A study on behaviour of bullwhip effect in (R, S) inventory control system considering DWT-MGGP demand forecasting model

Jaipuria, Sanjita; Mahapatra, Siba Sankar

doi:10.1108/jm2-04-2018-0053

Cited by 9 publications

(8 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The most widely used forecasting method in the inventory system is the exponential smoothing method (ESM) due to its stationary pattern fluctuations, sensitivity for estimating the forecast values of any targeted observations and ease of calculation, even with a small number of observations (Baykal–Gürsoy and Erkip, 2010; Durbin and Koopman, 2012; Jaipuria and Mahapatra, 2019; Koehler et al , 2012). By contrast, other methods (e.g.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Supply chain management and market responsiveness: a simulation study

Aljanabi

Ghafour

2020

JBIM

View full text Add to dashboard Cite

Purpose This study aims to provide a practical solution to the relationship between supply chain (SC) integration and market responsiveness (MR). A method is proposed to integrate SC and MR parameters, namely, product supply and demand in the context of low-value commodities (e.g. cement). Design/methodology/approach Simulation and forecasting approaches are adopted to develop a potential procedure for addressing demand during lead time. To establish inventory measurements (safety stock and reorder level) and increase MR and the satisfaction of customer’s needs, this study considers a downstream SC including manufacturers, depots and central distribution centers that satisfies an unbounded number of customers, which, in turn, transport the cement from the industrialist. Findings The demand during lead time is shown to follow a gamma distribution, a rare probability distribution that has not been considered in previous studies. Moreover, inventory measurements, such as the safety stock, depending on the safety factor under a certain service level (SL), which enables the SC to handle different responsiveness levels in accordance with customer requests. In addition, the quantities of the safety stock and reorder point represent an optimal value at each position to avoid over- or understocking. The role of SC characteristics in MR has largely been ignored in existing research. Originality/value This study applies SC flexibility analyzes to overcome the obstacles of analytical methods, especially when the production process involves probabilistic variables such as product availability and demand. The use of an efficient method for analyzing the forecasting results is an unprecedented idea that is proven efficacious in investigating non-dominated solutions. This approach provides near-optimal solutions to the trade-off between different levels of demand and the SC responsiveness (SLs) with minimal experimentation times.

show abstract

Section: Theoretical Backgroundmentioning

confidence: 99%

Supply chain management and market responsiveness: a simulation study

Aljanabi

Ghafour

2020

JBIM

View full text Add to dashboard Cite

show abstract

“…Al-Khazraji et al (2018) used particle swarm optimisation to solve a multi-objective model for inventory control in a supply chain with a single factory and a single retailer when the demand rate is assumed to be fluctuating. Jaipuria and Mahapatra (2019) investigated bullwhip effects on a supply chain under a novel forecasting technique. The technique was developed by combing an existing forecasting technique, namely, discrete wavelet transformation, with a genetic algorithm previously used to forecast demand.…”

Section: Literature Reviewmentioning

confidence: 99%

Three-echelon supply chain inventory model for growing items

Sebatjane

Adetunji

2019

JM2

View full text Add to dashboard Cite

Purpose The purpose of this paper is to formulate a coordinated inventory control model for growing items in a supply chain with farming, processing and retail operations. The farmer grows newborn items and then delivers them to a processor once the items mature. At the processing plant, the items are slaughtered, cut and packaged at a specified rate. The processor then delivers a certain number of equally sized shipments of processed items to a retailer who satisfies customer demand. Design/methodology/approach A cost minimisation inventory model describing the problem at hand is formulated with the number of shipments and the cycle time being the decision variables. A solution algorithm for solving the problem is presented and applied to a numerical example. Findings Opting for an integrated policy is favourable to all supply chain members. When the proposed model is compared to equivalent independent and equal-cycle time replenishment policies, the total cost savings amount to 3 and 14 per cent, respectively. Social implications The model can serve as a guideline for procurement managers dealing with growing items to better their inventory management practices. Considerable cost savings in food production chains can be achieved through improved inventory control, and these savings can be used to cushion consumers against rising food prices. Originality/value Most previously published models on inventory management for growing items were formulated under the assumption that the items are grown and then sold to consumers instantaneously. In real food production systems, the items need to be transformed and packaged into a consumable form before customer demand is met. The model presented in this paper accounts for this and is therefore more realistic.

