IND301B Industry project Assignment Sample
Individual/Group - Individual / Group
Length - 1500 / 2500 words (+/- 10%)
Learning Outcomes - The Subject Learning Outcomes demonstrated by the successful completion of the task below include:
a) Critically analyse and assess the IS technology that will support an organization’s goals and vision
b) Propose recommendations for an IS strategy
Task Instructions for online assignment help
In the Feasibility Report you will be required to:
a) Identify and describe the organisation which you have selected for your independent research and the industry in which they operate. Please ensure that your organisation has been approved by your Learning Facilitator.
b) Ascertain by research the problem which you will address for this organisation and describe this problem clearly in your report. The combination of the organisation and the selected problem together form the topic for this assignment.
c) Undertake a brief literature review of valid and reliable sources that you have used to
support your research.
d) Outline your research design and research methods and the data sources that you intend to use for your project.
e) A short project plan (timeline) on how you intend to develop your report.
Your Learning Facilitator will advise if you are completing this assessment individually or in a group. If you are completing the assessment in a group, form a group of 3 to 4 members. Please read the attached Group Work Guide document for information on group formation, registration and administration.
The organisation which will be focused on in this report is Billabong. Gordon Merchant established Billabong in 1973 on the Gold Coast of Queensland, Australia (Anderson, 2017). Billabong today sells a variety of clothes ranging from track pants to T-shirts and shorts, thanks to Gordon Merchant's debut with a board shaper and handcrafted board shorts. The company began with zero and has grown to become one of the world's largest corporations. It is one of Australia's major corporations with operations on four continents. It is a distributor and marketer of clothing, decorations, and sporting items, particularly in the renowned Australian sporting industry worldwide. Billabong International Limited's core mission clearly explains the organisation's goals for emphasising their goods and services. In addition, the stated mission specifies the company's performance targets, the methods the company implements to attain those objectives, target client segments, and the territory in which the company works (Billabong, 2022). The company's vision statement is very straightforward and up to the mark. It indicates that the company has not engaged in significant debates and discussions in order to express its ideas and perspectives to the general public and crucial collaborators. According to the study, Billabong's possibilities in its present condition are not promising (Anderson, 2017). Billabong's current troubles include massive indebtedness and an absence of a significant competitive edge, as well as a shortage of retail department managerial expertise, which is producing inventory turnover difficulties. The company is facing many difficulties in the supply chain and operational process due to such issues.
Concept of Big Data and Relation with Supply Chain
Big data is really a reasonably modern and popular idea that has the potential to improve businesses. The terminology "big data" refers to a large amount of information that is collected at a significant level from a variety of resources (Jin, 2019).
The study conducted by Fosso et al. (2018) revealed that the use of big data in the supply chain management significantly increases the efficiency of the business performances. It is considered important for business organisations to have a better control on the supply of materials, acquisition, manufacturing facilities, transportation, advertising, and various associated processes that allow products, operations, funds, and data to travel ahead and reverse (Richey, 2020). Govindan et al. (2018) also opined that big data help companies to have a close overview of business and operational activities along with identifying bottlenecks that slow down the performance of supply chain process. This in turn, supports in managing operational costs and streamline the supply chain activities of the organisations.
Big Data Analysis for Distribution Network
Outer streams and inner systems that incorporate network architecture or equipment in the manufacturing area produce a considerable volume of data in the manufacturing process. It is possible to enhance the effectiveness of the transportation and advertising operation as well as the constant surveillance of processes and items by applying big data for better evaluation and incorporation of various datasets. According to Nguyen et al. (2018), manufacturers must employ big data and analytical tools to expand the manufacturing base. The practice of picking the proper and appropriate seller for the distribution network is complicated because of the enormous numbers of sellers and the diversity of their identification and assessment factors. With the ability of web services and connection to current big data technologies and analytical packages, accessibility and visibility to statistics are more natural and consumer oriented with technological innovations (Mishra et al., 2018). Additionally, relational supplier management entails instituting self-control in strategy implementation and overseeing all conversations with a corporation's vendors in an attempt to minimise the threat of failings and enhance the significance of these relationships. In this process, developing tight connections with major suppliers and improving engagement with each other is critical to identifying and generating additional value and lowering the chance of loss (Ngai, 2018). Employing big data analytics tools, precise details on organisational purchasing behaviour may be obtained, which can aid in the management of supply chain operations and also supplier management (Gunasekaran et al. 2017). For instance, big data could offer detailed knowledge on the payback of every initiative's expenditure and a comprehensive assessment of possible suppliers. As per the view of Dubey, Gunasekaran & Childe (2019), businesses depend on supply chain management to get a competitive advantage; they must collaborate with inbound and outbound suppliers and be ready to optimise their supply chains. Consumers anticipate speedy logistics services and transportation, as well as a variety of different alternatives, designs, and functionalities. It is believed that organisations that fulfil these requirements would be documenting success tales. Tseng et al. (2021) mentioned that a crucial element is to provide client relevant data and projection analysis. Market analysis, product creation, sourcing choices, transportation, and consumer response are all sectors where big data plays an important part in supply chain operations.
