An inventory position-based policy for better supply chain performance

Pamulety, T. Chinna; George, Joby; Pillai, V. Madhusudanan

doi:10.1080/21681015.2016.1243166

Cited by 8 publications

(10 citation statements)

References 46 publications

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“…In this study, a single product divergent supply chain with four-stage and eight members is considered (Pamulety et al, 2017) and is shown in Fig. 4.1.…”

Section: Data Generation Using Simulationmentioning

confidence: 99%

“…Wholesalers place orders to the distributor (W2,1 and W2,2 place orders to D3,1). The distributor places an order with the factory (F4,1), and finally, the factory places an order for production (Pamulety et al, 2017). The inventory of each stage is reviewed periodically and the decision regarding when and how much to order for any member depends on the inventory policy followed by respective members (Lau et al, 2004).…”

Section: Data Generation Using Simulationmentioning

confidence: 99%

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Stockout Prediction in Multi Echelon Supply Chain using Machine Learning Algorithms

Shukla,

Pillai

2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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The main objective of a supply chain is to fulfil the customer demands in the right quantity at the right time. If an organization cannot meet the customer demand, stockout or out-of-stock situations happen. Inventory stockouts are costly to the organization and are very common in supply chains. It not only results in the loss of revenue but also affects the service level, which results in a loss of competitive advantage. Stockouts can be prevented by proper inventory planning and control. For any organization, it is crucial to maintain optimum inventory levels to ensure customer satisfaction. These days, technologies like Artificial Intelligence and Machine Learning give organizations the ability to foresee the future and proactively manage their inventory in the supply chain. This study investigates various supervised machine learning classifiers. It proposes the best machine learning stock out prediction model for each member of a four-stage divergent supply chain with eight members. Since the dataset for such a supply chain is not available, a supply chain operation simulation has been conducted under three distinct inventory replenishing policies such as Order-Up-To (OUT), Order-Up-To Smoothing (OUTS) and (s, S). Data generated from the simulation is divided into two sets, the train set and the test set. Various supervised machine learning algorithms are trained using training dataset. A five-fold cross-validation technique is adopted for the validation of the model. A random search cross-validation technique adjusts the hyperparameters of each classifier. The performance of the model is evaluated on the test dataset based on the assessment matrices, and it is found that boosting algorithms perform better than the other classifiers. This study proposes a meta-learning-based stacked ensemble model, using XG boost, Ada boost and random forest classifiers as the base model. The performance evaluation shows that for each supply chain member, the stacked ensemble model performs better than other classifiers, including boosting classifiers.

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“…In this study, a single product divergent supply chain with four-stage and eight members is considered (Pamulety et al, 2017) and is shown in Fig. 4.1.…”

Section: Data Generation Using Simulationmentioning

confidence: 99%

Section: Data Generation Using Simulationmentioning

confidence: 99%

Stockout Prediction in Multi Echelon Supply Chain using Machine Learning Algorithms

Shukla,

Pillai

2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…It equals the sum of the inventory holding cost, lost sales, ordering, and transportation costs over the planning horizon (Pamulety et al [10] ). (6).…”

Section: Total Cost Of Supply Chain (Tcsc)mentioning

confidence: 99%

“…The proper choice of inventory policy has an important role in supply chain performance such as operational cost reduction, production plan smoothness and the customer service level maximization [7] [8] [9]. Pamulety et al [10] presented a comprehensive review and summary of different inventory policies, discussed in literature.…”

Section: Introductionmentioning

confidence: 99%

Inventory Policy for Improved Supply Chain Performance Under Constrained Supply

Afifi

El-Kharbotly

Shedid

2020

Journal of Al-Azhar University Engineering Sector

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This paper proposes and evaluates an inventory policy of position-based type to enhance the supply chain performance under supplier capacity constraints. The proposed policy has been assessed, for different demand scenarios, by the fill rate, bullwhip effect, and total cost of the supply chain. For all scenarios, the supply chain performance of the proposed policy has been compared with the performance of other known inventory policies, such as Order-Up-To, (s, S), and (s, Q) using modeling and simulation methodology. The results show performance improvements using the proposed policy compared to the other policies. It provides a guide line for industrial managers, facing supplier capacity constraints, to select the best inventory policy under different operating conditions in a supply chain.

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“…alternatives based on the three performance measures. The details of GRA are given inMathew andRajendrakumar 2011 andPamulety et al 2017. For comparing the supply chains under experimental case and inventory policies, the performance measures of them are given in Table3.…”

mentioning

confidence: 99%

Impact of Ordering Decisions on Performance of a Supply Chain – An Experimental and Simulation Study

Pamulety¹,

Pillai

2022

jmmf

Self Cite

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A supply chain consists of a network of organizations. Each organization in it acts as either customer or supplier to others. Ordering and replenishment decisions of each organization or stage contribute to supply chain performance. A simulation study and experimentation were conducted in this study to determine the impact of ordering decisions on supply chain performance. The ordering decisions at each stage are taken by intuition for experimentation and are taken by inventory policies for simulation. Under customer demand distribution information sharing, the performance of a supply chain is evaluated and compared between those two conditions. A supply chain role play game software package is used to evaluate supply chain performance by intuition, and simulation is used to evaluate different inventory policies. Fixed order policy, Order Up-to Level (OUL), Modified OUL (MOUL), (r, Q) and (r, S) inventory policies are considered in this study. The performance measures used are the total supply chain inventory per period, bullwhip effect and supply chain fill rate. Grey Relational Analysis (GRA) is used to identify the best method to take decisions in supply chain. Results show that supply chain performance is best under MOUL policy. Based on the results of this study, supply chain members are encouraged to identify the best inventory policy and use that rather than making decisions based on intuition.

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An inventory position-based policy for better supply chain performance

Cited by 8 publications

References 46 publications

Stockout Prediction in Multi Echelon Supply Chain using Machine Learning Algorithms

Stockout Prediction in Multi Echelon Supply Chain using Machine Learning Algorithms

Inventory Policy for Improved Supply Chain Performance Under Constrained Supply

Impact of Ordering Decisions on Performance of a Supply Chain – An Experimental and Simulation Study

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