×

Supply chain forecasting: theory, practice, their gap and the future. (English) Zbl 1346.90181

Summary: Supply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products). This paper provides a comprehensive review of the literature and aims at bridging the gap between theory and practice in the existing knowledge base in SCF. We highlight the most promising approaches and suggest their integration in forecasting support systems. We discuss the current challenges both from a research and practitioner perspective and provide a research and application agenda for further work in this area. Finally, we make a contribution in the methodology underlying the preparation of review articles by means of involving the forecasting community in the process of deciding both the content and structure of this paper.

MSC:

90B06 Transportation, logistics and supply chain management
91B84 Economic time series analysis
62M20 Inference from stochastic processes and prediction
90-02 Research exposition (monographs, survey articles) pertaining to operations research and mathematical programming

Software:

Excel
PDF BibTeX XML Cite
Full Text: DOI Link

References:

[1] Ali, M. M., Centralised demand information sharing in supply chains, unpublished phd thesis, (2008), Buckinghamshire New University, Brunel University UK
[2] Ali, M.; Boylan, J. E., The value of forecast information sharing in the supply chain, Foresight, 18, 14-18, (2010)
[3] Ali, M. M.; Boylan, J. E., Feasibility principles for downstream demand inference in supply chains, Journal of the Operational Research Society, 62, 474-482, (2011)
[4] Ali, M. M.; Boylan, J. E., On the effect of non-optimal forecasting methods on supply chain downstream demand, IMA Journal of Management Mathematics, 23, 81-98, (2012) · Zbl 1248.90016
[5] Ali, M. M.; Boylan, J. E.; Syntetos, A. A., Forecast errors and inventory performance under forecast information sharing, International Journal of Forecasting, 28, 830-841, (2012)
[6] Alwan, L. C.; Liu, J. J.; Yao, D.-Q., Stochastic characterization of upstream demand processes in a supply chain, IIE Transactions, 35, 207-219, (2003)
[7] Amemiya, T.; Wu, R. Y., The effect of aggregation on prediction in the autoregressive model, Journal of the American Statistical Association, 67, 628-632, (1972) · Zbl 0258.62054
[8] Anderson, O. D., On a lemma associated with box, Jenkins and Granger, Journal of Econometrics, 3, 151-156, (1975) · Zbl 0303.62067
[9] Athanasopoulos, G.; Ahmed, R. A.; Hyndman, R. J., Hierarchical forecasts for Australian domestic tourism, International Journal of Forecasting, 25, 146-166, (2009)
[10] Babai, M. Z.; Ali, M. M.; Nikolopoulos, K., Impact of temporal aggregation on stock control performance of intermittent demand estimators: empirical analysis, OMEGA: The International Journal of Management Science, 40, 713-721, (2012)
[11] Barnea, A.; Lakonishok, J., An analysis of the usefulness of disaggregated accounting data for forecasts of corporate performance, Decision Sciences, 11, 17-26, (1980)
[12] Box, G. E.P.; Jenkins, G. M., Time series analysis, (1970), Holden-Day San Francisco · Zbl 0249.62009
[13] Boylan, J. E., Choosing levels of aggregation for supply chain forecasts, Foresight, 18, 9-13, (2010)
[14] Boylan, J. E.; Chen, H.; Mohammadipour, M.; Syntetos, A. A., Formation of seasonal groups and application of seasonal indices, Journal of the Operational Research Society, 65, 227-241, (2014)
[15] Boylan, J. E.; Syntetos, A. A., Accuracy and accuracy-implication metrics for intermittent demand, Foresight, 4, 39-42, (2006)
[16] Boylan, J. E.; Syntetos, A. A., Spare parts management: A review of forecasting research and extensions, IMA Journal of Management Mathematics, 21, 227-237, (2010)
[17] Boylan, J. E.; Syntetos, A. A., Supply chain forecasting: the customer dimension, (Paper presented at the 27th European Conference on Operational Research (EURO 2015), July 12-15, Glasgow, UK, (2015))