Four Phase Modelling of Big Data Analysis
Nowadays, data does not travel in a predictable manner. Data transfers, particularly via technological interactions across many supply chain participants, increasingly appear to be a contemporaneous exchange (Hofmann, 2018). Big data analysis might be deployed throughout the end-to-end distribution network. The majority of businesses embrace big data to enhance their operations on a regular basis. In data analysis, there are four phases that are often used. According to Engelseth & Wang (2018), the initial phase is to make certain that the information is accurate, organised, and coordinated so that it can be analysed properly. The second phase is to verify that the correct information is available in the correct format, at the correct time, as well as in the correct location. Quantitative assessments, such as advanced statistics, are the third phase. Modern analysis, such as forecasting modelling, computerised systems, and real-time information processing, is used in the fourth phase. The effective application of big data might contribute to advancements in supply chain operations. A comprehensive awareness of market dynamics and client requirements would result in distribution networks that are flexible (Li et al., 2018). The application of big data, as well as advanced analytics in the complete operations of distribution networks, would generate sustainable production lines (Talwar, Kaur & Dhir, 2021).
Table 1. Four Phase Modelling of Big Data Analysis
(Source: Engelseth & Wang (2018)
There are mainly three types of designs used in research, and the explanatory design would be used in this research subject. The objective of the explanatory concept is to provide scholars with a more detailed understanding of a particular topic. It is primarily carried out for issues that have not been well explored in the past, and as a result, it establishes goals, produces an analytical framework, and gives a suitable framework for performing research investigations (Lim & Oppenheimer, 2020). It really is necessary when presenting new information regarding a report's perspective. In comparison to the other two research approaches, this one concentrate primarily on describing the importance of big data analytics on Billabong's distribution network in a precise and comprehensive way. Also, data evaluation can be achieved in two forms: first, through primary evaluation, and subsequently, through secondary evaluation. Secondary data evaluation will be performed in this research project in order to achieve superior research results. The investigation takes into account a variety of data depending on attributes, such as peer-reviewed articles, papers, and effective literary materials used by academics (Panchenko&Samovilova, 2020). Secondary data evaluation includes gathering material from publications, peer-reviewed papers, books, and other sources to determine the influence of big data analytics on Billabong's logistics management.
Table 2: Gantt Chart
(Source: Created by Author)
It would be necessary to perform a brief background study as well as research regarding the topic for understanding the effect of big data analytics on the distribution network of Billabong. Both of these could take 2 to 3 months. After analysing the background, the researcher would be required to determine the design of the research and the data collection technique that helps them to take the research further. After completing the gathering of data, the researcher must interpret that data and compile the results of the research in order to come to a conclusion regarding the research.
In several domains, like supply chain operations, big data analytics is considered a complicating factor. In this space, there are numerous opportunities for development in the implementation of suitable analysis methods. The report aims to indicate several of the most basic and latest features of big data analytics in logistics management, as well as to highlight a few important supply chain managerial processes for administrators. Various issues have emerged from the research such as market analysis, transportation and sourcing choices which could be properly addressed in the consultative report. The next steps of the process would be to address these issues with great concerns and find solutions. To mitigate these issues, the potential solutions for Billabong Company are:
? Billabong could strengthen its strategic planning and demand-sensing skills by utilising data from various sources.
? By helping supply chain managers to analyse their database to forecast when a specific client is more certain to be at residence, big data analysis could help them to bring items with reduced delivery efforts.
? Billabong might establish a significant data set to confront transporters and logistical solution vendors by integrating information on the cost segmentation of activities of vehicles and facilities around the world.
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