Determining order-up-to levels under periodic review for compound binomial (intermittent) demand

Teunter, Ruud H.; Syntetos, Aris A.; Babaï, Mohamed Zied

doi:10.1016/j.ejor.2009.09.013

Cited by 55 publications

(49 citation statements)

References 12 publications

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“…To fill the gap, Janssen et al [18] make some modifications and adopt the (R, s, Q) inventory model to calculate the reorder point with a service-level restriction. Teunter et al [19] restrict their study to the calculation of the mean and variance of lead time demand and derive closed-form expressions for the service level and the expected total cost under the (T, S) policy with a compound binomial demand, thereby avoiding complicated calculation of the mean and variance.…”

Section: Research Backgroundmentioning

confidence: 99%

Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy

Jiang

Tam

Bao

et al. 2018

Mathematical Problems in Engineering

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Management of electric equipment has a direct impact on companies' performance and profitability. Considering the critical role that electric power materials play in supporting maintenance operations and preventing equipment failure, it is essential to maintain an inventory to a reasonable level. However, a majority of these electric power materials exhibit an intermittent demand pattern characterized by random arrivals interspersed with time periods with no demand at all. These characteristics cause additional difficulty for companies in managing these electric power material inventories. In response to the above problem, this paper, based on the electric power material demand data of Shanghai Electric Power Company, develops a new method to determine the optimal order quantity * in a cost-oriented periodic review ( , ) system, whereby unsatisfied demands are backordered and demand follows a compound Erlang distribution.* corresponds to the value of that gives the minimum expected total inventory holding and backordering cost. Subsequently, an empirical investigation is conducted to compare this method with the Newsvendor model. Results verify its superiority in cost savings. Ultimately, considering the complicated calculation and low efficiency of that algorithm, this paper proposes an approximation and a heuristic algorithm which have a higher level of utility in a real industrial context. The approximation algorithm simplifies the calculation process by reducing iterative times while the heuristic algorithm achieves it by generalizing the relationship between the optimal order quantity * and mean demand interarrival rate .

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Section: Research Backgroundmentioning

confidence: 99%

Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy

Jiang

Tam

Bao

et al. 2018

Mathematical Problems in Engineering

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show abstract

“…If demand arrives according to a Bernoulli process, then the inter-demand intervals will follow a geometric distribution. Croston (1972), Janssen et al (1998), Syntetos et al (2005, and Teunter et al (2010b), among others, have modelled the demand arrival process as a Bernoulli one.…”

Section: Research Backgroundmentioning

confidence: 99%

Spare parts management: Linking distributional assumptions to demand classification

Lengu

Syntetos

Babaï

2014

European Journal of Operational Research

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Spare parts are known to be associated with intermittent demand patterns and such patterns cause considerable problems with regards to forecasting and stock control due to their compound nature that renders the normality assumption invalid. Compound distributions have been used to model intermittent demand patterns; there is however a lack of theoretical analysis and little relevant empirical evidence in support of these distributions. In this paper, we conduct a detailed empirical investigation on the goodness of fit of various compound Poisson distributions and we develop a distribution-based demand classification scheme the validity of which is also assessed in empirical terms. Our empirical investigation provides evidence in support of certain demand distributions and the work described in this paper should facilitate the task of selecting such distributions in a real world spare parts inventory context. An extensive discussion on parameter estimation related difficulties in this area is also provided.

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“…Extensions into the distribution of inventory costs are feasible and intuitively appealing (Luciano et al, 2003) and further work into the issue of the efficient computation of VaR would constitute an important addition to the current state of knowledge in the area of inventory control. Dataset #2 has been used in the studies conducted by Syntetos et al (2009a) and Teunter et al (2010) Dataset #3 has been used in the study conducted by Babai et al (2010). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 …”

Section: Conclusion Implications and Further Researchmentioning

confidence: 99%

On the demand distributions of spare parts

Syntetos

Babaï

Altay

2012

International Journal of Production Research

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Determining order-up-to levels under periodic review for compound binomial (intermittent) demand

Cited by 55 publications

References 12 publications

Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy

Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy

Spare parts management: Linking distributional assumptions to demand classification

On the demand distributions of spare parts

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