7105Iba Strategic Sourcing - Free Assessment Answer

Answer:

Introduction

The phrase big data is used to describe a very large quantity of data which are both in a structured and unstructured form which overburdens the daily activities of a business. Big data can be used by business to analyse and form better strategic decisions which could help in the growth of the business. The idea of big data is comparatively new. However, the act of accumulating large amounts of data for analysis has been prevalent since ages (Swan, 2013). This concept came into the light when Doug Laney who was an industry analyst segmented the concept of big data into three parts or Vs which are Variety, Volume and Velocity. Variety meant that there are different varieties or formats of data such as structured data and unstructured data (Provost & Fawcett, 2013). Volume meant that there are various sources where data can be acquired by organisations such as social media, business transactions and many others. In current times there are various new technologies which can be beneficial in the storing of this large volume of accumulated data. Velocity means that the amount of data grows at high speed and they must be properly and systematically gathered for it to be useful in the future.

Big data can be stored in a large volume, and it will keep growing with time. There is a lot of potentials for businesses to gain beneficial information from this data if it is analysed in a proper way (Wu et al., 2014). The most important characteristic of big data is not to collect large volumes of data but to use the available data in the most beneficial way. Big data can be used by organisations for many of their activities such as new product development, making smart and strategic decisions, reducing cost and time and many others. When big data is combined with analytics which is high-powered, then they can be used to achieve several tasks in an organisation (Chen, Mao & Liu, 2014). Several industries use big data in today’s time like healthcare, education, banking, retail, government, manufacturing and others. In this report, the contribution of big data to the procurement of Australia will be discussed along with the challenges that come with obtaining big data. Moreover, the ways in which big data is measured will also be determined in this report. Finally, certain recommendations will be provided regarding the usage of big data followed by a conclusion.           

Contribution of big data to procurement

Sourcing and procurement expenditure consists of a large portion of the revenue of an organisation. For this big data can be sued so that this area of the supply chain can benefit from it. In current times, applications for sourcing are primarily used to segment the different suppliers on the basis of risks, core capabilities, management of tail-spend and measurement of cost. They are also used to understand the purchasing behaviour of the consumers and using that data to negotiate with the suppliers. As stated by Richey et al. (2016) the information in big data can be used to provide procurement trends which would help the organisation to improve upon their quality product or service, negotiation, bargaining strength, efficiency of operations and better lead and delivery times. (Chen & Zhang, 2014) also discuss the same advantages of big data. However, none of these studies gives a deeper insight about the advantages or on the ways that it could be achieved. Procurement means to manage the sourcing done globally and the suppliers who are upstream. Global sourcing enables a large number of transactions, and these transactions need to be connected with the internal finance of the company. However, the majority of the cost is the external expenditure that is done by the company. Most of these external expenditures are not always consolidated with the internal costs. The main issue with this is the semi-structured form of the data which makes it hard to be consolidated together. Therefore, this integration of data should not only be done for external expenditures but for the whole procurement (Hashem et al., 2015).

It is mandatory for the stakeholders to gain knowledge of the expenditure of their money. Using big data analytics can help in preventing fraud in organisations. Moreover, when stakeholders get the details about their money, then it could help them to form better strategic decisions regarding sourcing (Fan & Bifet, 2013). Many organisations in Australia have been striving to make changes within their sectors so that they could be capable of using big data analytics by making several investments in services and tools. These changes would help them to change their process of business and develop new services and products. According to Vilajosana et al. (2013), there is a high demand for the processing of large quantity of data is increasing with time so that the organisations in Australia can keep pace with the high explosion of such large quantities of data. Big players such as Amazon Web Services, Google, IBM, Microsoft and others have been eagerly waiting for different buyers to spend on big data due to the extreme availability of information. The department for procurement is very important in Austria As well as in other countries. This is because procurement is the backbone of several organisations. Many important decisions are made in procurement such as the chasing if good and services, building relationships with the suppliers and the stakeholders of the company and making decisions which will reduce the impact of cost in organisations.

Schoenherr & Speier-Pero (2015) states that procurement is a huge contributor in the profitability of the organisation by providing them with better sources for supplying their goods and services, decreasing the costs of materials and managing all other processes regarding supply chain. Big data helps a huge deal in the procurement process by optimising and improving the different functions of the process. Big data helps the procurement professionals with more and better information so that they become efficient in their jobs. Data analytics can help in the reduction of costs by identifying those areas where the spending have been more or unnecessary, record the activities of the suppliers on the basis of their deliveries, identify the frauds that occur and make successful negotiation deals. Therefore, it has been derived that big data has made several contributions to procurement in Australia (Zhou, Fu & Yang, 2016). Big data analytics have helped to bring about a change in the procurement process and have enabled businesses to get better results and increase their profitability with the arrival of new technologies in big data analytics. The combination of a proper framework for monitoring and reporting of the deliveries, the huge feeds of data and the analytics which predicts the different changes helps to reduce the costs of the organisation and offer them more control and more visibility. Certain examples of organisations that use big data analytics for their procurement and value creation are Walmart and Amazon. They have used big data to make changes in their supply chain. This have helped them to make easier and faster deliveries to their customers in all areas.    

