Supply chain forecasting: Theory, practice, their gap and the future

Syntetos, Aris A.; Babai, Zied; Boylan, John E.; Kolassa, Stephan; Νικολόπουλος, Κωνσταντίνος

doi:10.1016/j.ejor.2015.11.010

Cited by 238 publications

(131 citation statements)

References 112 publications

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“…However, in the case of many homogeneous demand series and small samples, the top-down approach can generate more accurate forecasts (Jin, Williams, Tokar, and Waller, 2015;Zotteri and Kalchschmidt, 2007;Zotteri et al, 2005). For instance, different brands of ice cream will have a similar seasonality with a summer peak, which may not be easily detected for low-volume flavors but can be estimated at a group level and applied on the product level (Syntetos, Babai, Boylan, Kolassa, and Nikolopoulos, 2016). Song (2015) suggested that it is beneficial to model and forecast at the level of data where stronger and more seasonal information can be collected.…”

Section: Forecasting Within a Product Hierarchymentioning

confidence: 99%

Retail forecasting: Research and practice

Fildes

Kolassa

2022

International Journal of Forecasting

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186

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This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice.

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Section: Forecasting Within a Product Hierarchymentioning

confidence: 99%

Retail forecasting: Research and practice

Fildes

Kolassa

2022

International Journal of Forecasting

Self Cite

186

View full text Add to dashboard Cite

show abstract

“…Therefore, the forecasting problem, from a practitioner perspective, reduces to finding meaningful minimum variance clusters from which forecasts can be constructed to support operational decision making. This problem is not unique to supply forecasting, as determining how to aggregate and forecast demand in multi-location and multi-product systems is an important area of research (See for example [51,52,53]).…”

Section: Summary Of Findingsmentioning

confidence: 99%

Analysis and prediction of food donation behavior for a domestic hunger relief organization

Davis

Jiang

Morgan

et al. 2016

International Journal of Production Economics

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Non-profit hunger relief organizations strive to counteract the negative effects of food insecurity by providing food and services to individuals in need. These organizations rely on the goodwill of donors, that provide cash or in-kind food donations, to help them meet their objectives. Food donations vary considerably over time in terms of their quantity and frequency, thus presenting a challenge as organizations strive to satisfy the needs of the population being served. Using data from a domestic hunger relief organization, we analyze the uncertainty associated with in-kind food donations. A comprehensive numerical study is performed to quantify the extent of uncertainty in terms of the donor, product, and supply chain structure. In addition, several predictive models are developed to estimate the quantity of in-kind donations. Based on our numerical study, we show the relationship between forecast accuracy, donation behavior, and data uncertainty. Additionally, implications on operational efficiency and service delivery are discussed.

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“…Here, we are mainly concerned with the reverse loop and the forecasting of returns, as this is the additional aspect of supply uncertainty remanufacturers face; we refer the interest to Syntetos et al (2016) for a literature review on demand forecasting methods. Depending on the setting, these returns forecasts can be used on their own, or subtracted from the demand forecasts to obtain net demand forecasts.…”

Section: Forecasting In Closed-loop Supply Chainsmentioning

confidence: 99%

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Goltsos

Ponte

Wang

et al. 2018

International Journal of Production Research

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Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three 'pillars' of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a 'boomerang' effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the welldeveloped research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems.

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Supply chain forecasting: Theory, practice, their gap and the future

Cited by 238 publications

References 112 publications

Retail forecasting: Research and practice

Retail forecasting: Research and practice

Analysis and prediction of food donation behavior for a domestic hunger relief organization

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

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