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Assessment of the Supermarkets and Grocery Stores Sector in Australia: A Case Study of Woolworths and Coles using DEA and VAIC™ Van Kampen, Toine; Kirkham, Ross https://research.usc.edu.au/discovery/delivery/61USC_INST:ResearchRepository/12132822630002621?l#13137209350002621

Van Kampen, T., & Kirkham, R. (2020). Assessment of the Supermarkets and Grocery Stores Sector in Australia: A Case Study of Woolworths and Coles using DEA and VAIC™. Journal of New Business Ideas & Trends, 18(1), 1–11. https://research.usc.edu.au/discovery/fulldisplay/alma99482293102621/61USC_INST:ResearchRepository Document Type: Published Version

USC Research Bank: https://research.usc.edu.au [email protected] Copyright © 2020 JNBIT. Reproduced with permission of the publisher. Downloaded On 2021/09/09 00:56:48 +1000 Please do not remove this page

Van Kampen & Kirkham – Volume 18 Issue 1 (2020)

Journal of New Business Ideas & Trends 2020, 18(1), June, pp. 1-11. ”http://www.jnbit.org”

Assessment of the Supermarkets and Grocery Stores Sector in Australia: A Case Study of Woolworths and Coles using DEA and VAIC™ Toine Van Kampen KPMG, Australia Ross Kirkham University of the Sunshine Coast, Australia

Abstract Purpose – The purpose of this study is to examine the level of efficiency in the retail Supermarket and Grocery Stores sector. Design/methodology/approach –Financial data from the annual reports of two companies registered on the Australian Stock Exchange (Woolworths and Coles) was extracted for the four years 2016 to 2019. Two models were used, DEA and VAIC™ to analyse the financial data to determine element s to assess the level of efficiency each company was achieving. Findings – There was some degree of diversity identified as existing between the two companies. The results from the DEA and VAIC ™ produced similar outcomes however, the two financial ratios (ROA and CFOROA) produce somewhat contrasting results in terms of differentiating between the performance of the two companies. Research limitations/implications – Whilst there are no industry or sector standards available the findings stand as relevant for the purpose of comparison between the two main players in the sector. These companies both have a high degree of diversity, however they share very similar forms of diversity in regards to specific segments. Keywords: Intellectual capital; financial statement analysis; value added intellectual coefficient model (VAIC™); data envelopment analysis (DEA); Supermarkets and Grocery Stores.

JEL Classifications: M14 PsycINFO Classifications: 3650 FoR Codes: 1501 ERA Journal ID #: 40840

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Introduction

. As of 2011 there were almost 140 000 retail businesses in Australia, accounting for 4.1 per cent of GDP and 10.7 per cent of employ ment (Productivity Commission Report, 2011). Australia also appeared to be lagging behind a number of comparable countries with regards to the development of online retailing. The Productivity Commission (2011) estimated that at the time online retailing represented 6 per cent of total Australian retail sales and this was broken down into 4 per cent domestic online ($8.4 billion) and 2 per cent from overseas ($4.2 billion). The retail industry is comprised of a diverse number of sectors which reflect the nature of goods sold and the retail format encompassed by the various business structures. In this regards the most significant type of retail operation that plays a vital role in the domestic market would have to be represented by the category of Supermarkets and Grocery Stores. This sector is one of the most competitive in Australia. Supermarkets and grocery stores are involved in retailing a range of groceries and food products, including fruit and vegetables, bread, cigarettes, canned goods, toiletries, dairy goods, delicatessen items and cleaning goods (IBISWorld report, 2020). With the seemingly explosion of ALDI stores the sector has significantly altered the industry's operating focus, with smaller supermarket chains closing or being taken over. ALDI's presence has caused the two established giants of the sector, Woolworths and Coles, to engage in cutting prices and expanding their private-label product ranges in response. As a result the companies that now hold the largest market share in the Supermarkets and Grocery Stores in Australia are effectively, Woolworths Group Limited, Coles Group Limited, Aldi Stores (A Limited Partnership) and Metcash Limited (IBISWorld report, 2020). For the purpose of this study, the evaluation of the performance of the two leading companies in the Supermarkets and Grocery Stores, Woolworths Group Limited, Coles Group Limited, will focus on the performance efficiency of the organisations.