Measurement of big data

Big data is a large volume of data which means that there is no set way to measure it or its effectiveness in the organisations. However, with the change in time, there have been several solutions to this issue regarding the measurement if big data analytics and its performance effectiveness. Data as a whole is not important for any person organisation no matter which form it holds and whether it is structured or unstructured. A data only turns important when it aligns and helps in making possible the different business goals and outcomes. Therefore, the measurement of the whole data will have several options which would have no end. Hence, the measurement of data is only possible by measuring the performance of the organisation which has occurred based on the outcomes which were derived from the data. Varian (2014) states that the true worth of the big data is only understood when the capability of the organisation is measured. According to Tambe (2014), the potential of the data is understood when a business acquires the data and analyse it with the help of experts and then use the analysed data for forming their strategic decisions regarding the procurement process of the company.

One method of measuring big data in procurement is spend analysis which is the process through which the expenditure on data is first collected, then categorized and then analysed. As stated by Nakabayashi (2013) this method is adopted to decrease the cost of procurement and make the efficiency in the process by monitoring the compliance. The three main areas of the analysis are process, visibility and analysis. The spend analysis is a part of a bigger section which is known as spend management which incorporates three areas which are spend analysis, strategic sourcing and commodity management. There are several automated software for measuring spend analysis in organizations which is done for various reasons, one of them being profitability which is managed with the help of this method. Kim, Sures & Kocabasoglu-Hillmer (2015) also explains that the software will also help in understanding the expenditure and monitor the different items that the money is being spent on and this will help in the sourcing functions of the organization.        

Challenges of big data

Organisations face a lot of challenges with the initiatives that are taken by them for using big data. The challenges are as follows. Ammu & Irfannudin (2013) argues that firstly, the most common challenge of big data is the problem of gathering, storing and understanding or analysing the huge amount of data. There are various researches which establish that the amount of data and information that is being stored in the whole world is approximately doubling every two years. Most of the data that is present is unstructured data which means that it has no particular database to store it in. Such data can be very difficult to search among the other data and analyse them when the need arises. Therefore the management of unstructured data is becoming a major issue in current times. The major issue that this becomes for procurement is that the high amount of data become difficult to analyse by searching them from numerous documents, inboxes and other physical data. Secondly, the next challenge is being able not just to store the huge amount of data but to analyse them and gain insights from the data in a timely manner (Nasser &Tariq, 2015). Organisations collect these data in the hope that they will be able to use it for various functions of their business such as fund new opportunities and capabilities of services that they can generate, launch a new line of products and services, reduce the operational cost by making operations more efficient, create a path for new innovations and develop a culture which is data driven. In the scenario of procurement if the data is not analysed at the proper time then other organizations will gain competitive advantage over them. The field of procurement is such that every moment is of value to them as there is massive competition between every firm and the one who delivers the products at the correct time in the correct method by analysing the data becomes the major competitor for others.

Thirdly, for companies to make sure that their big data applications are operated and utilised in the right manner they need people who would be able to develop and operate those applications in the proper way. The main issue that arises due to this is that the people who are professionals in operating and managing big data analytics have high remuneration. This becomes quite expensive for the organisations to hire these experts who can manage these applications. Hence, the challenge is to appoint and retain these experts who are proficient in managing big data applications (Katal, Wazid & Goudar, 2013). This is very important in the procurement process as it is mandatory to hire people who can keep up with the speed of matching with the procurement process so that no false information is derived. Fourthly, the concept of data validation is very closely related to the concept of data integration. Organisations many times get the same kind of data from several systems, and those data might not be aligned with each other or match. For this, there is a system known as data governance which helps to match those data as well as make sure that the derived data are accurate and secured for further use (Sivarajah et al., 2017). However, it is up to the organisation to determine the mismatch of the data at the correct time so that it can be rectified and to find the correct data from the correct source so that no false data is analysed and used which could lead to various other problems. This is very important in procurement as analysing repetitive data will lead to a waste of time and also incur more cost which can be saved by data governance. Jagadish et al. (2014) derive that fifthly, another concern or challenge that is faced since the establishment of this system is the security of the big data. This is a big issue for the organisations to maintain that their stored data is safe and secure from any outside influence or theft. It is important to keep these data secure as there might be several sensitive information that might be present. The stores of the big data are also a huge attraction for hackers, and this could be dangerous for the organisations. Organisations have to install several security systems which would help them to keep the data safe. The procurement process is different for every organization hence stealing the data will make the organization lose their competitive edge. The above mentioned challenges are the most common and the most important ones in the field of procurement of an organization which needs to be solved and managed in the best possible way.

