TY - JOUR

T1 - iETS: State space model for intermittent demand forecasting

AU - Svetunkov, Ivan

AU - Boylan, John E.

PY - 2023/11/30

Y1 - 2023/11/30

N2 - Inventory decisions relating to items that are demanded intermittently are particularly challenging. Decisions relating to termination of sales of product often rely on point estimates of the mean demand, whereas replenishment decisions depend on quantiles from interval estimates. It is in this context that modelling intermittent demand becomes an important task. In previous research, this has been addressed by generalised linear models or integer-valued ARMA models, while the development of models in state space framework has had mixed success. In this paper, we propose a general state space model that takes intermittence of data into account, extending the taxonomy of single source of error state space models. We show that this model has a connection with conventional non-intermittent state space models used in inventory planning. Certain forms of it may be estimated by Croston’s and Teunter–Syntetos–Babai (TSB) forecasting methods. We discuss properties of the proposed models and show how a selection can be made between them in the proposed framework. We then conduct a simulation experiment, empirically evaluating the inventory implications.

AB - Inventory decisions relating to items that are demanded intermittently are particularly challenging. Decisions relating to termination of sales of product often rely on point estimates of the mean demand, whereas replenishment decisions depend on quantiles from interval estimates. It is in this context that modelling intermittent demand becomes an important task. In previous research, this has been addressed by generalised linear models or integer-valued ARMA models, while the development of models in state space framework has had mixed success. In this paper, we propose a general state space model that takes intermittence of data into account, extending the taxonomy of single source of error state space models. We show that this model has a connection with conventional non-intermittent state space models used in inventory planning. Certain forms of it may be estimated by Croston’s and Teunter–Syntetos–Babai (TSB) forecasting methods. We discuss properties of the proposed models and show how a selection can be made between them in the proposed framework. We then conduct a simulation experiment, empirically evaluating the inventory implications.

KW - Industrial and Manufacturing Engineering

KW - Management Science and Operations Research

KW - Economics and Econometrics

KW - General Business, Management and Accounting

U2 - 10.1016/j.ijpe.2023.109013

DO - 10.1016/j.ijpe.2023.109013

M3 - Journal article

VL - 265

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

M1 - 109013

ER -