Literature review Research into the retail industry and specifically the supermarket and grocery store sector has involved the use of rather diverse methods and studies have examined performance and efficiency using a disparate range of variables and issues. Efficiency of the retail supermarkets has been examined using data envelopment analysis, Sellers-Rubio and Mas-Ruiz (2006) examined the performance of 100 Spanish supermarket chains over the period 1995 to 2001 they used number of employees, number of outlets and capital to operationalise the inputs and to operationalise the outputs they used sales and profits they found high levels of inefficiency. In a similar study, Athanassopoulos and Ballantine (1995) used DEA to compare the efficiency of supermarket chains operating in the United Kingdom using capital employed, fixed assets, number of employees, number of outlets and sales area to operationalise the inputs and total sales to operationalise the output. Barros (2006) used a two stage approach, involving the use of DEA and a Tobit regression model, to examine the efficiency in Portuguese hypermarkets a nd supermarkets. The inputs were operationalised as labour, and capital while the outputs were operationalised as sales, operational results and value added. The findings were that the efficiency of hypermarkets and supermarkets was high compared to the levels found in other sectors and that larger retail groups were generally more efficient than the smaller retailers. In a study involving a variation on the DEA model Vaz, Camanho and Guimarães (2010) examined supermarket store in Portugal and operationalised the inputs using the number of different products on sale, the value of the products for sale, the value of products stolen, spoiled or disposed after expiry date. Taking a different approach, Kämäräinen, Småros, Holmström and Jaakola (2001) examined the cost effectiveness of the relatively new phenomena of e-grocery stores and the focus being on the © JNBIT Vol.18, Iss.1 (2020)

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operational expediency in terms of use of automation and number of distribution centres to meet demand. They reported that full capacity of distributio n centres was efficiently utilised due the fluctuation in demand and that this lead to low investment in automation due to the diminishing financial attractiveness of such operations. This was supported by the study undertaken by Tanskanen, Yrjölä & Holmström (2002) they focused on approaches to achieving profitability in the internet grocery retailing identifying issues such as supply chain management and in particular to the need to concentrate on the sales per geographic area. In contrast research has also been undertaken to examine the performance of retail supermarkets and grocery stores in terms of the use of intellectual capital. A case study undertaken by Lueg, Nedergaard and Svendgaard (2013) examined the use of intellectual capital as a com petitive tool in a large Danish retail chain that consisted of two segments one retail food and groceries with the other being garden centres and hardware. The case study revealed the need to concentrate on different business strategies for the different segments with emphasis on customers, employees, technology and processes. As an extension to the relevance Watson, Stanworth, Healeas, Purdy and Stanworth (2005) explored the implications of intellectual capital in the approaches employed by organisations in the UK involved in franchising their retail shops. They pointed to there being a relationship between the head office structure, communication strategy, and a willingness to accept franchisee recommendations for innovative changes.

Method Source of Dat a The financial data used in this study was derived from the annual financial reports of Woolworths Group Limited and Coles Group Limited, both are publicly listed companies and the data was publicly available on the internet. The annual financial reports are for the four financial years from 2016 to 2019. The demographic data concerning the retail side of the two companies being examined are presented in Table 1.

Table 1: Demographic Data of Retail Stores Coles Supermarkets Department Stores Home Improvement Office Supplies Liquor Supplies

Coles (807 stores); Coles Express and Coles Online Kmart and Target Bunnings Warehouse Officeworks First Choice; Liquorland and Vintage Cellars