Recommendations

There are different ways in which these challenges can be resolved, and for this, there are different recommendations which can be used to maintain, use and secure this big data for procurement process. Firstly, to deal with the growth of data organisations should adopt different technologies which can be used to manage this data. For the management of data companies, a software-based technology is required so that a precise form is arranged and the companies can easily map their hardware. Digitalization of the unstructured data will help procurement to access the data whenever need and they will be able to unlock the full potential of the data according to their requirements. Hence, the adoption of technology for storage is important for every organisation (Gu, Li & Cao, 2014). Secondly, the goals that are incurred by the organisation will enable them to become more and more competitive. However, this will only be possible if the organisations are able to analyse the data in the proper way. The complete analysis of the data will help the companies to make faster decisions regarding any issue. Waller & Fawcett (2013) recommends that to get timely insights into the data proper tools of analytics to need to be used by the organisations which can help them to reduce the report generation time and help them in making the procurement process more strong and operational. The companies should invest in such software which would increase their capability of data analysis according to the competitive nature of the market that they exist in.

Thirdly, the shortage of talent who are accustomed to the use of data analytics software is very rare and expensive for organisations. To overcome this challenge, it is recommended that organisations could adapt certain options. Wamba et al. (2015) also state that organisations could increase their budgets as well as put more effort into recruiting and retaining those rare talents. They could also invest in the training of their current staff so that they can get the desired experts from within their organisation and not spend on any outside recruitment. Moreover, they could depend on technologies which would not need the expertise of a professional data analyst and could be learnt by the staff that are present in the organisation (Bertot & Choi, 2013). Hiring human resources will mostly help in the planning of a procurement process which will be successful and beneficial for the organization as opposed to purchasing technologies to do the same work. However, both of the solutions could be beneficial for procurement depending on the products and services of the company. Fourthly, to get the maximum business value from the big data companies could make the outcomes more customer oriented. This means that the analytics should help in the formation of strategies which will be able to provide better services to the customers which will help in retaining the customers. To do this, the organisations need, and they also need to invest in several new and expensive technologies of analytics which will provide them with the desired data. If this successful, the organisations will be able to connect with the customers through procurement who will be useful to the organisation.

Fifthly, organisations face many challenges with the governance of data since it a complex process which requires several policy changes and complex technologies. To solve this challenge and overcome it organisations need to hire a group of people who would be able to oversee the process of governance of data and will also write the required procedures and policies of the organisation (George, Haas & Pentland, 2014). Organisations in procurement also need to invest in solutions for the management of data which are formulated in such a way that they make the governance of data a simple process and also ensures that the stores of big data are accurate in their information. Sixthly, security is a big issue for organisations who use big data. That is why an organisation rely on heavy security systems to keep their data safe and secure and away from the clutches of hackers and privacy thieves (Inukollu, Arsi & Ravuri, 2014). There have been many evolutions in the methods and technologies of data security, and there are many companies who should invest in multiple security options so that their valuable data is not lost or stolen due to any reason. Moreover, security is not just used during the storage of data but also during the analysis process as well. These recommendations if followed by organisations would help them to get the maximum advantages from big data for procurement in every organization. Even though not all recommendations would be the same for every industry, however, these recommendations are mostly common for any company who has adapted to the technology of big data.                                       

Conclusion

Hence, it can be deduced from the above research report that big data is an integral part of every organisation in current times. To establish big data in the procurement in an organisation it is necessary to have a deep realization of the supply chain and of the way that big data works in favour of procurement. Companies used to adapt many complex solutions in earlier times to access and gather their data regarding procurement, however, organisations have seen with the change in time that big data could provide them many benefits regarding the same process in much lesser cost and time. This understanding has led many organisations to invest in technologies regarding big data and use it for the analysis of data which would be beneficial to them. The study also explores how big data can be measured. The method of spend analysis can be used to measure the success of big data in companies, which helps them to decrease the cost of procurement and help to determine the expenditure that is done by the use of big data.

