Inventory - forecasting: mind the gap

We are concerned with the interaction and integration between demand forecasting and inventory control, in the context of supply chain operations. The majority of the literature is fragmented. Forecasting research more often than not assumes forecasting to be an end in itself, disregarding any subsequent stages of computation that are needed to transform forecasts into replenishment decisions. Conversely, most contributions in inventory theory assume that demand (and its parameters) are known, in effect disregarding any preceding stages of computation. Explicit recognition of these shortcomings is an important step towards more realistic theoretical developments, but still not particularly helpful unless they are somehow addressed. Even then, forecasts often constitute exogenous variables that serially feed into a stock control model. Finally, there is a small but growing stream of research that is explicitly built around jointly tackling the inventory forecasting question. We introduce a framework to define four levels of integration: from disregarding, to acknowledging, to partly addressing, to fully understanding the interactions. Focusing on the last two, we conduct a structured review of relevant (integrated) academic contributions in the area of forecasting and inventory control and argue for their classification with regard to integration. We show that the development from one level to another is in many cases chronological in order, but also associated with specific schools of thought. We also argue that although movement from one level to another adds realism, it also adds complexity in terms of actual implementations, and thus a trade-off exists. The article makes a contribution into an area that has always been fragmented despite the importance of bringing the forecasting and inventory communities together to solve problems of common interest. We close with an indicative agenda for further research and a call for more theoretical contributions, but also more work that would help to expand the empirical knowledge base in this area

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

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 well-developed 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

Forecasting for lead-time period by temporal aggregation: whether to combine and how

Temporal aggregation (TA) refers to transforming a time series from higher to lower frequencies (e.g. monthly to quarterly). There are two different types of aggregation: overlapping and non-overlapping; which, when added to the option of using original time series, present the forecaster with three approaches to produce required forecasts over the lead-time period: i) non-overlapping aggregation (NOA) ii) overlapping aggregation (OA); and iii) bottom-up to aggregate forecast (BU). Forecasters may then need to decide which approach to use or alternatively combine the forecasts generated by the three approaches, instead. In this study, we design and execute an experiment using the M4 competition dataset, to explore the effect of different initial frequencies (i.e. daily, monthly, and quarterly), data aggregation levels and combination methods on forecast accuracy. We are surprised to find that neither temporal aggregation strategies have an overall gain on forecasting accuracy. Equally concerning is the fact that straight (average) combinations of these forecasts are similarly of no benefit to the accuracy. To extract the benefits of both well-supported individual forecasting practices of temporal aggregation and combination, we propose a framework that aims to combine temporal aggregation and forecasting combinations using a polynomially weighted average with multiple learning rates. We find considerable overall improvement in forecasting accuracy by using the proposed combination, especially for longer lead-times. We discuss areas where the framework is expected to perform best in the future and conclude that further research is required in this area. We note that our method can work in parallel of others and close with an agenda for further research on forecasting by temporal aggregation

Forecasting for remanufacturing: the effects of serialization

Remanufacturing operations rely upon accurate forecasts of demand and returned items. Return timing and quantity forecasts help estimate net demand (demand minus returns) requirements. Based on a unique data set of serialized transactional issues and returns from the Excelitas Group and one of their defense contractors, Qioptiq, we assess the empirical performance of some key methods in the area of returns forecasting. We extend their application (for net demand forecasting), by considering that demand is also subject to uncertainty and thus needs to be forecast. Information on remanufacturing costs allows for an evaluation of the inventory implications of such forecasts under various settings. A foray into the literature on information technologies enables a discussion on the interface between information availability and forecast accuracy and utility. We find that serialization accounts for considerable forecast accuracy benefits, and that the accuracy of demand forecasts is as important as that of returns. Further, we show how the combined returns and demand forecast uncertainty affects the inventory performance. Finally, we identify opportunities for further improvements for the operations of Qioptiq, and for remanufacturing operations in general

Inventory – forecasting: Mind the gap.

We are concerned with the interaction and integration between demand forecasting and inventory control, in the context of supply chain operations. The majority of the literature is fragmented. Forecasting research more often than not assumes forecasting to be an end in itself, disregarding any subsequent stages of computation that are needed to transform forecasts into replenishment decisions. Conversely, most contributions in inventory theory assume that demand (and its parameters) are known, in effect disregarding any preceding stages of computation. Explicit recognition of these shortcomings is an important step towards more realistic theoretical developments, but still not particularly helpful unless they are somehow addressed. Even then, forecasts often constitute exogenous variables that serially feed into a stock control model. Finally, there is a small but growing stream of research that is explicitly built around jointly tackling the inventory forecasting question. We introduce a framework to define four levels of integration: from disregarding, to acknowledging, to partly addressing, to fully understanding the interactions. Focusing on the last two, we conduct a structured review of relevant (integrated) academic contributions in the area of forecasting and inventory control and argue for their classification with regard to integration. We show that the development from one level to another is in many cases chronological in order, but also associated with specific schools of thought. We also argue that although movement from one level to another adds realism, it also adds complexity in terms of actual implementations, and thus a trade-off exists. The article makes a contribution into an area that has always been fragmented despite the importance of bringing the forecasting and inventory communities together to solve problems of common interest. We close with an indicative agenda for further research and a call for more theoretical contributions, but also more work that would help to expand the empirical knowledge base in this area

Probabilistic forecasting of daily COVID-19 admissions using machine learning

Accurate forecasts of daily COVID-19 admissions are critical for healthcare planners and decision-makers to better manage scarce resources during and around infection peaks. Numerous studies have focused on forecasting COVID-19 admissions at the national or global levels. Localised predictions are vital, as they allow for resource planning redistribution, but also scarce and harder to get right. Several possible indicators can be used to predict COVID-19 admissions. The inherent variability in the admissions necessitates the generation and evaluation of the forecast distri- bution of admissions, as opposed to producing only a point forecast. In this study, we propose a quantile regression forest (QRF) model for probabilistic forecasting of daily COVID-19 admissions for a local hospital trust (aggregation of 3 hospitals), up to 7-days ahead, using a multitude of different predictors. We evaluate point forecast accuracy as well as the accuracy of the forecast distribution using appro- priate measures. We provide evidence that QRF outperforms univariate time series methods and other more sophisticated benchmarks. Our findings also show that lagged admissions, total positive cases, daily tests performed, and Google grocery and Apple driving are the most salient predictors. Finally, we highlight areas where further research is needed