[18] Boylan, J. E.; Syntetos, A. A.; Karakostas, G. C., Classification for forecasting and stock control: a case study, Journal of the Operational Research Society, 59, 473-481, (2008) · Zbl 1153.90488
[19] Brännäs, K.; Hellstrom, J.; Nordstrom, J., A new approach to modelling and forecasting monthly guest nights in hotels, International Journal of Forecasting, 18, 19-30, (2002)
[20] Caniato, F.; Kalchschmidt, M.; Ronchi, S.; Verganti, R.; Zotteri, G., Clustering customers to forecast demand, Production Planning and Control, 16, 32-43, (2005)
[21] Carstensen, J.; Telford, R. J.; Birks, H. J.H., Diatom flickering prior to regime shift, Nature, 498, E11-E12, (2013)
[22] Chen, H.; Boylan, J. E., Use of individual and group seasonal indexes in subaggregate demand forecasting, Journal of the Operational Research Society, 58, 1660-1671, (2007) · Zbl 1141.91583
[23] Chen, H.; Boylan, J. E., Empirical evidence on individual, group and shrinkage seasonal indexes, International Journal of Forecasting, 24, 525-534, (2008)
[24] Cheng, T. C.E.; Wu, Y. N., The impact of information sharing in a two-level supply chain with multiple retailers, Journal of the Operational Research Society, 56, 1159-1165, (2005) · Zbl 1081.90004
[25] Chopra, S.; Meindl, P., Supply chain management: strategy, planning and operation, (2012), Pearson New Jersey
[26] Croston, J. D., Forecasting and stock control for intermittent demands, Operational Research Quarterly, 23, 289-303, (1972) · Zbl 0238.90021
[27] Dalhart, G., Class seasonality - a new approach, (American production and inventory control society conference proceedings, (1974))
[28] Dangerfield, B. J.; Morris, J. S., Top-down or bottom-up: aggregate versus disaggregate extrapolations, International Journal of Forecasting, 8, 233-241, (1992)
[29] Dawson, P. (2013). Managing Director of DBO Services (http://www.dboservises.com). Private communications to the corresponding author.
[30] de Almeida, M. M.K.; Marins, F. A.S.; Salgado, A. M.P.; Santos, F. C.A.; da Silva, S. L., Mitigation of the bullwhip effect considering trust and collaboration in supply chain management: a literature review, The International Journal of Advanced Manufacturing Technology, 77, 495-513, (2015)
[31] DeHoratius, N.; Raman, A., Inventory record inaccuracy: an empirical analysis, Management Science, 54, 627-641, (2008)
[32] Dekker, M.; van Donselaar, K.; Ouwehand, P., How to use aggregation and combined forecasting to improve seasonal demand forecasts, International Journal of Production Economics, 90, 151-167, (2004)
[33] Disney, S.; Holmstrom, J.; Kaipia, R.; Towill, D. R., Implementation of a VMI production and inventory control system, (Paper presented at the 6th International Symposium of Logistics, July 8-10, (2001), Saltsburg, Austria)
[34] Fildes, R.; Goodwin, P., Against your better judgment? how organizations can improve their use of management judgment in forecasting, Interfaces, 37, 570-576, (2007)
[35] Fildes, R.; Goodwin, P., Forecasting support systems: what we know, what we need to know, International Journal of Forecasting, 29, 290-294, (2013)
[36] Fildes, R.; Goodwin, P.; Lawrence, M.; Nikolopoulos, K., Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning, International Journal of Forecasting, 25, 3-23, (2009)
[37] Fildes, R.; Nikolopoulos, K.; Crone, S.; Syntetos, A. A., Forecasting and operational research: a review, Journal of the Operational Research Society, 59, 1150-1172, (2008) · Zbl 1153.90009
[38] Fisher, M.; Hammond, J. H.; Obermeyer, W.; Raman, A., Making supply meet demand in an uncertain world, Harvard Business Review, 72, 83-92, (1994)
[39] Fisher, M.; Rajaram, K., Accurate retail testing of fashion merchandise: methodology and application, Marketing Science, 19, 266-278, (2000)
[40] Fliedner, G., An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation, Computers and Operations Research, 26, 1133-1149, (1999) · Zbl 0940.90004
[41] Fliedner, E. B.; Lawrence, B., Forecasting system parent group formation: an empirical application of cluster analysis, Journal of Operations Management, 12, 119-130, (1995)
