Enriching demand forecasts with managerial information to improve inventory replenishment decisions: Exploiting judgment and fostering learning

Rekik, Yacine; Glock, C. H.; Syntetos, Aris A.

doi:10.1016/j.ejor.2017.02.001

Cited by 19 publications

(12 citation statements)

References 42 publications

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“…We model it as an independent and identically distributed (i.i.d.) random variable following a normal distribution with mean and variance (Chatfield et al 2004, Rekik et al 2017. Products are assumed to be held by the Customer during periods, representing the consumption lead time.…”

Section: Closed-loop Supply Chain Modelmentioning

confidence: 99%

On the dynamics of closed-loop supply chains under remanufacturing lead time variability

Domínguez

Cannella

Ponte

et al. 2020

Omega

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A closed-loop supply chain with remanufacturing lead time variability is analysed  Through simulation, the dynamic performance is assessed under a variety of scenarios  Different levels of information transparency are considered  The variability of remanufacturing lead times seriously damage the dynamic behaviour  Managerial implications are discussed

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Section: Closed-loop Supply Chain Modelmentioning

confidence: 99%

On the dynamics of closed-loop supply chains under remanufacturing lead time variability

Domínguez

Cannella

Ponte

et al. 2020

Omega

View full text Add to dashboard Cite

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“…We believe that the problem addressed in this paper comes close to falling in that variety. While there is little published work in this area, a notable recent exception is (Rekik, Glock, and Syntetos 2017) which investigates expert judgmental adjustments from a statistical forecast in a finite-time horizon setting and proposes an analytical model to support this.…”

Section: Implications For Practicementioning

confidence: 99%

“…This is an important problem in practice but one which has received little attention in the literature. A notable recent exception in this regard is work by Rekik, Glock, and Syntetos (2017).…”

Section: Introductionmentioning

confidence: 99%

Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context

Boutselis

McNaught

2019

International Journal of Production Economics

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A problem faced by some Logistic Support Organisations (LSOs) is that of forecasting the demand for spare parts, corresponding to equipment failures within the system. Here we are particularly concerned with a final phase of operations and the opportunity to place only a single order to cover demand during this phase. The problem is further complicated when the service logistics context can change during this final phase, e.g. as the number of systems supported or the LSO's resources change. Such a problem is typical of the final phase of many military operations. The LSO operates the recovery and repair loop for the equipment in question. By developing a simulation of the LSO, we can generate synthetic operational data regarding equipment breakdowns, etc. We then split that data into a training set and a test set in order to compare several approaches to forecasting demand in the final operational phase. We are particularly interested in the application of Bayesian network models for this type of forecasting since these offer a way of combining hard observational data with subjective expert opinion. Different LSO configurations were simulated to create a test dataset and the simulation results were compared with the various forecasts. The BN that learned from training data performed best, followed by a hybrid BN design combining expert elicitation and machine learning, and then a logistic regression model. An expert-adjusted exponential smoothing model was the poorest performer and these differences were statistically significant. The paper concludes with a discussion of the results, some implications for practice and suggestions for future work.

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“…Their impacts are assessed using a fuzzy inference engine that ensures the coherence of the results and limits the biases in decision making. Rekik et al (2017) analyze judgmental adjustments to replenishment order quantities in a newsvendor setting where information available to managers is reflected in the form of a signal. They find it beneficial even when the probability of a correct signal is not known and offer some interesting insights for judgmentally adjusting order quantities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The approach involves identification and classification of four different types of potential future events and assessment of their impacts using a fuzzy inference engine that ensures the coherence of the results and limits the biases in decision making. Rekik et al (2017) analyze judgmental adjustments in a newsvendor setting while determining replenishment order sizes and where demand information availability is in a form of signal. It is found beneficial even when the probability of a correct signal is unknown and offers some interesting insights for judgmentally adjusting the order sizes.…”

Section: Literature Reviewmentioning

confidence: 99%

Extended distribution‐free newsvendor models with demand updates using experts’ judgment

Nagare¹,

Dutta

Cheikhrouhou

2019

Int Trans Operational Res

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Retailers of short life cycle products, such as [life and style goods are required to commit an order with their suppliers far ahead of their selling seasons inclusive scant demand information. Most of the time, they practice preseason two-stage ordering (instants) that provide an opportunity to modify an initial order based on updated demand forecast obtained at a later stage. The present paper utilizes expert judgment to assess potential impact(s) of contextual information acquired between two instants, in order to revise demand forecast. Additionally, the scant demand information available may not reveal the underlying demand distribution. In this context, we develop inventory models under distribution free newsvendor framework to determine optimal order quantity and weight factor considering also the revised demand forecast. The models consider bidirectional changes in demand and three cases of demand variability: a constant variance case ("CVC"), a constant coefficient of variation case ("CCVC"), and General Case ("GC"). The models developed in the first instance without constraints are subsequently extended by enforcing constraints for practical consideration such as limited storage space or maintenance of pre-defined service level. Moreover, these single-item models are extended to multi-items case to improve their practical utility. The closed form expressions are obtained for decision variables and lower bound of expected profit and their results are discussed using numerical examples. Results show economic benefits in revising the demand forecast using expert judgment and/or negative impact of constraints and/or negative role of demand variability. In addition, a case study is presented to illustrate the potential demand impact assessment and the application of the proposed models within real life circumstances.

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Enriching demand forecasts with managerial information to improve inventory replenishment decisions: Exploiting judgment and fostering learning

Cited by 19 publications

References 42 publications

On the dynamics of closed-loop supply chains under remanufacturing lead time variability

On the dynamics of closed-loop supply chains under remanufacturing lead time variability

Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context

Extended distribution‐free newsvendor models with demand updates using experts’ judgment

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