Metcash project proposal Assignment PDF

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METCASH PROJECT PROPOSAL

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Executive Summary Despite being a clear market leader in the liquor wholesaling industry by holding a 69% market share, Metcash faces several challenges, including increased Industry participation driven by increased customer emphasis on alcohol premiumisation and falling per capita alcohol consumption; trends that can constrain the Metcash market performance[ CITATION IBI20 \l 3081 ] . As a result, Metcash is placing a strong emphasis on a strategic objective to align its product range with consumption trends to counteract these threats and expand its market share for its wholesaling liquor division.

Project Proposal Metcash and its competitors utilise traditional labour-intensive analytical methods that are extremely slow and laborious in generating valuable consumer insights. This industry standard creates a bottleneck that results in a slow supply chain adaptation to new consumer trends in the wholesaling liquor industry. Thus, to adapt accordingly to the volatile consumer trends impacting the Liquor wholesaling industry and achieve their strategic objective, Metcash should invest in AI-enabledcustomeranalyticsorCognitiveInsightsAI to support their highly automated, efficient distribution network. AI-enabled customer analytics refers to a subset of business intelligence that uses machine learning techniques to discover insights, find new patterns and discover relationships in the data. This project involves integrating AI-enabled customer analytics into Metcash's demand forecasting processes and distribution network to align Metcash's procurement and supply decisions with changing consumer preferences and trends. ProjectImplementation To integrate Cognitive Insights AI successfully into their liquor wholesaling division, Metcash must first: 

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Optimise internal business environment to support AI technology. Successfully integrating AI requires a dedicated team with a clear understanding of the goal at hand. Thus, Metcash needs to hire or train existing employees to create a team competent in understanding and debugging the system to maximise the benefits of AI. More importantly, there needs to be a cross-alignment between Metcash's business processes to ensure data connectivity.[ CITATION IIo17 \l 3081 ] Optimise data collection policies to ensure data is consistent, complete, and compact. With these three components, the data analysed by AI can be trusted to decipher valuable consumer insights . Low-quality data can impact the performance of a machine learning model (IIoT World, 2017). Run experiments by examining different machine learning approaches and testing each approach to determine the best combination for achieving the strategic objective[CITATION Har02 \l 3081 ] . Training a Machine Learning model usually takes a few tries before it's able to provide high-quality results. The number of times it needs to be tested depends on the data quality it uses and insights extracted by the algorithm

ProjectObjectives The objectives of this project proposal entail:    

Increased data processing efficiency Greater volume of more valuable consumer insights Smooth operation and integration of AI technology Greater supply chain efficiency when adapting to volatile consumer trends and preferences

Benefits/Linktothestrategicobjective AI-enabled analytics can give Metcash valuable insights in a fraction of the time it would take a human analyst while eliminating human bias and error when there are anomalies in data or when analysing a complex data space. This advantage of AI would help Metcash change its supply and procurement via changing consumer preferences and trends with greater efficiency. AI technology can also research and analyse thousands of data sets and sources instantly to discover consumers' exact preferences and interests. For example, Advanced AI algorithms can investigate demographic information, social media, and digital footprints of consumers to decode their specific tastes and preferences within a short time frame. The scalability of AI technology across a massive data set is a clear upgrade on labour-intensive data analytics, which is often overexerted when handling the large data processing requirements of a major company like Metcash. Utilising AI can subvert this trend, which leads to the slow supply chain adoption to consumer trends or provides mediocre analysis that wouldn't provide valuable insights to Metcash. The positive correlation between investing in AI-enabled customer analytics and promoting better consumer understanding is evidenced by a study done by the Boston Consulting Group. The study found that brands can create more effective personalised experiences by adapting to changing consumer trends and preferences via the integration of advanced digital technologies and proprietary data, i.e. AI-enabled customer analytics. The study concluded that companies who invested in new technology saw revenue increase by 6% to 10% — two to three times faster than companies who didn't.[ CITATION Bos17 \l 3081 ] Ultimately, AI-enabled technologies can help Metcash predict future consumer behaviours and attitudes, trends, and preferences with higher accuracy in a short time frame when compared to their current analytics processes . Metcash can ultimately utilise this project to achieve its liquor wholesaling operating division strategic objective. PotentialRisksandIssues  Data Bias -Although less biased than humans, an AI system's decisions are only effective if trained with unbiased data. If a particular population is underrepresented in the data used to train a machine learning model, the model's output might not represent Metcash's entire consumer population[ CITATION Cal21 \l 3081 ].  Internal conflict created by automation-integrating artificial technology into Metcash creates redundancies which can create worker discontent. Its essential workers support AI integration as many automated processes still require judgment calls because AI is still an imperfect technology.[ CITATION Cal21 \l 3081 ]