[42] Franses, P. H.; Legerstee, R., Properties of expert adjustments on model-based SKU-level forecasts, International Journal of Forecasting, 25, 35-47, (2009)
[43] Franses, P. H.; Legerstee, R., Do experts’ adjustments on model-based SKU-level forecasts improve forecast quality?, Journal of Forecasting, 29, 331-340, (2010) · Zbl 1204.91090
[44] Franses, P. H.; Legerstee, R., Combining SKU-level sales forecasts from models and experts, Expert Systems with Applications, 38, 2365-2370, (2011)
[45] Franses, P. H.; Legerstee, R., Experts’ adjustment to model-based SKU-level forecasts: does the forecast horizon matter?, Journal of the Operational Research Society, 62, 537-543, (2011)
[46] Franses, P. H.; Legerstee, R., Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?, International Journal of Forecasting, 29, 80-87, (2013)
[47] Gardner, E. S., Exponential smoothing: the state of the art - part II, International Journal of Forecasting, 22, 637-666, (2006)
[48] Gardner, E. S., Forecasting for operations, (Keynote paper presented at the 31st international symposium on forecasting, June 27-29, Prague, Czech Republic, (2011))
[49] Ghobbar, A. A.; Friend, C. H., Sources of intermittent demand for aircraft spare parts within airline operations, Journal of Air Transport Management, 8, 221-231, (2002)
[50] Gilbert, K., An ARIMA supply chain model, Management Science, 51, 305-310, (2005) · Zbl 1232.90052
[51] Goodwin, P.; Meeran, S.; Dyussekeneva, K., The challenges of pre-launch forecasting of adoption time series for new durable products, International Journal of Forecasting, 30, 1082-1097, (2014)
[52] Gordon, T.; Dangerfield, B.; Morris, J., Top-down or bottom-up: which is the best approach to forecasting?, The Journal of Business Forecasting, 16, 13-16, (1997)
[53] Granger, C. W.J.; Morris, M. J., Time series modeling and interpretation, Journal of the Royal Statistical Society, 139, 246-257, (1976), Series A
[54] Grimson, J. A.; Pyke, D. F., Sales and operations planning: an exploratory study and framework, International Journal of Logistics Management, 18, 322-346, (2007)
[55] Gross, C. W.; Sohl, J. E., Disaggregation methods to expedite product line forecasting, Journal of Forecasting, 9, 233-254, (1990)
[56] Hammond, J. H., Barilla spa (A). harvard business school case 694-046, (1994), Harvard University
[57] Harvey, A. C., Time series models 2nd edition, (1993), Harvester Wheatsheaf New York
[58] Heinecke, G.; Syntetos, A. A.; Wang, W., Forecasting-based SKU classification, International Journal of Production Economics, 143, 455-462, (2013)
[59] Holweg, M.; Disney, S.; Holmstrom, J.; Smaros, J., Supply chain collaboration: making sense of the strategy continuum, European Management Journal, 23, 170-181, (2005)
[60] Hosoda, T.; Naim, M. M.; Disney, S. M.; Potter, A., Is there a benefit to sharing market sales information? linking theory and practice, Computers and Industrial Engineering, 54, 315-326, (2008)
[61] Humby, C., Hunt, T., & Phillips, T. (2008). Scoring points: How Tesco continues to win customer loyalty. Kogan Page, 2nd rev. ed.
[62] Hyndman, R. J.; Ahmed, R. A.; Athanasopoulos, G.; Shang, H. L., Optimal combination forecasts for hierarchical time series, Computational Statistics and Data Analysis, 55, 2579-2589, (2011) · Zbl 1464.62095
[63] Hyndman, R.; Athanasopoulos, G., Optimally reconciling forecasts in a hierarchy, Foresight, 35, 42-48, (2014)
[64] Hyndman, R. J.; Kostenko, A. V., Minimum sample size requirements for seasonal forecasting models, Foresight, 6, 12-15, (2007)
[65] Hyndman, R. J.; Lee, A. J.; Wang, E., Fast computation of reconciled forecasts for hierarchical and grouped time series, (2014), Department of Econometrics & Business Statistics, Monash University Australia, Working paper 17/14
[66] Hyndman, R. J., Lee, A. J., & Wang, E. (2014b). hts: Hierarchical and grouped time series. URL: Cran.r-project.org/package=hts.
