||Efficiency and Productivity Analyses of a Taiwan Bakery Enterprise–Application of DEA and Malmquist Productivity Index
||Doctoral Program, Department of Management Sciences
data envelopment analysis
total factor productivity
return on assets
||基於人類的需求，食品工業不僅已成為民生行業，而且反映了國家或地區的發展水平，並顯示了人們的生活質量。烘焙業作為食品業之一，對它的快速增長和快速發展具有重要意義。因此，了解生產效率和深入研究烘焙業效率管理對於確保烘焙業公司的可持續性具有重要意義。本文旨在運用DEA-Malmquist模型評估85°C公司2011年至2016年的效率和生產率變化。通過採用資料包絡分析法(data envelopment analysis)，發現規模效率(scale efficiency)高於純技術效率(pure technical efficiency)，這表明技術效率(technical efficiency)低是導致純技術效率降低的主要原因，這表明85°C公司的在研究期間，尤其是在2015年第三季度的第三階段營運模式之後，效率水平逐漸提高。同時，第三階段營運模式在技術上比第一階段營運模式和第二階段營運模式更加有效，並且在純技術上更為有效，這表明及時改變了作戰方式戰略對提高運營效率有重大影響。此外，本文並發現資產回報率(return on assets)與技術效率成正比關係，這意味著保持在產品前線的生產能力決定了85°C公司的財務績效。 本文同時指出，從2011年到2016年，85°C的總生產率變化的總體平均值略有增加，並且生產率變化很容易受到技術進步的影響。此外，本文還發現，北部自有商店在2011-2016年期間的技術進步和總要素生產率(total factor productivity)變動最差。
||Food industry has become not only a livelihood industry but also reflect the national or regional level of development and shows people’s quality of life. Bakery industry, as one of the food industry, is significant for its rapid growth and fast development. Thus, understanding the production efficiency and the in-depth research of the efficiency management of bakery industry are important to ensure the sustainability of the companies in the bakery industry. This thesis aims to evaluate the efficiency and productivity change of 85°C company from 2011 to 2016 by using the DEA-Malmquist model.
By adopting the Data Envelopment Analysis (DEA), it reveals that the low technical efficiency is the major reason for lower pure technical efficiency as the finding that the scale efficiency is higher than pure technical efficiency, which indicates that the 85°C company’s efficiency level has progressively improved during the period under study, particularly after the III-generation operations style period of 2015 Q3. Meanwhile, the III-generation operations style is more technically efficient and more pure-technically efficient than the I-generation and II-generation, shows that the timely change of operational strategy has a major impact on improving operational efficiency. Moreover, the findings of Return on Assets (ROA) was positively related to technical efficiency implies that a producer's ability to stay on the production frontier decides the financial performance of 85°C company.
This study also, dynamically, implies that the overall mean for total productivity change of 85°C increased slightly from 2011 to 2016, and the productivity change was easily affected by technical progress. Furthermore, the results also show that the north-district self-owned stores (which located in subtropical climate) have the worst technical progress and total factor productivity change during 2011-2016 period.
||TABLE OF CONTENTS
TABLE OF CONTENTS...IV
LIST OF FIGURES...VI
LIST OF TABLES...VII
Chapter 1 Introduction...1
1.2 Statement of the Problem...4
1.3 Purpose and Objectives...5
1.4 Research Questions...6
1.5 Structure and outline of this study...6
Chapter 2 Literature Review...8
2.1 DEA Method on the Efficiency of Food Industry...8
2.2 DEA-Malmquist Index Model on Prodcutivity Change of Food Industry...10
2.3 The Application of Other Indicators in Food Industry...12
Chapter 3 Methodology...16
3.1 Overview of Data Envelopment Analysis...16
3.2 Overview of Malmquist Total Factor Productivity...18
3.3 Overview of Kruskal–Wallis Test...20
Chapter 4 Results of Static Efficiency Analysis...21
4.1 Case Company (85°C) Profile...21
4.2 Operational overview of 85°C...25
4.3 Empirical results...31
Chapter 5 Results of Dynamic Productivity Change Analysis...41
5.1 Data Collection...41
5.2 Dynamic Productivity Change Results and Discussion...44
5.3 Management Decision Matrix...48
Chapter 6 Conclusions...51
6.1 Findings and Discussions...51
6.2 Implications of the Study...54
6.3 Limitations of this Study and Future Research...55
LIST OF FIGURES
Figure 1-1. Taiwan consumer demand and potential for food...2
Figure 1-2. Average price spent on bakery products in USD...3
Figure 1-3. Taiwan contribution rates of baked and steamed food industry...4
Figure 1-4. Thesis structure and outline...7
Figure 4-1. Company Structure of 85°C...22
Figure 4-2. Store development overview of 85°C...26
Figure 4-3. Details of store development in the U.S....27