Woolworths Woolworths (995 stores); Woolworths Online Big W

BWS; Dan Murphy’s; Langtons and Cellarmaster

In this study two models to assess the efficient performance are used. The first is the data envelopment analysis method (DEA) initially conceived by Farrell (1957) for single input/output analysis and extended by Charnes, Cooper and Rhodes (1978) to accommodate multiple input/output analysis. The second is the value added intellectual coefficient model (VAIC™) developed by Pulic (1998, 2000, 2004). DEA Model In simple terms the DEA approach uses linear programming methods to calculate the scores of the variables to construct an optimal scale of the level of efficiency (Norman & Stoker, 1991). Following on from the prior research identified in the literature review the variables for this study will be operationalise in the following manner: inputs - number of outlets; wages; and invento ry; and outputs – sales; and profits. A key consideration in the application of a DEA model is the selection of inputs and outputs (Coelli, 1996): the outputs should reflect the business goals, and the inputs should be the required © JNBIT Vol.18, Iss.1 (2020)

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resources for achieving those goals (Charnes Cooper, Lewin & Seiford, 1994). A constraint in any empirical study can be the availability of data and this particularly true in this situation because the data is primarily derived from the annual financial reports. However, there is evidence that supports the use of financial information to generate a multi-factor financial performance model that effectively acknowledges trade-offs amongst various financial measures (Zhu, 2000). It is this approach that is employed where the data for each company is derived from the consolidated financial statements of companies. The outputs used are sales revenues and earnings, which are frequently stated as strategic objectives. Note that the earnings metric used was neither net profit (bottom line), as this can be subject to tax differences and the effect of extraordinary items, nor was it operational profit, which can be subject to the effect of different types of amortization/depreciation policies as well as management strategies in relation to real estate ownership. Rather, the EBITDA was used to measure of operating performance because it is not subject to the limitations of net profit and operational profit. As for inputs, the chosen variables also are a reflection of the strategic, financial, and operational decisions that contribute to the outputs considered (sales revenues and EBITDA). Strategic decisions are often attributable to the investments made, such as the type and amount of fixed assets, the types of contracts and the mode of ownership, and all of these can be inherent in the fixed assets. Further, financial decisions tend to define the capital structure and subsequently, impact on the shareholder’s equity. Finally, operational decisions are intrinsically linked to both the cost of the service provided and the working capital requirements such as inventory and accounts receivable, hence these types of decisions encapsulate the current assets. In summary, the variables selected to operationalise inputs are the current assets, net fix ed assets, shareholders’ equity and cost of goods and services. The DEA model is reflected in the overview presented in Figure 1.

Figure 1: Overview of the DEA Model

Note: EBITDA stands for Earnings Before Interest Tax Depreciation Amortisation

The DEAP package available from the University of Queensland (Coelli, 2019) was the software used to compute the DEA model for this study. The efficiency index initially assumes a constant returns-to-scale (CRS), in which an increase in the inputs would be followed by the same proportional increase in the outputs for all subjects, ignoring the firms’ scale or size (Charnes et al., 1978) commonly referred to as the technical efficiency (TE). However, as this study is interested in the change in productivity over time the DEA – Malmquist index (Malmquist, 1953) is used. The use of DEA efficiency scores to calculate the Malmquist index is recognized as being appropriate for measuring produ ctivity changes over time (Berg et al., 1992). VAIC™ Model The next model is the VAIC™ which is aimed at measuring the total value creation efficiency of a company. Inherent in the model is the, Intellectual Capital Efficiency (ICE), and this highlights the efficiency of intellectual capital (IC ) within a company. The VAIC™ method is based on the premise that value creation is effectively derived from two sources: physical capital resources and intellectual © JNBIT Vol.18, Iss.1 (2020)

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capital resources. To this extent, the VAIC™ mo del is concerned with providing an indication of the total efficiency of va lue creation from all resources employed and embedded in this model is the notion that ICE is a reflection of the efficiency of value otherwise created by the IC employed. This in effect means that the better a company’s resources have been u sed it can be expected that the higher the company’s value creation efficiency level will be reflected in the outcome of the model (Pulic, 2000). The VAIC™ model is a reasonably simple process (Schneider, 1998) which utilises publicly available data (Andriessen, 2004), that in turn is derived from a standardised source (Williams, 2001), which has been externally audited (Firer and Williams, 2003), and as a consequence this makes the data and t he results far more objective and verifiable (Pulic,1998, 2000). The VAIC™ model is concerned with assigning values through data formulas to the establish in the first instance: value added (VA), structural capital (SC), intellectual capital (IC), and capital employed (CE), and this is then followed by determining t he efficiency indicators of: structural capital efficiency, human capital efficiency, intellectual capital efficiency, and capital employed efficiency, with the final outcome being the overall indicator of the VAIC index. The result is intended to provide a measure of the extent to which a company creates added value (Pulic,1998, 2000). The concept is basically explained by the following overview as presented in Figure 2.

