Influence of controllable lead time, premium price, and unequal shipments under environmental effects in a supply chain management

2019 ◽  
Vol 53 (4) ◽  
pp. 1427-1451 ◽  
Author(s):  
Baishakhi Ganguly ◽  
Biswajit Sarkar ◽  
Mitali Sarkar ◽  
Sarla Pareek ◽  
Muhammad Omair
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
Carbon Emission ◽  
Stackelberg Game ◽  
Transportation Cost ◽  
Setup Cost ◽  
Total Cost ◽  
Decision Variables ◽  
Premium Price ◽  
Optimum Cost

Recently, carbon emission becomes a major issue during transportation of products from one player to another player. Due to the increasing number of single-setup-multi-delivery (SSMD) policies by several industries, fixed and variable transportation cost and carbon emission cost are considered. The aim of the model is to reduce the total cost of supply chain for controlling the lead time and to diminish setup cost by a discrete investment. A premium cost is introduced and Stackelberg game policy is employed to obtain the analytical solution. Some numerical examples are given to validate the model. Sensitivity analysis and managerial insights are given to show the applicability of the model. Finally, the outcomes show that the model minimizes the optimum cost at the optimal values of the decision variables. It is found that the total cost is minimized when the multi-buyer is leader and vendor is follower.

scholarly journals Effects of Unequal Lot Size and Variable Transportation in Unreliable Supply Chain Management

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 357 ◽  
Author(s):  
Soumya Kanti Hota ◽  
Biswajit Sarkar ◽  
Santanu Kumar Ghosh
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Cost Reduction ◽  
Lead Time ◽  
Carbon Emission ◽  
Optimization Technique ◽  
Lot Size ◽  
Setup Cost ◽  
Chain Management ◽  
Special Cases

The effect of unreliable players on the supply chain management with a single-setup-multi-unequal-increasing-delivery-policy (SSMUID) along with a service-dependent demand and investment is discussed in this model. The manufacturer is unreliable which causes an increase of lead time and shortage. For solving the shortage problem and reducing lead time crashing cost (LTCC), an investment is utilized with the variable backorder price discounts. The number of transportation increases due to the new transportation policy and it causes pollution. Besides the fixed transportation and carbon emission cost (FTCEC), a container dependent carbon emission cost is applied. Some investments for setup cost reduction (SCR), ordering cost reduction (OCR), and quality improvement (QI) are considered. The lead time demand follows a normal distribution. The total cost of the supply chain is optimized and the model is tested numerically. The main intent of this study is to solve the shortage problem which occurs due to unreliability of the manufacturer. The study helps to reduce the unreliability issue of the manufacturer. The objective function is solved by using the classical optimization technique. Numerical results show that the discount for partial backorder enhances the profitability of the manufacturer. The sensitiveness of the parameters are discussed through the sensitivity of analysis and some special cases. Managerial insights provide the applicability of this study among different sectors.

A novel optimisation model in the collaborative supply chain with production time capacity consideration

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Ridwan Andi Purnomo ◽  
Adhe Rizky Anugerah ◽  
Salvia Fatma Aulia ◽  
Abdullah ‘Azzam
Keyword(s):  
Supply Chain ◽  
Low Cost ◽  
Transportation Cost ◽  
Production Costs ◽  
Furniture Industry ◽  
Production Time ◽  
Content Type ◽  
Total Cost ◽  
Penalty Cost ◽  
Optimisation Model

Purpose This study aims to propose an optimal procurement model of the collaborative supply chain in the furniture industry. The final output is the total cost minimisation to produce a furniture product that covers material cost, processing cost, transportation cost and holding cost. Therefore, if companies can give the best value to customers at a low cost, then competitive advantages can be achieved. Design/methodology/approach A genetic algorithm (GA) as a metaheuristic approach was used to solve problems in this research. The optimisation was initiated by developing a mathematical model to formulate the objective function. Findings Based on the case study, the proposed GA model was able to reduce the total cost of production. The cost was reduced by 73.09% compared to the existing system. Besides, the production time of the proposed model is within the capacity of both companies; hence, no penalty cost is imposed. Practical implications The proposed GA model has been implemented and tested to minimise production costs in the Indonesian furniture industry. Originality/value To the best of author knowledge, there is no research has proposed an optimisation model that incorporates production cost, transportation cost and production time capacity together in the collaborative supply chain. This research is the first to collaborate these factors using GA in the furniture industry.

scholarly journals Comparison of particular logistic models’ adoption in the Czech Republic

