A comparative study of forecasting methods for sporadic demand in an auto service station

Mor, Rahul S.; Nagar, Jitendra; Bhardwaj, Arvind

doi:10.1504/ijbfmi.2019.099009

Cited by 3 publications

(2 citation statements)

References 14 publications

(16 reference statements)

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“…The SBA method introduced a bias term

to mitigate the uncertainty of intermittent distributions. In addition, some studies proposed different metrics, such as the average demand interval (ADI) and the square of the coefficient of variation (CV2) [ 16 ], to explore intermittent characteristics to better extract intrinsic information from demand sequences [ 17 ]. Another typical approach is to perform hierarchical clustering on demand sequences [ 18 ], which divides the original sequences with weak overall patterns into multiple clusters with more significant patterns.…”

Section: Introductionmentioning

confidence: 99%

Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization

Hong

Ren

et al. 2023

Sensors

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Demand for spare parts, which is triggered by element failure, project schedule and reliability demand, etc., is a kind of sensing data to the aftermarket service of large manufacturing enterprises. Prediction of the demand for spare parts plays a crucial role in inventory management and lifecycle quality management for the aftermarket service of large-scale manufacturing enterprises. In real-life applications, however, demand for spare parts occurs randomly and fluctuates greatly, and the demand sequence shows obvious intermittent distribution characteristics. Additionally, due to factors such as reporting mistakes made by personnel or environmental changes, the actual data of the demand for spare parts are prone to abnormal variations. It is thus hard to capture the evolutional pattern of the demand for spare parts by traditional time series forecasting methods. The reliability of prediction results is also reduced. To address these concerns, this paper proposes a tensor optimization-based robust interval prediction method of intermittent time series for the aftersales demand for spare parts. First, using the advantages of tensor decomposition to effectively mine intrinsic information from raw data, a sequence-smoothing network based on tensor decomposition and a stacked autoencoder is proposed. Tucker decomposition is applied to the hidden features of the encoder, and the obtained core tensor is reconstructed through the decoder, thus allowing us to smooth outliers in the original demand sequence. An alternating optimization algorithm is further designed to find the optimal sequence feature representation and tensor decomposition factors for the extraction of the evolutionary trend of the intermittent series. Second, an adaptive interval prediction algorithm with a dynamic update mechanism is designed to obtain point prediction values and prediction intervals for the demand sequence, thereby improving the reliability of the forecast. The proposed method is validated using the actual aftersales data from a large engineering manufacturing enterprise in China. The experimental results demonstrate that, compared with typical time series prediction methods, the proposed method can effectively grab the evolutionary trend of various intermittent series and improve the accuracy of predictions made with small-sample intermittent series. Moreover, the proposed method provides a reliable elastic prediction interval when distortion occurs in the prediction results, offering a new solution for intelligent planning decisions related to spare parts in practical maintenance.

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“…The SBA method introduced a bias term

Section: Introductionmentioning

confidence: 99%

Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization

Hong

Ren

et al. 2023

Sensors

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“…The reduced costs, eradication of non-value-added activities (NVAs), demand forecasting, and process integration are fundamental to modern manufacturing enterprises (Mor et al, 2021(Mor et al, , 2019Chen and Simchi-Levi, 2004). Beemsterboer et al (2016), Pauls-Worm et al (2016 considered a general class where the order quantity of the products is permitted to differ where the closed-form expressions are used to determine the optimal order numbers.…”

Section: Introductionmentioning

confidence: 99%

Achieving cost efficiency through increased inventory leanness: Evidence from manufacturing industry

Mor

Kumar

Yadav

et al. 2021

Production Engineering Archives

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Inventory management’s fundamental problem starts with maintaining equilibrium among the operating efficiency, cost of investment, and other allied costs with extensive inventories to keep the actual conflicts at the minimum while optimizing the inventory holding levels. But, inventory management practices have not been well exploited in various manufacturing industries yet. In this study, inventory management tools, i.e., ABC and VED analysis, have been applied in the manufacturing industry, considering 146 items as raw material for an assembly. A total of 15 items under ‘AV’ class have been identified that consume 82.05 % of the total cost, and these items need strict control and frequent ordering. Sigma level of suppliers is also calculated, which comes out to be 2.36, and it must be improved to reduce the overall inventory cost.

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Demand Forecasting of the Short-Lifecycle Dairy Products

Mor

Jaiswal

Singh

et al. 2018

Understanding the Role of Business Analytics

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A comparative study of forecasting methods for sporadic demand in an auto service station

Cited by 3 publications

References 14 publications

Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization

Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization

Achieving cost efficiency through increased inventory leanness: Evidence from manufacturing industry

Demand Forecasting of the Short-Lifecycle Dairy Products

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