Figure 2: Overview of the VAIC™ Model

Source: Laing, Dunn & Hughes-Lucas (2010)

The VAIC™ model construction involves the calculation of seven key elements and each stage has its pertinent variables expressed in the formulas as the model progresses to the ultimate identification of the Value Added Intellectual coefficient (VAIC™). The formulas and the sources of the pertinent variables required to operationalise them are presented in a step by step approach in Table 2.

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Table 2: VAIC™ Calculations by Steps Steps

Title Formula Value Added (VA) VA = OP + EC + D + A

Variables Operationalised OP = Operating Profit; EC = Employee Costs; D = Depreciation; A = Amortisation

2

Intellectual Capital (IC) IC = EC + SC

SC = Structural Capital HC = Human Capital SC = VA – HC

3

Human Capital Efficiency (HCE) HCE = VA / HC Structural Capital Efficiency (SCE) SCE = SC / VA Intellectual Capital Efficiency (ICE) ICE = HCE + SCE

1

4 5

6

Capital Employed Efficiency (CEE) CEE = VA / CE

7

Value Added Intellectual coefficient (VAIC™) VAIC = ICE + CEE

Source

Comment

Profit & Loss Statement; Notes to Financial Statements Profit & Loss Statement; Notes to Financial Statements Fiat measure (derived)

Employee costs are added back to operating profit because these costs are now treated as part of the intellectual capital (i.e. a form of asset);

Human Capital Efficiency is an indicator of the efficiency of human capital resources to add value.

Fiat measure (derived) Fiat measure (derived) CE = Book-value of Net Assets

Balance Sheet Statement; Notes to Financial Statements Fiat measure (derived)

“… ICE reflects the efficiency of value created by the IC (Intellectual capital) employed.” (Kujansivu & Lonnqvist, 2007, 276) Capital Employed Efficiency indicates how much of the added value is generated from the capital employed.

“VAIC™ measures how much new value has been created per invested monetary unit in each resource. A high coefficient indicates a higher value creation using the company’s resources, including its intellectual capita l.” (Pulic, 2004, 65) “ … VAIC™ does not present the monetary value of IC (Intellectual capital). Instead, it considers different efficiency factors related to IC, and in so doing, evaluates how effectively the organisation’s IC adds value to the organisation.” (Kujansivu & Lonnqvist, 2007, 276)

Source: Laing, Dunn & Hughes-Lucas (2010)

Results and Analysis DEA Results An output orientated Malmquist DEA analysis was performed to analyse the productivity change over time. However, as all the indices are relative to the previous year the results begin with year 2, which in this case is 2017. The DEA-Malmquist index summary of annual means is presented in Table 3.

Table 3: DEA-Malmquist Index Summary of Annual Means Year

effch

techch

pech

sech

tfpch

2017 2018 2019 Mean

1.000 1.000 1.000 1.000

0.931 0.879 0.801 0.869

1.000 1.000 1.000 1.000

1.000 1.000 1.000 1.000

0.931 0.879 0.801 0.869

The analysis for the annual means indicates that for the year 2017, the total factor productivity change (tfpch) was 0.931 or 93.1% from the previous year (2016). However, in the next year (2018) it declined to 0.879 or 87.9% and this decline continued with the year 2019. The © JNBIT Vol.18, Iss.1 (2020)

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interpretation on this point is that in the year 2017 the technological change (techch) contributed 93.1% to the growth in the output variables. In terms of the following two years even though they produced lower percentages the technological change ( techch) did contribute to the output variables but at a lower amount – 2018 only 87.9% and 2019 only 80.1%. Focusing on the company means, the results are presented in Table 4 below. With regards to the pure tech...