[67] Januschowski, T.; Kolassa, S.; Lorenz, M.; Schwarz, C., Forecasting with in-memory technology, Foresight, 31, 14-20, (2013)
[68] Johnston, F. R., Lead time demand adjustment or when a model is not a model, Journal of the Operational Research Society, 51, 1107-1110, (2000)
[69] Johnston, F. R.; Boylan, J. E., Forecasting for items with intermittent demand, Journal of the Operational Research Society, 47, 113-121, (1996) · Zbl 0842.90031
[70] Johnston, F. R.; Harrison, P. J.; Marshall, A. S.; France, K. M., Modelling and the estimation of changing relationships, The Statistician, 35, 229-235, (1986)
[71] Kostenko, A. V.; Hyndman, R. J., A note on the categorization of demand patterns, Journal of the Operational Research Society, 57, 1256-1257, (2006)
[72] Kourentzes, N.; Petropoulos, F.; Trapero, J. R., Improving forecasting by estimating time series structural components across multiple frequencies, International Journal of Forecasting, 30, 291-302, (2014)
[73] Lee, H. L.; Padmanabhan, V.; Whang, S., Information distortion in a supply chain: the bullwhip effect, Management Science, 43, 546-558, (1997) · Zbl 0888.90047
[74] Lee, H. L.; So, K. C.; Tang, C. S., The value of information sharing in a two-level supply chain, Management Science, 46, 626-643, (2000) · Zbl 1231.90044
[75] Luiz, K. H.; Pedro, A. M.; Pedro, L. V.P., The effect of overlapping aggregation on time series models: an application to the unemployment rate in Brazil, Brazilian Review of Econometrics, 12, 223-241, (1992)
[76] Luna, I.; Ballini, R., Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting, International Journal of Forecasting, 27, 708-724, (2011)
[77] Lütkepohl, H., Forecasting contemporaneously aggregated vector ARMA processes, Journal of Business & Economic Statistics, 2, 201-214, (1984)
[78] Mathews, B.; Diamantopoulos, A., Managerial intervention in forecasting: an empirical investigation of forecast manipulation, International Journal of Research in Marketing, 3, 3-10, (1986)
[79] Mathews, B.; Diamantopoulos, A., Judgmental revision of sales forecasts: a longitudinal extension, Journal of Forecasting, 8, 129-140, (1989)
[80] Mathews, B.; Diamantopoulos, A., Judgmental revision of sales forecasts: effectiveness of forecast selection, Journal of Forecasting, 9, 407-415, (1990)
[81] Mathews, B.; Diamantopoulos, A., Judgmental revision of sales forecasts - the relative performance of judgementally revised versus non revised forecasts, Journal of Forecasting, 11, 569-576, (1992)
[82] Meddahi, N.; Renault, E., Temporal aggregation of volatility models, Journal of Econometrics, 119, 355-379, (2004) · Zbl 1282.91239
[83] McCullough, B. D.; Heiser, D. A., On the accuracy of statistical procedures in microsoft excel 2007, Computational Statistics and Data Analysis, 52, 4570-4578, (2008) · Zbl 1452.62017
[84] Mohammadipour, M.; Boylan, J. E., Forecast horizon aggregation in integer autoregressive moving average (INARMA) models, OMEGA: The International Journal of Management Science, 40, 703-712, (2012)
[85] Mohammadipour, M.; Boylan, J. E.; Syntetos, A. A., The application of product-group seasonal indexes to individual products, Foresight, 26, 18-24, (2012)
[86] Najafi, M.; Zanjirani Farahani, R., New forecasting insights on the bullwhip effect in a supply chain, IMA Journal of Management Mathematics, 25, 259-286, (2014) · Zbl 1291.62270
[87] Nikolopoulos, K.; Fildes, R., Adjusting supply chain forecasts for short-term temperature estimates: a case study in a brewing company, IMA Journal of Management Mathematics, 24, 79-88, (2013) · Zbl 1258.90009
[88] Nikolopoulos, K.; Petropoulos, F., Forecasting, foresight and strategic planning for black swans, (2015), Bangor University UK, BBSWP/15/0004, Bangor Business School Working Paper Series
[89] Nikolopoulos, K.; Syntetos, A. A.; Boylan, J.; Petropoulos, F.; Assimakopoulos, V., An aggregate-disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis, Journal of the Operational Research Society, 62, 544-554, (2011)
[90] Ouwehand, P.; van Donselaar, K. H.; de Kok, A. G., The impact of the forecasting horizon when forecasting with group seasonal indexes, (2005), Eindhoven University of Technology The Netherlands, Working paper 162
