2015).
Onewould think due to the technicality of big data analysis, itsapplication in SMEs can be costly due to the insufficiency of enoughcapital, but thanks to technology it’s cheaper. Growth intechnology has made everything more affordable, thus making the useof big data in SMEs logistic management cost effective (Kethikidis etal., 2008). Big business organizations are at the forefront in usingthese data sets as they work with thousands upon thousands of SKUs,managing their logistic with the use of massive data. Thisinformation is public knowledge yet the SMEs have dragged themselvesbehind in taking the leap of faith in the adoption of thistechnology.
The primary research question will focus on how small andmedium-sized retailers are using big data in their logistics systems. There are two reasons for working with these types of companies.First, retailers are at the fore of using this data. By theirnature, working with thousands upon thousands of SKUs, retailers havealways had to manage logistics using massive amounts of data. Thelargest retailers have tremendous economies of scale that allow themto adopt promising technologies early and use them to gaincompetitive advantage. By studying small and medium-sized retailers,it is possible to get a better sense of how much penetration into themarket the use of big data is. Big data is inherently expensive, butthe costs are decreasing as the technology evolves, and ultimatelythese companies need to expand their use of data to be competitive.
Therehave not been that many studies on this particular subject of SMEsand big data. One such study noted that there is a tremendousopportunity for such companies, especially as the cost of acquiring,storing and processing data becomes cheaper (Mawhinney & Self,2015). Thus, this study will seek to understand the state of thingson SME retailers and their use (or lack thereof) of big data inrunning their logistics management. My research will concentrate itsresources in seeking to answer the why or barriers to the adoption ofbig data and provide the direction as well as the advantages SMEswill tap if they adopt the use of bid data technology. My workingthesis is that the adoption of big data technology and techniques isnascent among SME retailers.
Methodsand Procedures
Thestudy will be comprised mainly of primary data, in the form ofsurveys and closed questionnaires. The first step will be to gatherthe data that is available, to better understand the concept of bigdata, and how it is presently used by the larger retailers. Thisinformation will contribute to the formation of specific surveyquestions. The surveys will be distributed to logistics managers froma group of big business corporations, to learn what their usage ofbig data is, if there are barriers to its adoption, and if they douse it what are their experiences. Then the closed questionnaireswill have questions formulated to answer whether SMEs use big data orthe plan to adopt it in a near future and what are the constraintsthe firm face in adopting big data. Due to financial constraint, theresearch will be conducted with a sample population of 20 SMEs and 5big firms which will be chosen randomly. This study will provideinsight into the state of SME retailers and big data techniques. Theinformation will be valuable to retail industries to betterunderstand what the needs of SME retailers are versus the largerones, and whether they feel that they need big data to compete.
Schedule
Thepreliminary research is the first step and can be completed in amatter of weeks. Gathering a list of potential subject companies,and the contact names of people in logistics, will probably takelonger – the contact information may or may not be readilyavailable. The survey questions will be designed by the time thecontact list is completed. The subsequent step will be to completethe mail out. I expect a lag time regarding response, between 2-4weeks.
Thecollection of data and its processing will be a slow process asironically this survey will be mostly qualitative in nature. Thiswill take a month. The final paper can be written and completed bythe end of May 2016 in rough form. This timeframe is necessarybecause of the scope of the project, and the fact that surveys have arelatively long lead time to get returns, given that the peopleanswering them are volunteering to do so, and will be doing so ontheir own time.
Dataand Analysis
Idistributed 20 questionnaires in 20 SMEs firm logistic managementdepartments. Of the 20, 18 questionnaires were responded to. Out ofthe 18 SMEs that participated in the filling of the questionnaires,only two organizations admitted to using big data in their logisticmanagement. Major concerns that were cited for not adopting SMEs werefinancial issues, lack of the technical knowledge. The above dataconfirms my thesis statement. These means that the average of SMEsusing big data is 0.1111 while those that don’t use, 0.888. Theseresults predict a low probability of big data utilized in the largepopulation of SMEs. While the number of surveys of big firms wasminimal there was positive and productive information about theimportance of big data application. They stated that the majoradvantage of using this technology was its ability to aid themanagement to make strategic decisions.
Recommendation
Theuse of big data can be complicated when the personnel handling it hasno idea of how to use its techniques. I recommend that organizationshould offer education in this field that allows logistics managementto equip its personnel with the knowledge in big data application.When the top level management is setting budgets for various businessinvestments, they should see the application of this technology as aninvestment which will bear its fruits with time. If finance is theissue, I recommend that organizations adopt its use with a slow pace.Big data will create value for SME products and services. Informationis made more transparent and therefore usable in time. Also, moreaccurate and detailed information is collected from the digitalsources and could range from inventories to employ sick days. Withthe use of SMEs future products and services can be developed as itpromotes innovation.
Conclusion
Thereis no doubt that the adoption of big data technology in the businessworld has been crucial. Their application in logistic management andsupply chains has helped the organization to predict market trendsand be able to make crucial decisions. Though its implementation hasbeen instrumental, the SMEs seem to be lagging behind, a concernwhich has become an object of interest to a number of businessacademic researchers. Academicians are interested in identifying whatare the potential barriers that SMEs face, having in mind that itsadoption has been made cheap by advancement in technology andinnovation. After identifying potential barriers, then researcherswill come up with viable solutions which are the objective of thisresearch.
Aftercarrying out this research, I found out that big businesses havequickly adopted and appreciated the advantages of the big data. Onthe other hand, the research proved that SMEs are lagging behind,this was supported by a small mean of SMEs using big data compared tothose that don’t. Though technology has been cited to reduce thecost of its use, some firms stated that they find its applicationexpensive and also, that there were barriers of its knowledge. Theresearch paper provided training, education and slowly adoption ofits use as probable solutions towards the use of big data by SMEs.
References
Kethikidis,P., Koh, S., Dimitriadis, N., Gunesekaran, A. & Kehajova, M.(2008). The use of information systems for logistics and supplychain management in South East Europe: Current status and futuredirection. WhiteRose Research Online.Retrieved April 14, 2016 fromhttp://eprints.whiterose.ac.uk/3550/1/Ketikidis1_-_final.pdf
Mawhinney,L. & Self, R. (2015). . IS practices 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. & Olhager, J. (2012). Supply chain integration andperformance: The effects of long-term relationships, informationtechnology and sharing, and logistics integration. InternationalJournal of Production Economics.Vol. 135 (1) 514-522.
Waller,M. & Fawcett, S. (2013). Data science, predictive analytics, andbig data: A revolution that will transform supply chain design andmanagement. Journalof Business Logistics.Vol. 34 (2) 77-84.
Wang,G., Gunesakaran, A., Ngai, E. & Papadopoulos, T. (2016). Bigdata analytics in logistics and supply chain management: Certaininvestigations for research and applications. InternationalJournal of Production Economics. Vol. 176 (2016) 98-110.
Wu,Y., Cegielski, C., Hazen, B. & Hall, D. (2013). Cloud computingin support of supply chain information system 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. & Zhang, T. (2015). Abig data approach for logistics trajectory discovery fromRFID-enabled production data. InternationalJournal of Production Economics. Vol. 175.