Performance analysis of log yards using data envelopment analysis

References

• Anand G, Kodali R. 2008. Benchmarking the benchmarking models. Benchmarking: An International Journal. 15(3):257–291.
   Google Scholar
• Baker P, Canessa M. 2009. Warehouse design: a structured approach. Eur J Oper Res. 193(2):425–436.
   Web of Science ®Google Scholar
• Banker RD. 1993. Maximum likelihood, consistency and data envelopment analysis: a statistical foundation. Manage Sci. 39(10):1265–1273.
   Web of Science ®Google Scholar
• Banker RD, Charnes A, Cooper WW. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci. 30(9):1078–1092.
   Web of Science ®Google Scholar
• Barrento M, de Castro Neto M. 2017. European benchmarking in healthcare: analysis by country with highlight on Portugal. In: 2017 12th Iberian Conference on Information Systems and Technologies, Lisbon, Portugal, (CISTI); IEEE. p. 1–4. .
   Google Scholar
• Beaudoin D, LeBel L, Soussi MA. 2012. Discrete event simulation to improve log yard operations. INFOR. 50(4):175–185.
   Web of Science ®Google Scholar
• Bhagavath V. 2006. Technical efficiency measurement by data envelopment analysis: an application in transportation. Alliance J Bus Res. 2(1):60–72.
   Google Scholar
• Bonaplata J 1995. Evaluation of productivity and quality performance in a newspaper preprint insertion process [master’s thesis]. Blacksburg, VA: Virginia Tech.
   Google Scholar
• Charnes A, Cooper WW, Rhodes E. 1978. Measuring the efficiency of decision making units. Eur J Oper Res. 2(6):429–444.
   Web of Science ®Google Scholar
• Coelli T J, Prasada Rao DS, O’Donnell CJ, Battese GE. 2005. An introduction to efficiency and productivity analysis. New York (NY): Springer Science & Business Media.
   Google Scholar
• Cook WD, Seiford LM, Zhu J. 2004. Models for performance benchmarking: measuring the effect of e-business activities on banking performance. Omega. 32(4):313–322.
   Web of Science ®Google Scholar
• Cooper WW, Seiford LM, Zhu J. 2004. Data envelopment analysis. Handbook on data envelopment analysis. Int Ser Oper Res Manag Sci. 71:1–39.
   Google Scholar
• De Koster MBM, Balk BM. 2008. Benchmarking and monitoring international warehouse operations in Europe. Prod Oper Manag. 17(2):175–183.
   Web of Science ®Google Scholar
• Diaz-Balteiro L, Herruzo AC, Martinez M, Jacinto GP. 2006. An analysis of productive efficiency and innovation activity using DEA: an application to Spain’s wood-based industry. For Policy Econ. 8:762–773.
   Web of Science ®Google Scholar
• Dyson RG, Allen R, Camanho AS, Podinovski VV, Sarrico CS, Shale EA. 2001. Pitfalls and protocols in DEA. Eur J Oper Res. 132:245–259.
   Web of Science ®Google Scholar
• Emrouznejad A, Yang GL. 2018. A survey and analysis of the first 40 years of scholarly literature in dea: 1978–2016. Socio-economic Planning Sciences. 61:4–8.
   Web of Science ®Google Scholar
• Flynn BB, Huo B, Zhao X. 2010. The impact of supply chain integration on performance: a contingency and configuration approach. J Oper Manag. 28(1):58–71.
   Web of Science ®Google Scholar
• Frazelle E 2002. Supply chain strategy: the logistics of supply chain management. New York, NY: McGrraw Hill.
   Google Scholar
• Fried HO, Schmidt SS, Yaisawarng S. 1999. Incorporating the operating environment into a nonparametric measure of technical efficiency. J Productiv Anal. 12(3):249–267.
   Web of Science ®Google Scholar
• Gu J, Goetschalckx M, McGinnis LF. 2010. Research on warehouse design and performance evaluation: a comprehensive review. Eur J Oper Res. 203(3):539–549.
   Web of Science ®Google Scholar
• Haas DA, Murphy FH. 2003. Compensating for non-homogeneity in decision-making units in data envelopment analysis. Eur J Oper Res. 144(3):530–544.
   Web of Science ®Google Scholar
• Hackman ST, Frazelle EH, Griffin PM, Griffin SO, Vlasta DA. 2001. Benchmarking warehousing and distribution operations: an input-output approach. J Productiv Anal. 16(1):79–100.
   Web of Science ®Google Scholar
• Hailu A, Veeman TS. 2003. Comparative analysis of efficiency and productivity growth in Canadian regional boreal logging industries. Can J For Res. 33(9):1653–1660.
   Web of Science ®Google Scholar
• Hamdan A, Rogers JKJ. 2008. Evaluating the efficiency of 3PL logistics operations. Int J Prod Econ. 113(1):235–244.
   Web of Science ®Google Scholar
• Hampton CM 1981. Dry land log handling and sorting: planning, construction, and operation of log yards. San Francisco, CA: Miller Freeman Publications.
   Google Scholar
• Heng T, Thangavelu S. 2016. Productivity in Singapore’s retail and food services sectors: contemporary issues. Singapore: World Scientific Publishing.
   Google Scholar
• Johnson A, McGinnis L. 2011. Performance measurement in the warehousing industry. IIE Trans. 43(3):220–230.
   Web of Science ®Google Scholar
• Lähtinen K, Haara A, Leskinen P, Toppinen A. 2008. Assessing the relative importance of tangible and intangible resources: empirical results from the forest industry. For Sci. 54(6):607–616.