ProjectProposal-CompetitiveAdvantage A fundamental weakness within the wholesaling industry is a firm's capability to maintain profitability with price competition forcing many firms to adopt a high volume/low margin product strategy[ CITATION IBI20 \l 3081 ]. For Liquor wholesalers to distribute a high turnover of products, they must have an accurate and up-to-date understanding of current and future market demand to maintain profitability. If Metcash invests in this project, AIenabled customer analytics boost Metcash's capability to understand current market demand and predict future demand accurately. Thus, AI technology can provide Metcash with a competitive advantage by directly addressing an industry weakness that affects competitors. Purchasing and integrating AI-enabled technologies, especially within a vast and complex distribution network required by a wholesaler, is extremely expensive. On average, a single unit of AI-enabled technologies required for complex analytics can cost from $6000 to over $300,000, which includes development and rollout[ CITATION Web21 \l 3081 ]. This ultimately makes the prospects of investing in artificial technology exclusive for companies with high capital reserves. Outside Metcash and its biggest competitor, The Independent Liquor Group Co-operative (ILG), Australia's Liquor wholesaling market concentration consists of small and medium enterprises who hold 3-5% of market share individually. Of Metcash's competition, "50% of the wholesalers outsource operating activities within the industry, and a further 42.4% of companies employ fewer than 20 people"[ CITATION IBI20 \l 3081 ]. Thus, after considering the high capital costs required to partake in wholesaling industry, i.e., storage warehouses, transport and logistics vehicles, automated inventory systems, etc., and the historically lowprofit margins, many firms in the industry currently don't have capital reserves to match Metcash and implement this project. Metcash's domination in the market also ensures many small firms are forced to be price-takers from liquor manufacturers, which makes their costs variable to the volatile market conditions of the liquor wholesaling industry. This makes it extremely difficult for Metcash's competition to accrue the level of capital required to invest in AI in the long run. In the scenario, minor players in the market innovate to stay competitive, their investment will most likely be targeted at improving operating efficiencies, cutting costs, and improving warehouse efficiency to reduce labour costs. As evidenced by firms within the industry increasingly adopting automatic storage and retrieval systems (AS/RS) over the past five years to automate and streamlines operations at warehouses [ CITATION IBI20 \l 3081 ] . The likelihood of investment in a similar AI project is improbable, especially considering the rate of new innovative technologies beyond automation entering the industry is low, [ CITATION IBI20 \l 3081 ] . This is emphasised by the operations of the Independent Liquor Group Co-operative (ILG). ILG has no current or future initiatives to invest in cutting-edge technology beyond automation, despite holding the second largest market share in the industry and possessing large capital reserves.

If they pursue this project, Metcash gains a strategic asset via first-mover advantage as Cognitive Insights AI is not used in the liquor wholesaling industry. Moreover, AI also has specialised characteristics and capabilities that bestow a Metcash competitive advantage in customer analytics. Thus, after considering the heavy market concentration of SMEs, Metcash's existing domination within the industry, the cost of AI technology, and industry innovation trends compared to Metcash capabilities with AI, it is viable that this project's competitive advantage can last for 10+ years.