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Petra Vrbová ◽  
Václav Cempírek
Keyword(s):  
Supply Chain ◽  
Czech Republic ◽  
Lead Time ◽  
Logistic Model ◽  
Logistic Models ◽  
Service Level ◽  
Time Service ◽  
The Czech Republic ◽  
Total Cost ◽  
Challenging Tasks

Abstract Managing inventory is considered as one of the most challenging tasks facing supply chain managers and specialists. Decisions related to inventory locations along with level of inventory kept throughout the supply chain have a fundamental impact on the response time, service level, delivery lead-time and the total cost of the supply chain. The main objective of this paper is to identify and analyse the share of a particular logistic model adopted in the Czech Republic (Consignment stock, Buffer stock, Safety stock) and also compare their usage and adoption according to different industries. This paper also aims to specify possible reasons of particular logistic model preferences in comparison to the others. The analysis is based on quantitative survey held in the Czech Republic.

scholarly journals Coordinating Supply-Chain Management under Stochastic Fuzzy Environment and Lead-Time Reduction

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 480 ◽  
Author(s):  
Asif Iqbal Malik ◽  
Biswajit Sarkar
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Lead Time ◽  
Optimal Solution ◽  
Chain Model ◽  
Setup Cost ◽  
Chain Management ◽  
The Impact ◽  
Collaborative Supply Chain Management

In this paper, a supply-chain (SC) coordination method based on the lead-time crashing is proposed for a seller–buyer system. By considering different transportation modes, we control the lead-time (LT) variability. For the first time, we have attempted to determine the impact of the reliable and unreliable seller in a continuous-review supply-chain model under the stochastic environment. The authors discussed two reliability cases for the seller. First, we consider the seller is unreliable and in the second case, the seller is reliable. In addition, the demand during the lead time is stochastic with the known mean and variance. The proposed approach tries to find an optimal solution that performs well without a specific probability distribution. Besides, a discrete investment is made to reduce the setup cost, which will indirectly help supply-chain members to increase the total profit of the system. In the proposed model, the seller motivates the buyer by reducing lead time to take part in coordinating decision-making for the system’s profit optimization. We derive the coordination conditions for both members, the seller and the buyer, under which they are convinced to take part in the cooperative decision-making plan. Therefore, lead-time crashing is the proposed incentive mechanism for collaborative supply-chain management. We use a fixed-charge step function to calculate the lead-time crashing cost for slow and fast shipping mode. We give two numerical examples to validate the proposed models and demonstrate the service-level enhancement under the collaborative supply-chain management in case of an unreliable seller. Concluding remarks and future extensions are discussed at the end.

Enhanced Inventory Management Using Blockchain Technology Under Cloud Sector Enabled by Hybrid Multi-Verse with Whale Optimization Algorithm

2021 ◽  
pp. 1-38
Author(s):  
Chinnaraj Govindasamy ◽  
Arokiasamy Antonidoss
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Optimization Algorithm ◽  
Transportation Cost ◽  
The Other ◽  
Hybrid Optimization ◽  
Setup Cost ◽  
Blockchain Technology ◽  
Hybrid Optimization Algorithm ◽  
Whale Optimization

Inventory cost control is an essential factor in supply chain management. If the supplier’s inventory is insufficient, then the chance to trade the product will be reduced. The manufacturer’s inadequate material inventory will have an effect in termination of production, delays, and a waste of resources and time. On the other hand, postponed transportation will certainly raise costs such as transportation costs and cancellation of orders. Therefore, the operation costs of enterprises will be more, which will lower profits. In conventional supply chains, inventory costs control is not feasible for the view of the entire supply chain. The main intent of this paper is to plan for intelligent inventory management using blockchain technology under the cloud sector. The inventory management of the supply chain includes “multiple suppliers, a manufacturer, and multiple distributors”. The proposed inventory management models consider some significant costs like “transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, backordering cost, and quality improvement cost”. This multi-objective cost function is minimized by a novel hybrid optimization algorithm; the concept of WOA is integrated to produce the new algorithm which is termed as Whale-based Multi Verse Optimization (W-MVO) algorithm. For securing the data of distributors, using blockchain technology in a cloud environment helps from the leakage of data to other unauthorized users. Once the cost is reduced in all aspects based on the proposed hybrid optimization algorithm, the distributer will store the concerning data in the blockchain under the cloud sector, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The valuable performance analysis over the conventional optimization algorithms proves the effective and reliable performance of the proposed model over the conventional models.

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