Naslov
Using Ordinal Logistic Regression to Predict Terms of Payment in Shipbuilding Industry

Autori
Rozga, Ante ; Luetić, Ante

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 58th World Statistical Congress / - Dublin : International Statistical Institute, 2010, 1-4

Skup
58th World Statistical Congress

Mjesto i datum
Dublin, Irska, 21.08.2011. - 26.08.2011

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
ordinal regression; delays in payment

Sažetak
We have analysed delay of payments towards suppliers in shipyard „Brodosplit“ in Split, Croatia. Delay of payments was ordered into four groups: 0-60 days, 61-90 days, 91-120 days and over 120 days. Several predictors were proposed at the beginning of the study. Those variables are different suppliers regarding the sort of materials they offer. The first one was “ML” which represents the most expensive material whose potential suppliers are the shipyard and the ship-owner. Variable “A” is composed of direct materials which need further analysis of the offers. Variable “N” is standard material which is specified by the list of materials whose term of delivery is within 60 days. Variable “ZM” is also standard material which is specified by special demand whose term of delivery is over 60 days. Variable “L” is the list of materials whose assortment and quantity is limited by catalogue at the annual level. Delay of payment was treated as dependent ordered variable and we used ordinal regression. Predictors were dichotomous whose modalities were “yes” or “no” which depends if supplier is on the list or not. Reference category for dependent variable was last one: terms of payments over 120 days as the longest one. Applying ordinal regression we have found two variables to be statistically significant: ZM (p = 0, 002) with the estimate of regression coefficient of -3.524 and L (p = 0, 075) with the estimate of regression coefficient of -1.051. All statistics in ordinal regression were sufficient and significant and this model could be regarded as good for prediction. References: 1. Hosmer, David W. and Lemeshow, Stanley (1989). Applied Logistic Regression. John Wiley & Sons, New York. 2. Bender, R. & Benner, A. (2000). Calculating Ordinal Regression Models in SAS and S-Plus. Biom. Journal 42, 677-699. 3. Chu, W., & Ghahramani, Z. (2005). Gaussian Processes for Ordinal Regression. Journal of Machine Learning Research, 6, 1019-1041. 4. Ditman, P., Slone, R. Mentzer, John T.(2010). Supply Chain Risk: It's Time to Measure it, Harvard Business Review, February 5, 2010. 5. Ha, Ho S., Krishnan R.(2008). A Hybrid Approach to supplier Selection for the Maintenance of a Competitive Supply Chain. Expert Systsems with Applications, 34., 2008. 6. Jacoby, D. (2009). Guide to Supply Chain Management. The Economist, London. 7. Lambert, D.M. ed. (2008). Supply Chain Management. 3rd Edition. Supply Chain Management Institute. Sarasota. 8. McCullagh, P. and Nelder (1989). Regression Models for Ordinal Data (with Discussion), Journal of the Royal Statistical Society – B 42(2), 109-142. 9. O’Connell, A. A. (2006). Logistic Regression Models for Ordinal Response Variables. Thousand Oaks: SAGE. 10. SPSS, Inc. (2002). Ordinal Regression Analysis. SPSS Advanced Models 10.0, Chicago, Il. 11. Žibret, B. (2007). Strateška nabava. MATE and ZŠEM. Zagreb, Croatia.

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija

POVEZANOST RADA