Figure 4-4. Trends in mean efficiency scores....37
Figure 4-5. Relationship between return on assets (ROA) and technical efficiency between 2011 and 2016....40
Figure 5-1. Trend of productivity change and its component in 85°C from 2011 to 2016....45
Figure 5-2. Management decision matrix of 85°C during 2011–2016 periods....50
LIST OF TABLES
Table 2-1 Categorization of the literature devoted to the efficiency of the food industry...14
Table 2-1 Categorization of the literature devoted to the efficiency of the food industry. (continued)...15
Table 4-1 Personnel information of 85°C...23
Table 4-2 Milestones of 85°C...24
Table 4-3 85°C business proportion of main products l...25
Table 4-4 Numbers of stores and employees of 85°C...26
Table 4-5 Details of store development in China...27
Table 4-6 Main product (service) areas of 85°C...28
Table 4-7 Summary statistics of all variables in the data envelopment analysis (DEA) model...33
Table 4-8 Correlation coefficients among inputs and outputs....34
Table 4-9 Efficiency scores from the Banker et al. (BCC) DEA model...36
Table 4-10 Average efficiency measures for different stores size (2011–2016) and results of the Kruskal–Wallis test....37
Table 4-11 Summary of overall efficiency and returns to scale....38
Table 4-12 Summary of CRS, IRS, and DRA of different panels....39
Table 4-13 Correlation coefficients among ROA and technical efficiency...40
Table 5-1 Correlation analysis among variables....42
Table 5-2 Means and standard deviations for the data of 85°C...43
Table 5-3 Average total factor productivity change and its decomposition of 85°C, 2011-2016...45
Table 5-4 The summary of Malmquist productivity index of 85°C, 2011–2016....47
Table 5-5 Variation of Efficiencies of 22 self-owned stores, 2011–2016....47
Table 5-6 Average change in TFP and its different components, and the Kruskal–Wallis test...48
Adam Jabłoński (2019). Sustainable Business Models. Sustainability, ISBN 978-3-03897-560-1. https://doi.org/10.3390/books978-3-03897-561-8
Athanasoglou, P.P., Brissimis, S.N., & Delis, M.D. (2008). Bank-specific, industry specific and macroeconomic determinants of bank profitability. Journal of International Financial Markets, Institutions and Money, 18(2), 121–136.
Banker, R.D., Charnes, A., & Cooper, W.W. (1984). Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.
Barros, C.P. & Mascarenhas, M.J. (2005). Technical and allocative efficiency in a chain of small hotels. International Journal of Hospitality Management, 24(3), 415–436.
Business Monitor Online (BMO). Available online: https://bmo.bmiresearch.com/search/results?all_words_kw=taiwan&kw=1. (accessed on 4 July 2018).
Caves, D.W., Christensen, L R., & Diewert, W.E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica: Journal of the Econometric Society, 50(6), 1393–1414.
Chai, P., Zhang, Y., Zhou, M., Liu, S., & Kinfu, Y. (2019). Technical and scale efficiency of provincial health systems in China: a bootstrapping data envelopment analysis. BMJ Open, 9, e027539. doi:10.1136/bmjopen-2018-027539.
Charnes, A., & Cooper, W.W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3-4), 181–186.
Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
Chen, H.S., Tsai, B.K., Liou, G.B., & Hsieh, C.M. (2018). Efficiency assessment of inbound tourist service using data envelopment analysis. Sustainability, 10(6), 1866–1880.
Confederação Nacional da Indústria (CNI); Associação Brasileira das Indústrias da Alimentação (ABIA). Sustainability in the Food Industry: A Vision of the Future for Rio + 20 (in Portuguese). (Cadernos Setoriais Rio+20). 2012. Available online: http://arquivos.portaldaindustria.com.br/app/conteudo_18/2013/09/23/4970/20131002162456498394o.pdf (accessed on 16 November 2019).
Cooper, W.W., Seiford, L.M., & Tone, K. (2001). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software; Kluwer Academic Publishers: Boston, MA, USA.
Directorate-General of Budget, Accounting and Statistics (2018). Available online: https://www.dgbas.gov.tw/ct.asp?xItem=33338&ctNode=3099&mp=1. (accessed on 5 October 2018).
Doucouliagos, H., & Hone, P. (2001). The efficiency of the Australian dairy processing industry. Australian Journal of Agricultural and Resource Economics, 44(3), 423–438.
Dunn, O.J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3), 241–252.
Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.