   Google Scholar
• LeBel LG 1996. Performance and efficiency evaluation of logging contractors using data envelopment analysis [Ph.D. Dissertation]. Blacksburg, VA: Virginia Tech.
   Google Scholar
• LeBel LG, Stuart WB. 1998. Technical efficiency evaluation of logging contractors using a nonparametric model. J For Eng. 9(2):15–24.
   Google Scholar
• Malmborg C J. 1998. Analysis of storage assignment policies in less than unit load warehousing systems. International Journal of Production Research. 36(12):3459–75.
   Web of Science ®Google Scholar
• Marinescu MV, Sowlati T, Maness TC. 2005. The development of a timber allocation model using data envelopment analysis. Can J For Res. 35(10):2304–2315.
   Google Scholar
• Martić M, Novaković M, Baggia A. 2009. Data envelopment analysis – basic models and their utilization. Organizacija. 42(2):37–43.
   Google Scholar
• McLinton SS, Loh MY, Dollard MF, Michelle MR, Tuckey M, Idris A, Morton S. 2018. Benchmarking working conditions for health and safety in the frontline healthcare industry: perspectives from Australia and Malaysia. J Adv Nurs. 74(8):1851–1862.
   Web of Science ®Google Scholar
• Nyrud AQ, Bergseng ER. 2002. Production efficiency and size in Norwegian sawmilling. Scand J For Res. 17(6):566–575.
   Google Scholar
• Obi OF, Visser R. 2017a. Operational efficiency analysis of New Zealand timber harvesting contractors using data envelopment analysis. Int J For Eng. 28(2):85–93.
   Web of Science ®Google Scholar
• Obi OF, Visser R. 2017b. Influence of the operating environment on the technical efficiency of forest harvesting operations. Int J For Eng. 28(3):1–8.
   Web of Science ®Google Scholar
• Penfield PC, Germain R, Smith W. 2014. Assessing the supply chain efficiency of hardwood sawmills in New York state through case study analysis and data envelopment analysis modeling. For Prod J. 64(3–4):90–96.
   Web of Science ®Google Scholar
• Salehirad N, Sowlati T. 2005. Performance analysis of primary wood producers in British Columbia using data envelopment analysis. Can J For Res. 35(2):285–294.
   Web of Science ®Google Scholar
• Salvador F, Forza C, Rungtusanatham M, Choi, TY. 2001. Supply chain interactions and time‐related performances. International Journal of Operations and Production Management. 21(4):461–75.
   Web of Science ®Google Scholar
• Saygin D, Worrell E, Patel MK, Gielen DJ. 2011. Benchmarking the energy use of energy-intensive industries in industrialized and in developing countries. Energy. 36(11):6661–6673.
   Web of Science ®Google Scholar
• Schefczyk M. 1993. Industrial benchmarking: a case study of performance analysis techniques. Int J Prod Econ. 32(1):1–11.
   Web of Science ®Google Scholar
• Shahmohammadi F, Charmi M. 2004. Using DEA to measure performance of science parks: case of Iran. 4th International Symposium of DEA; 2004 Sept 5–6, Aston, UK, Data Envelopment Analysis and Performance Management. p. 5–6.
   Google Scholar
• Shiba M. 1997. Measuring the efficiency of managerial and technical performances in forestry activities by means of data envelopment analysis (DEA). J For Eng. 8(1):7–19.
   Google Scholar
• Sinclair A, Wellburn G. 1984. A handbook for designing, building and operating a log sortyard. Vancouver, BC: Forest Engineering Research Institute of Canada.
   Google Scholar
• Sink DS, Tuttle TC. 1989. Planning and measurement in your organization of the future. Norcross, GA: Industrial Engineering and Management Press.
   Google Scholar
• Staudt FH, Alpan G, Mascolo MD, Carlos M. 2015. warehouse performance measurement : a literature review. Int J Prod Res. 7543:1–21.
   Google Scholar
• Sufian F. 2011. Benchmarking the efficiency of the Korean banking sector: a DEA approach. Benchmarking. 18(1):107–127.
   Google Scholar
• Trzcianowska M, Beaudoin D, LeBel L. 2016. Wood yard design methodology for improved supply chain performance. From Theory to Practice: Challenges for Forest Engineering. 49th Symposium on Forest Mechanization; 2016 Sept 4–7, Warsaw, Poland, Proceedings and Abstracts. p. 129–134.
   Google Scholar
• Vachon-Robichaud S, Beaudoin D, Luc L 2014. Log Yard design using discrete-event simulation: first step towards a formalized approach. OSIM 2014, 10ème Conf ́erence Francophone de Modélisation, Nancy, France, Optimisation et Simulation, no. 1981. p 11.
   Google Scholar
• Vahid S, Sowlati T. 2007. Efficiency analysis of the Canadian wood-product manufacturing subsectors: a DEA approach. For Prod J. 57(1–2):71–77.
   Web of Science ®Google Scholar
• Van den Berg JP, Zijm WHM. 1999. Models for warehouse management: classification and examples. International Journal of Production Economics. 59(1):519–28.
   Web of Science ®Google Scholar
• Vitasek KL, Manrodt KB. 2005. Finding best practices in your own backyard. Supply Chain Manag Rev. 9(1):54–58.
   Google Scholar
• Yasin MM. 2002. The theory and practice of benchmarking: then and now. Benchmarking. 9(3):217–243.
   Google Scholar