show abstract

“…Such combination between DWT and machine-learning model for forecasting applications included daily precipitation forecast (Kisi and Cimen, 2012), monthly tourism forecast (Kummong and Supratid, 2016) using DWT-SVR and DWT-NARX, respectively, and drought and river flow long-term multi-step forecasts (Belayneh et al , 2014; Badrzadeh et al , 2013) based on DWT-BPNN, DWT-SVR, DWT-ANFIS, where ANFIS refers to adaptive neuro-fuzzy inference system (Jang, 1993). In addition, the work (Jaipuria and Mahapatra, 2019) combined DWT and multi-gene genetic programming (MGGP), namely, DWT-MGGP for demand forecasts, with regard to inventory control under uncertain environment. A modified version of DWT, maximal overlap discrete wavelet transform (MODWT) (Nason and Sachs, 1999; Percival and Walden, 2000) was developed to be executed naturally for all-time series data size.…”

Section: Related Workmentioning

confidence: 99%

Long-term forecasting system using wavelet – nonlinear autoregressive neural network conjunction model

Kummong

Supratid

2019

JM2

View full text Add to dashboard Cite

Purpose An accurate long-term multi-step forecast provides crucial basic information for planning and reinforcing managerial decision-support. However, nonstationarity and nonlinearity, normally consisted of several types of managerial data can seriously ruin the forecasting computation. This paper aims to propose an effective long-term multi-step forecasting conjunction model, namely, wavelet–nonlinear autoregressive neural network (WNAR) conjunction model. The WNAR combines discrete wavelet transform (DWT) and nonlinear autoregressive neural network (NAR) to cope with such nonstationarity and nonlinearity within the managerial data; as a consequence, provides insight information that enhances accuracy and reliability of long-term multi-step perspective, leading to effective management decision-making. Design/methodology/approach Based on WNAR conjunction model, wavelet decomposition is executed for efficiently extracting hidden significant, temporal features contained in each of six benchmark nonstationary data sets from different managerial domains. Then, each extracted feature set at a particular resolution level is fed into NAR for the further forecast. Finally, NAR forecasting results are reconstructed. Forecasting performance measures throughout 1 to 30-time lags rely on mean absolute percentage error (MAPE), root mean square error (RMSE), Nash-Sutcliffe efficiency index or the coefficient of efficiency (Ef) and Diebold–Mariano (DM) test. An effect of data characteristic in terms of autocorrelation on forecasting performances of each data set are observed. Findings Long-term multi-step forecasting results show the best accuracy and high-reliability performance of the proposed WNAR conjunction model over some other efficient forecasting models including a single NAR model. This is confirmed by DM test, especially for the short-forecasting horizon. In addition, rather steady, effective long-term multi-step forecasting performances are yielded with slight effect from time lag changes especially for the data sets having particular high autocorrelation, relative against 95 per cent degree of confidence normal distribution bounds. Research limitations/implications The WNAR, which combines DWT with NAR can be accounted as a bridge for the gap between machine learning, engineering signal processing and management decision-support systems. Thus, WNAR is referred to as a forecasting tool that provides insight long-term information for managerial practices. However, in practice, suitable exogenous input forecast factors are required on the managerial domain-by-domain basis to correctly foresee and effectively prepare necessary reasonable management activities. Originality/value Few works have been implemented to handle the nonstationarity, consisted of nonlinear managerial data to attain high-accurate long-term multi-step forecast. Combining DWT and NAR capabilities would comprehensively and specifically deal with the nonstationarity and nonlinearity difficulties at once. In addition, it is found that the proposed WNAR yields rather steady, effective long-term multi-step forecasting performance throughout specific long time lags regarding the data, having certainly high autocorrelation levels across such long time lags.

show abstract

A study on behaviour of bullwhip effect in (R, S) inventory control system considering DWT-MGGP demand forecasting model

Cited by 9 publications

References 60 publications

Supply chain management and market responsiveness: a simulation study

Supply chain management and market responsiveness: a simulation study

Three-echelon supply chain inventory model for growing items

Long-term forecasting system using wavelet – nonlinear autoregressive neural network conjunction model

Contact Info

Product

Resources

About