[91] Pegels, C., Exponential forecasting: some new variations, Management Science, 15, 311-315, (1969)
[92] Petropoulos, F.; Fildes, R.; Goodwin, P., Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?, European Journal of Operational Research, (2015), advance online publication · Zbl 1346.91038
[93] Petropoulos, F.; Kourentzes, N., Improving forecasting via multiple temporal aggregation, Foresight, 34, 12-17, (2014)
[94] Petropoulos, F.; Kourentzes, N., Forecast combinations for intermittent demand, Journal of the Operational Research Society, 66, 914-924, (2015)
[95] Porras, E. M.; Dekker, R., An inventory control system for spare parts at a refinery: an empirical comparison of different reorder point methods, European Journal of Operational Research, 184, 101-132, (2008) · Zbl 1278.90033
[96] Raghunathan, S., Information sharing in a supply chain: a note on its value when demand is non-stationary, Management Science, 47, 605-610, (2001) · Zbl 1232.90084
[97] Raghunathan, S., Impact of demand correlation on the value of and incentives for information sharing in a supply chain, European Journal of Operational Research, 146, 634-649, (2003) · Zbl 1037.90544
[98] Regattieri, A.; Gamberi, M.; Gamberini, R.; Manzini, R., Managing lumpy demand for aircraft spare parts, Journal of Air Transport Management, 11, 426-431, (2005)
[99] Rostami-Tabar, B.; Babai, M. Z.; Syntetos, A.; Ducq, Y., Demand forecasting by temporal aggregation, Naval Research Logistics, 60, 479-498, (2013)
[100] Rostami-Tabar, B.; Babai, M. Z.; Syntetos, A.; Ducq, Y., A note on the forecast performance of temporal aggregation, Naval Research Logistics, 61, 489-500, (2014)
[101] Rostami-Tabar, B.; Babai, M. Z.; Ducq, Y.; Syntetos, A., Non-stationary demand forecasting by cross-sectional aggregation, International Journal of Production Economics, 170, 297-309, (2016)
[102] Sbrana, G.; Silvestrini, A., Forecasting aggregate demand: analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework, International Journal of Production Economics, 146, 185-198, (2013)
[103] Shlifer, E.; Wolff, R. W., Aggregation and proration in forecasting, Management Science, 25, 594-603, (1979) · Zbl 0428.62062
[104] Silvestrini, A.; Veredas, D., Temporal aggregation of univariate and multivariate time series models: a survey, Journal of Economic Surveys, 22, 458-497, (2008)
[105] Singh, S. K. (2013). Chief Operations Officer (COO) of Arkieva (http://www.arkieva.com). Private communication to the corresponding author.
[106] Spithourakis, G. P.; Petropoulos, F.; Babai, M. Z.; Nikolopoulos, K.; Assimakopoulos, V., Improving the performance of popular supply chain forecasting techniques: an empirical investigation, Supply Chain Forum: An international Journal, 12, 16-25, (2011)
[107] Spithourakis, G. P.; Petropoulos, F.; Nikolopoulos, K.; Assimakopoulos, V., A systemic view of the ADIDA framework, IMA Journal of Management Mathematics, 25, 125-137, (2014) · Zbl 1286.91080
[108] Spithourakis, G. P.; Petropoulos, F.; Nikolopoulos, K.; Assimakopoulos, V., Amplifying the learning effects via a forecasting and foresight support system, International Journal of Forecasting, 31, 20-32, (2015)
[109] Strijbosch, L. W.G.; Heuts, R. M.J.; Moors, J. J.A., Hierarchical estimation as a basis for hierarchical forecasting, IMA Journal of Management Mathematics, 19, 193-205, (2008) · Zbl 1145.90316
[110] Strijbosch, L. W.G.; Heuts, R. M.J.; van der Schoot, E. H.M., A combined forecast-inventory control procedure for spare parts, Journal of the Operational Research Society, 51, 1184-1192, (2000) · Zbl 1107.90312
[111] Strijbosch, L. W.G.; Moors, J. J.A., Calculating the accuracy of hierarchical estimation, IMA Journal of Management Mathematics, 21, 303-315, (2010) · Zbl 1280.91134
[112] Syntetos, A. A., Forecasting by temporal aggregation, Foresight, 34, 6-11, (2014)
[113] Syntetos, A. A.; Boylan, J. E., The accuracy of intermittent demand estimates, International Journal of Forecasting, 21, 303-314, (2005)
[114] Syntetos, A. A.; Boylan, J. E., On the stock-control performance of intermittent demand estimators, International Journal of Production Economics, 103, 36-47, (2006)