CompanyWACCcalculation CalculationMethodology Marketrateofreturn All Ordinaries were utilised as the reference market to calculate market returns. This market index comprises 500 of the largest companies listed on the Australian Securities Exchange (ASX), the largest amalgamation of shares in the ASX, making the index the most accurate substitute for the market index. Risk-Freerate As per industry standard, the risk-free rate is calculated as the rate of return of a "risk-free "Government bond." Based on the industry standard that " long term project planning should not be affected by short term market movements," a 10-year bond yield was the most appropriate risk-free rate[CITATION Bis18 \p 11 \l 3081 ]. This risk-free rate measurement is also consistent with the Market Risk Premium, which is calculated over 10 years. MarketRiskPremium Research by multiple academic sources has determined Australia's Market risk premium is between 5-8%, with most sources concluding 6% to be appropriate relative to the 10 year Government Bond Rate[CITATION Bis18 \p 19 \l 3081 ] . Thus, the Market risk premium chosen for this project is 6%. BetaCalculation SEEAPPENDIX1 Metcash's β for this project is calculated using the levered beta formula by utilising Woolworth's beta as a proxy. Woolworths is an appropriate proxy for Metcash because it operates in Australia's Liquor and retail divisions and is thus exposed to similar risk factors faced by Metcash. Moreover, Woolworth's extensive amount of stable historical price data via their listing on the ASX is sufficient to estimate a company β accurately. Woolworths Beta was calculated using historical data, which is adjusted for dividends and a continuously compounded rate of return formula, which is considered the most accurate rate of return methodology as per industry standard. Leveredbetaformula

Market values were used for equity values, and book values were used for debt in debt to equity ratio. Costofdebt The cost of Metcash's debt was calculated as Interest expense /average net debt. With the assumptions that Interest Expense = Interest paid (on debts) and Average Net debt= long term debt - cash. MetcashCompanyWACC(A/T):4.42% SEEAPPENDIX2

ProjectWACCJustification In 2018, Metcash signed an agreement with Complexica, an Australian company specialising in Artificial Intelligence software for supply & demand optimisation, to standardise and optimising its promotional activities for its liquor operating division. This project involved implementing Complexica's Artificial Intelligence engine Larry within Metcash's operations to analyse internal and external data and provide "intelligent "decision support for Metcash's promotional activities. The AI implemented for promotional planning is essentially a variant of the Cognitive AI required for this project proposal. Moreover, the actual function of the AI for the promotional planning project also draws parallels to the purpose of AI for this project, which suggests that Metcash would be well versed in dealing with inherent risks and costs and the processes of integrating the specific AI required for this project proposal. This is evidenced by Metcash demonstrating a "successful proof of concept" to executives before implementing the AI for their promotional planning project and Metcash's increased liquor sales since the project's implementation in 2018. Metcash also regularly invests in risky projects that cost over 100 million dollars, suggesting their investments to improve its operating divisions' productivity and competitiveness, i.e. (Sapphire Program, Warehouse automation) are "normal" business initiatives. Thus, it's clear that risks absorbed by Metcash during their "normal business activities " across all their operating divisions are similarly risky as this project proposal.

Metcash's previous implementation of Artificial technology, and regular investments in risky projects, suggest the risk profile and nature of this project proposal is similar to Metcash's normal business activities, which justify using theCompanyWACC for this project proposal.

APPENDIX

Appendix 1

Appendix 2

References Bishop, S., Carlton, T., & Pan, T. (2018). Market Risk Premium. Macquarie University, Department of Applied Finance.

Boston Consulting Group. (2017). BCG. Callahan, G. (2021, January 5). Rev. Retrieved from What Are the Potential Risks of Artificial Intelligence?: https://www.rev.com/blog/what-are-the-potential-risks-of-artificialintelligence Hardy, S. (2020, July 27). AMBIATA. Retrieved from Turning an AI strategy into an AI plan: https://www.ambiata.com/blog/2020-07-27-ai-strategy-to-implementation IBIS World. (2020). AUSTRALIA INDUSTRY (ANZSIC) REPORT F3606A. IIoT World. (2017, September 8). Retrieved from Implementing Artificial intelligence Solutions: Are Your Operations Ready?: https://iiot-world.com/industrial-iot/connectedindustry/implementing-artificial-intelligence-solutions-are-your-operations-ready/ Morningstar. (2021, April 17). Metcash Limited. Retrieved from Morningstar: https://datanalysismorningstar-com-au.ezproxy.library.sydney.edu.au/af/company/forecasts? ASXCode=MTS&xtm-licensee=datpremium Troung, G. (2008). Cost-of-Capital Estimation and CapitalBudgeting Practice in Australia. AUSTRALIAN JOURNAL OF MANAGEMENT, 3-10. Webfx. (2021, April). Webfx. Retrieved from AI Pricing: How Much Does Artificial Intelligence Cost?: https://www.webfx.com/internet-marketing/ai-pricing.html...