FIRDI Food Industry Research and Development Institute. 2019 Food Industry. Available: https://www2.itis.org.tw/PubReport/PubReport_Detail.aspx?rpno=79053019&industry=&ctgy=&free (accessed on 21 August 2019)
Geylani, P.C., & Stefanou, S.E. (2011). Productivity growth patterns in US dairy products manufacturing plants. Applied Economics, 43(24), 3415–3432.
Giokas, D., Eriotis, N., & Dokas, I. (2015). Efficiency and productivity of the food and beverage listed firms in the pre-recession and recessionary periods in Greece. Applied Economics, 47(19), 1927–1941.
Golany, B. & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250.
Gonzalez-Hermosillo, B., Pazarbasioglu, C., & Billings, R. (1997). Determinants of banking system fragility: A case study of Mexico. Staff Papers, 44(3), 295–314.
González Rodríguez, M.R., Martín Samper, R.C., & Giuliani Carlos, A. (2015). Evaluating the efficiency progress with technology in a Spanish hotel chain. Revista de Administração de Empresas, 55(5), 551-562.
Gurpreet, K.A. (2014). Evaluating the efficiency of Indian banking industry using data envelope analysis. Journal of Economics, Commerce and Management, II(8), 1-13.
Holyk, S. (2016). Measuring technical efficiency and returns to scale in Finnish food processing industry. International Journal of Sciences: Basic and Applied Research, 27(3), 226–238.
Hu, J.L., Chiu, C.N., & Chu, H.T. (2019). Managerial efficiency in the food and beverage industry in Taiwan. Journal of Hospitality Financial Management, 27(1), article 5.
Hu, J.L., Shieh, H.S., Huang, C.H., & Chiu, C.N. (2009). Cost efficiency of international tourist hotels in Taiwan: A data envelopment analysis application. Asia Pacific Journal of Tourism Research, 14(4), 371–384.
Huang, S.W., Kuo, H.F., Hsieh, H.I., & Chen, T.H. (2016). Environmental efficiency evaluation of coastal tourism development in Taiwan. International Journal of Environmental Science and Development, 7(2), 145–150.
Huang, Y., Luo, S., Xu, G., & Zhou, G. (2018). Quantitative analysis and evaluation of enterprise group financial company efficiency in China. Sustainability, 10(9), 3210–3227.
Jabir, A., Singh, S.P., & Ekanem, E. (2009). Efficiency and productivity changes in the Indian food processing industry: Determinants and policy implications. The International Food and Agribusiness Management, 12(1), 43–66.
Kapelko, M. (2019). Measuring productivity change accounting for adjustment costs: Evidence from the food industry in the European Union. Annual of Operations Research, 278, 215–234.
Kapelko, M., Lansink, A.O., & Stefanou, S.E. (2015). Effect of food regulation on the Spanish food processing Industry: A dynamic productivity analysis. PLoS ONE, 10(6), e0128217.
Kapelko, M., Lansink, A.O., & Stefanou, S.E. (2016). Investment age and dynamic productivity growth in the Spanish food processing industry. American Journal of Agricultural Economics, 98(3), 946–961.
Kapelko, M., Lansink, A.O., & Stefanou, S.E. (2017a). The impact of 2008 financial crisis on dynamic productivity growth of the Spanish food manufacturing industry. Agricultural Economics, 48(5), 561–571.
Kapelko, M., Lansink, A.O., & Stefanou, S.E. (2017b). Assessing the impact of changing economic environment on productivity growth: The case of the Spanish dairy processing industry. Journal of Food Products Marketing, 23(4), 384–397.
Kaur, N., & Kaur, K. (2016). Efficiency, productivity and profitability changes in the Indian food processing industry: A firm level analysis. Pacific Business Review International, 1(1), 264–272.
Kumar, M., & Basu, P. (2008). Perspectives of productivity growth in Indian food industry: A data envelopment analysis. International Journal of Productivity and Performance Management, 57(7), 503–522.
Lima, L.P., Ribeiro, G.B.D., Silva, C.A B., & Perez, R. (2018). An analysis of the Brazilian dairy industry efficiency level. International Food Research Journal, 25(6), 2478–2485.
Lo, F.Y., Chen, C.F., & Lin, J.T. (2001). A DEA study to evaluate the relative efficiency and investigate the district reorganization of the Taiwan power company. IEEE Transactions on Power Systems, 16(1), 170–178.
Machmud, A., Ahman, E., Dirgantrari, P.D., Waspada, I., & Nandiyanto, A.B.D. (2019). Data development analysis: The efficiency study of food industry in Indonesia. Journal of Engineering and Technology, 14(1), 479–488.