[115] Syntetos, A. A.; Boylan, J. E.; Croston, J. D., On the categorization of demand patterns, Journal of the Operational Research Society, 56, 495-503, (2005) · Zbl 1095.90510
[116] Syntetos, A. A.; Boylan, J. E.; Disney, S. M., Forecasting for inventory planning: a 50-year review, Journal of the Operational Research Society, 60, S1, 149-160, (2009) · Zbl 1168.90305
[117] Syntetos, A. A.; Georgantzas, N. C.; Boylan, J. E.; Dangerfield, B. C., Judgement and supply chain dynamics, Journal of the Operational Research Society, 62, 1138-1158, (2011)
[118] Syntetos, A. A.; Kholidasari, I.; Naim, M., The effects of integrating management judgement into OUT levels: in or out of context?, European Journal of Operational Research, (2015), advance online publication · Zbl 1346.90561
[119] Syntetos, A. A.; Nikolopoulos, K.; Boylan, J. E., Judging the judges through accuracy-implication metrics: the case of inventory forecasting, International Journal of Forecasting, 26, 134-143, (2010)
[120] Syntetos, A. A.; Nikolopoulos, K.; Boylan, J. E.; Fildes, R.; Goodwin, P., The effects of integrating management judgment into intermittent demand forecasts, International Journal of Production Economics, 118, 72-81, (2009)
[121] Syntetos, A. A.; Teunter, R. H.; Prak, D., On the calculation of safety stocks, European Journal of Operational Research, (2015), under review (working paper available from the corresponding author)
[122] Tiao, G. C., Asymptotic behaviour of temporal aggregates of time series, Biometrika, 59, 525-531, (1972) · Zbl 0263.62051
[123] Viswanathan, S.; Widiarta, H.; Piplani, R., Forecasting aggregate time series with intermittent subaggregate components: top-down versus bottom-up forecasting, IMA Journal of Management Mathematics, 19, 275-287, (2008) · Zbl 1144.91362
[124] Weatherford, L. R.; Kimes, S. E.; Scott, D. A., Forecasting for hotel revenue management: testing aggregation against disaggregation, The Cornell Hotel and Restaurant Administration Quarterly, 42, 53-64, (2001)
[125] Weiss, A. A., Systematic sampling and temporal aggregation in time series models, Journal of Econometrics, 26, 271-281, (1984)
[126] Widiarta, H.; Viswanathan, S.; Piplani, R., On the effectiveness of top-down strategy for forecasting autoregressive demands, Naval Research Logistics, 54, 176-188, (2007) · Zbl 1126.62088
[127] Widiarta, H.; Viswanathan, S.; Piplani, R., Forecasting item-level demands: an analytical evaluation of top-down versus bottom-up forecasting in a production-planning framework, IMA Journal of Management Mathematics, 19, 207-218, (2008) · Zbl 1144.90373
[128] Widiarta, H.; Viswanathan, S.; Piplani, R., Forecasting aggregate demand: an analytical evaluation of top-down versus bottom-up forecasting in a production planning framework, International Journal of Production Economics, 118, 87-94, (2009)
[129] Willemain, T. R.; Smart, C. N.; Shocker, J. H.; DeSautels, P. A., Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston’s method, International Journal of Forecasting, 10, 529-538, (1994)
[130] Withycombe, R., Forecasting with combined seasonal indexes, International Journal of Forecasting, 5, 547-552, (1989)
[131] Yelland, P., Bayesian forecasting of parts demand, International Journal of Forecasting, 26, 374-396, (2010)
[132] Zellner, A.; Tobias, J., A note on aggregation, disaggregation and forecasting performance, Journal of Forecasting, 19, 457-469, (2000)
[133] Zhang, X., Evolution of ARMA demand in supply chains, Manufacturing and Service Operations Management, 6, 195-198, (2004)
[134] Zotteri, G.; Kalchschmidt, M., A model for selecting the appropriate level of aggregation in forecasting processes, International Journal of Production Economics, 108, 74-83, (2007)
[135] Zotteri, G.; Kalchschmidt, M.; Caniato, F., The impact of aggregation level on forecasting performance, International Journal of Production Economics, 93-94, 479-491, (2005)
[136] Zotteri, G.; Kalchschmidt, M.; Saccani, N., Forecasting by cross-sectional aggregation, Foresight, 35, 35-41, (2014)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.