A new approach of forecasting intermittent demand for spare parts inventories in the process industries

Journal of the Operational Research Society

Volumn 58, Issue 1, 2007, Pages 52-61

  Hua, Z S ^a   Zhang, B ^a   Yang, J ^a   Tan, D S ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Autocorrelation; Bootstrapping; Forecasting; Intermittent demand; Regression analysis

Indexed keywords

FORECASTING; INDUSTRIAL MANAGEMENT; INFORMATION TECHNOLOGY; MACHINE COMPONENTS; PROCESS CONTROL; PROCESS ENGINEERING; REGRESSION ANALYSIS;

AUTOCORRELATION; BOOTSTRAPPING; ENTERPRISE MANAGEMENT; INTERMITTENT DEMAND;

INVENTORY CONTROL;

EID: 33845540695     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/palgrave.jors.2602119     Document Type: Article

References (41)

1. Al-Ghamdi AS (2002). Using logistic regression to estimate the influence of accident factors on accident severity. Accid Anal Prev 34: 729-741.
2. Bagchi U (1987). Modeling lead-time demand for lumpy demand and variable lead time. Naval Res Logist 34: 687-704.
3. Bier IJ (1984). Boeing commercial airplane group spares department: simulation of spare parts operations. ORSA/ TIMS Joint National Meeting, Detroit, MI.
4. Bookbinder JH and Lordahl AE (1989). Estimation of inventory reorder levels using the bootstrap statistical procedure. IIE Trans 21: 302-312.
5. Croston JD (1972). Forecasting and stock control for intermittent demands. Oper Res Quart 23(3): 289-303.
6. de Gooijer JG and Vidiella-i-Anguera A (2004). Forecasting threshold cointegrated systems. Int J Forecast 20: 237-253.
7. Dunsmuir WTM and Snyder RD (1989). Control of inventories with intermittent demand. Eur J Oper Res 40: 16-21.
8. Duy TA and Thoma MA (1998). Modeling and forecasting cointegrated variables: some practical experience. J Econ Bus 50: 291-307.
9. Efron B (1979). Bootstrap methods: another look at the jackknife. Am Stat 7: 1-26.
10. Gamero MJ, Garcia JM and Reyes AM (1998). Bootstrapping statistical functionals. Stat Probab Lett 39(3): 229-236.
11. Ghobbar AA and Friend CH (2003). Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model. Comput Oper Res 30: 2097-2114.
12. Herzel H and Große I (1995). Measuring correlations in symbol sequences. Physica A 216: 518-542.
13. Huiskonen J (2001). Maintenance spare parts logistics: special characteristics and strategic choices. Int J Prod Econ 71: 125-133.
14. James JL and Kim K E (1996). Restraint use by children involved in crashes in Hawaii' 1986-1991. Transp Res Rec 1560: 8-12.
15. Johnston FR and Boylan JE (1996). Forecasting for items with intermittent demand. J Oper Res Soc 47: 113-121.
16. Jovanis PP and Chang H (1986). Modeling the relationship of accidents to miles traveled. Transp Res Rec 1068: 42-51.
17. Kennedy WJ, Patterson JW and Fredendall LD (2002). An overview of recent literature on spare parts inventories, Int J Prod Econ 76: 201-215.
18. Kim K and Li L (1996). Modeling fault among bicyclists and drivers involved in collisions in Hawaii' 1986-1991. Transp Res Rec 1538: 75-80.
19. Lau HS and Wang MC (1987). Estimating the lead-time demand distribution when the daily demand is non-normal and autocorrelated. Eur J Oper Res 29: 60-69.
20. Mercier CR, Shelley MC, Rimkus J and Mercier JM (1997). Age and gender as predictors of injury severity in head-on highway vehicular collisions. Transp Res Rec 1581: 37-46.
21. Milton J and Mannering F (1997). Relationship among highway geometric, traffic-related elements, and motor-vehicle accident requencies. The 76th Annual Meeting of the Transportation Research Board, Washington, DC.
22. Mitchell CR, Rappold RA and Faulkner WB (1983). An analysis of Air Force EOQ data with an application to reorder point calculation. Manage Sci 29: 440-446.
23. Mosteller F and Tukey JW (1977). Data Analysis and Regression: A Second Course in Statistics, Addison-Wesley: Reading, MA.
24. Nassar SA, Saccomanno FF and Shortreed JH (1997). Integrated risk model (IRM) of Ontario. The 76th Annual Meeting of the Transportation Research Board, Washington, DC.
25. Petrovic D and Petrovic R (1992). SPARTA II: further development in an expert system for advising on stocks of spare parts. Int J Prod Econ 24: 291-300.
26. Rao AV (1973). A comment on: forecasting and stock control for intermittent demands. Oper Res Quart 24(4): 639-640.
27. Sani B and Kingsman BG (1997). Selecting the best periodic inventory control and demand forecasting methods for low demand items. J Opl Res Soc 48: 700-713.
28. Schultz CR (1987). Forecasting and inventory control for sporadic demand under periodic review. J Opl Res Soc 37: 303-308.
29. Segerstedt A (1994). Inventory control with variation in lead times, especially when demand is intermittent. Int J Prod Econ 35: 365-372.
30. Silver EA (1981). Operations research in inventory management: a review and critique. Oper Res 29: 628-645.
31. Snyder R (2002). Forecasting sales of slow and fast moving inventories. Eur J Opl Res 140: 684-699.
32. Tyworth JE and O'Neill L (1997). Robustness of the normal approximation of lead-time demand in a distribution setting. Naval Res Logist 44: 165-186.
33. Van Ness PD and Stevenson WJ (1983). Reorder-point models with discrete probability distributions. Decision Sci 14: 363-369.
34. Wang M and Rao SS (1992). Estimating reorder points and other management science applications by bootstrap procedure. Eur J Opl Res 56: 332-342.
35. Ward JB (1978). Determining reorder points when demand is lumpy. Manage Sci 24: 623-632.
36. Watson RB (1987). The effects of demand-forecast fluctuations on customer service and inventory cost when demand is lumpy. J Opl Res Soc 38: 75-82.
37. Willemain TR, Ratti EWL and Smart CN (1994a). Forecasting intermittent demand using a cox process model. INFORMS Meetings, Boston, USA, pp 1-14.
38. Willemain TR, Smart CN and Schwarz HF (2004). A new approach to forecasting intermittent demand for service parts inventories. Int J Forecast 20: 375-387.
39. Willemain TR, Smart CN, Shockor JH and DeSautels PA (1994b). Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method. Int J Forecast 10(4): 529-538.
40. Williams TM (1982). Reorder levels for lumpy demand. J Opl Res Soc 33: 185-189.
41. Williams TM (1984). Stock control with sporadic and slow-moving demand. J Opl Res Soc 35: 939-948.