Madau, F.A., Furesi, R., & Pulina, P. (2017). Technical efficiency and total factor productivity changes in European dairy farm sectors. Agricultural and Food Economics, 5(1), 1–14.
Mathur, R., & RAJU RAMNATH, S. (2018). Efficiency in food grains production in India using DEA and SFA. Central European Review of Economics and Management, 2(1), 79–101.
Mordor Intelligence (2019). Bakery products market-growth, trend and forecast (2019-2024). Available online: https://www.mordorintelligence.com/industry-reports/bakery-products-market. (accessed on August 18, 2019).
Muhammad Afzal, & Maryam Ayaz (2013). Efficiency of food sector of Pakistan-A DEA analysis. Asian Journal of Empirical Research, 3(10), 1310–1330.
Munshi Naser Ibne Afzal, Roger Lawrey, Mir Shatil Anaholy, Jhalak Gope (2018). A comparative analysis of the Efficiency and productivity of selected food processing industries in Malaysia. Malaysian Journal of Sustainable Agriculture, 2(1), 19–28.
Náglová, Z., & Šimpachová Pechrová, M. (2019). Subsidies and technical efficiency of Czech food processing industry. Agricultural Economics – Czech, 65(4), 151–159.
Ohlan, R. (2013). Efficiency and total factor productivity growth in Indian dairy sector. Quarterly Journal of International Agriculture, 52(1), 51–77.
Pongpanich R., Peng K.C., & Wongchai A. (2018). The performance measurement and productivity change of agro and food industry in the stock exchange of Thailand. Agricultural Economics – Czech, 64, 89–99.
Popović, R., & Panić, D. (2018). Technical efficiency of Serbian dairy processing industry. Economics of Agriculture, 65(2), 569–581.
Qiang, FU, & Fang, JI. (2017). Total factor productivity of food manufacturing industry in China: A DEA-Malmquist index measurement. Revista de la Facultad de Ingenieria, 32(4), 1–8.
Rezitis, A.N., & Kalantz, M.A. (2016). Investigating technical efficiency and its determinants by data envelopment analysis: An application in the Greek food and beverages manufacturing industry. Agribusiness, 32(2), 254–271.
Rodmanee, S., & Huang, W. (2103). Efficiency evaluation of food and beverage companies in Thailand: An application of relational two-stage data envelopment analysis. International Journal of Social Science and Humanity, 3(3), 202–205.
Rudinskaya T. (2017). Heterogeneity and efficiency of food processing companies in the Czech Republic. Agricultural Economics – Czech, 63, 411–420.
Setiawan, M. (2019). Dynamic productivity growth and its determinants in the Indonesian food and beverages industry. International Review of Applied Economics, 33(6), 774-788.
Setiawan, M., & Oude Lansink, A.G.J.M. (2018). Dynamic technical inefficiency and industrial concentration in the Indonesian food and beverages industry. British Food Journal, 120(1), 108–119.
Su, C.S. (2013). An importance-performance analysis of dining attributes: A comparison of individual and packaged tourists in Taiwan. Asia Pacific Journal of Tourism Research, 18(6), 573–597.
Tatli, H., & Bayrak, R. (2017). Total factor productivity analysis in food sector. International Journal of Advances in Management and Economics, 6(4), 25–34.
Vlontzos, G., & Theodoridis, A. (2013). Efficiency and productivity change in the Greek dairy industry. Agricultural Economics Review, 14(2), 14–28.
United States Department of Agriculture Foreign Agricultural Service (USDA FAS) GAIN reports (2019). Taiwan: Food Processing Ingredients. https://gain.fas.usda.gov/Recent%20GAIN%20Publications/Food%20Processing%20Ingredients_Taipei%20ATO_Taiwan_3-28-2019.pdf (accessed on August 18, 2019).
Yang, X.L., Zhang, Y.J., & Wang, L. (2012). An Empirical analysis of total factor productivity of the food processing industry in Jilin Province. Journal of Agrotechnical Economics, 12, 61–67.
Yin, P., Tsai, H., & Wu, J. (2015). A hotel life cycle model based on bootstrap DEA efficiency: The case of international tourist hotels in Taipei. International Journal of Contemporary Hospitality Management, 27(5), 918–937.
Yodfiatfinda, Mad Nasir, S., Zainalabidin, M., Md Ariff, H., Zulkornain, Y., & Alias, R. (2012). The empirical evaluation of productivity growth and efficiency of LSEs in the Malaysian food processing industry. International Food Research Journal, 19(1), 287–295.
Zhang, Z. & Wang, K. (2014). Scale competitiveness of food enterprises and TFP growth. Science Technology and Industry, 14, 83–88.