Furthermore, in spite of its benefits, there are various challenges that are faced by organisations regarding the use of big data. It is mandatory for every company to overcome these challenges if they want to maximise the use of big data without any hurdles. Most of the challenges that are associated with big data are internal rather than external. The security and proper analysis are all internal problems that would occur inside of the organisation if big data is not managed in the proper way. There should be proper experts who should be hired by the organisations to keep their big data in a systematic way and also give recommendations to the management on how and where to properly use the data. The proper analysis of data is very important for the development of proper strategic decisions which could help the company in the long run and enable them to get a competitive edge in the market. The study also provides certain recommendations which could help the company to overcome the above mentioned challenges and any other challenges that might be faced in the future. The study gives an in-depth knowledge about the different ways that big data has contributed to the procurement process in Australia and how it has helped organisations to maximise their potential and make good strategic decisions for their benefit.

References

Ammu, N., & Irfanuddin, M. (2013). Big data challenges. International Journal of Advanced Trends in Computer Science and Engineering, 2(1), 613-615. Doi=10.1.1.380.2518

Bertot, J. C., & Choi, H. (2013, June). Big data and e-government: issues, policies, and recommendations. Proceedings of the 14th Annual International Conference on Digital Government Research, 1–10. Doi:10.1145/2479724.2479730

Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314–347. Doi:10.1016/j.ins.2014.01.015

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209. Doi: 10.1007/s11036-013-0489-0

Fan, W., & Bifet, A. (2013). Mining big data: current status, and forecast to the future. ACM SIGKDD Explorations Newsletter, 14(2), 1–5. Doi:10.1145/2481244.2481246

George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Doi: 10.5465/amj.2014.4002

Gu, M., Li, X., & Cao, Y. (2014). Optical storage arrays: a perspective for future big data storage. Light: Science & Applications, 3(5), e177-e117. Doi:10.1038/lsa.2014.58

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115. Doi:10.1016/j.is.2014.07.006

Inukollu, V. N., Arsi, S., & Ravuri, S. R. (2014). Security issues associated with big data in cloud computing. International Journal of Network Security & Its Applications, 6(3), 45. Doi: 10.5121/ijnsa.2014.6304

Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86–94. Doi: 10.1145/2611567

Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013, January). Big data: Issues and challenges moving forward. In System sciences (HICSS), 2013 46th Hawaii international conference on (pp. 995-1004). IEEE. Doi 10.1109/HICSS.2013.645

Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: issues, challenges, tools and good practices. IEEE, 404–409. Doi:10.1109/IC3.2013.6612229

Kim, M., Suresh, N. C., & Kocabasoglu-Hillmer, C. (2015). A contextual analysis of the impact of strategic sourcing and E-procurement on performance. Journal of Business & Industrial Marketing, 30(1), 1–16. Doi: 10.1108/JBIM-01-2012-0010

Nakabayashi, J. (2013). Small business set-asides in procurement auctions: An empirical analysis. Journal of Public Economics, 100, 28–44. Doi:10.1016/j.jpubeco.2013.01.003

Nasser, T., & Tariq, R. S. (2015). Big data challenges. J Comput Eng INF Technol 4: 3. Doi: https://dx.doi.org/10.4172/2324, 9307(2). Doi: 10.4172/2324-9307.1000133

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59. Doi: 10.1089/big.2013.1508

Richey, R. G., Morgan, T. R., Lindsey-Hall, K., & Adams, F. G. (2016). A global exploration of big data in the supply chain. International Journal of Physical Distribution & Logistics Management, 46(8), 710–739. Doi: 10.1108/IJPDLM-05-2016-0134

Schoenherr, T., & Speier?Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120–132. Doi:10.1111/jbl.12082

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. Doi:10.1016/j.jbusres.2016.08.001

Swan, M. (2013). The quantified self: Fundamental disruption in big data science and biological discovery. Big Data, 1(2), 85-99. Doi: 10.1089/big.2012.0002

Tambe, P. (2014). Big data investment, skills, and firm value. Management Science, 60(6), 1452–1469. Doi:10.1287/mnsc.2014.1899

Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28. Doi=10.1257/jep.28.2.3 

Vilajosana, I., Llosa, J., Martinez, B., Domingo-Prieto, M., Angles, A., & Vilajosana, X. (2013). Bootstrapping smart cities through a self-sustainable model based on big data flows. IEEE Communications magazine, 51(6), 128–134. Doi:10.1109/MCOM.2013.6525605

Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84. Doi:10.1111/jbl.12010

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. Doi:10.1016/j.ijpe.2014.12.031

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. Doi:10.1109/TKDE.2013.109

Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56, 215–225. Doi:10.1016/j.rser.2015.11.050