Heuristic analyses of separate and bundling sales for complimentary products under consignment stock policy

2021 ◽  
pp. 107297
Author(s):  
M. Hemmati ◽  
S.M.J. Mirzapour Al-e-Hashem ◽  
S.M.T. Fatemi Ghomi
Keyword(s):  
2021 ◽  
Author(s):  
Alain Bensoussan ◽  
Suresh Sethi ◽  
Abdoulaye Thiam ◽  
Janos Turi

10.5772/56859 ◽  
2013 ◽  
Vol 5 ◽  
pp. 41 ◽  
Author(s):  
Maria Elena Nenni ◽  
Massimiliano M. Schiraldi

As a means of avoiding stock-outs, safety stocks play an important role in achieving customer satisfaction and retention. However, traditional safety stock theory is based on the assumption of the immediate delivery of the ordered products, which is not a common condition in business-to-business contexts. Virtual safety stock theory was conceived to raise the service level by exploiting the potential time interval in the order-to-delivery process. Nevertheless, its mathematical complexity prevented this technique from being widely adopted in the industrial world. In this paper, we present a simple method to test virtual safety stock effectiveness through simulation in an inventory system using a base stock policy with periodic reviews and backorders. This approach can be useful for researchers as well as practitioners who want to model the behaviour of an inventory system under uncertain conditions and verify the opportunity for setting up a virtual safety stock on top of, or instead of, the traditional physical safety stock.


2021 ◽  
Author(s):  
Ehab A. Bazan

A consignment stock is a type of supply-chain coordination for the management of supply-chains in which there is a joint vendor and buyer policy that is mainly focused on having the vendor manage the buyer's inventory. This thesis aims to investigate the consignment stock strategy in a single-vendor single-buyer supply-chain context considering imperfect items that may be produced from an imperfect production process. It develops a flexible mathematical model that allows for managerial decisions with regards to imperfect items and seeks to minimize costs (maximize profits) of the supply-chain. Such managerial decisions include scrapping items at a cost, selling them for a marginal profit to a secondary market, applying re-work, and/or applying minor setups to restore the production process. Results show that the introduction of imperfect items increases the batch size and reduces the number of shipments. Minor setups were shown to reduce cost, increase the number of shipments and reduce its size.


2020 ◽  
Vol 26 (3) ◽  
pp. 266-274
Author(s):  
Uttam Kumar Khedlekar ◽  
Priyanka Singh ◽  
Neelesh Gupta

This paper aims to develop a dynamic pricing policy for deteriorating items with price and stock dependent demand. In declining market demand of items decreases with respect to time and also after a duration items get outdated. In this situation it needs a pricing policy to sale the items before end season. The proposed dynamic pricing policy is applicable for a limited period to clease the stock. Policy decision regarding the selling price could aggressively attracts the costumers. Objectives are to maximize the prot/revenue, pricing strategy and economic order level for such a stock dependent and price sensitive items. We are giving numerical example and simulation to illustrate the proposed model.


Author(s):  
Tor Schoenmeyr ◽  
Stephen C. Graves

Problem definition: We use the guaranteed service (GS) framework to investigate how to coordinate a multiechelon supply chain when two self-interested parties control different parts of the supply chain. For purposes of supply chain planning, we assume that each stage in a supply chain operates with a local base-stock policy and can provide guaranteed service to its customers, as long as the customer demand falls within certain bounds. Academic/practical relevance: The GS framework for supply chain inventory optimization has been deployed successfully in multiple industrial contexts with centralized control. In this paper, we show how to apply this framework to achieve coordination in a decentralized setting in which two parties control different parts of the supply chain. Methodology: The primary methodology is the analysis of a multiechelon supply chain under the assumptions of the GS model. Results: We find that the GS framework is naturally well suited for this decentralized decision making, and we propose a specific contract structure that facilitates such relationships. This contract is incentive compatible and has several other desirable properties. Under assumptions of complete and incomplete information, a reasonable negotiation process should lead the parties to contract terms that coordinate the supply chain. The contract is simpler than contracts proposed for coordination in the stochastic service (SS) framework. We also highlight the role of markup on the holding costs and some of the difficulties that this might cause in coordinating a decentralized supply chain. Managerial implications: The value from the paper is to show that a simple contract coordinates the chain when both parties plan with a GS model and framework; hence, we provide more evidence for the utility of this model. Furthermore, the simple coordinating contract matches reasonably well with practice; we observe that the most common contract terms include a per-unit wholesale price (possibly with a minimum order quantity and/or quantity discounts), along with a service time from order placement until delivery or until ready to ship. We also observe that firms need to pay a higher price if they want better service. What may differ from practice is the contract provision of a demand bound; our contract specifies that the supplier will provide GS as long as the buyer’s order are within the agreed on demand bound. This provision is essential so that each party can apply the GS framework for planning their supply chain. Of course, contracts have many other provisions for handling exceptions. Nevertheless, our research provides some validation for the GS model and the contracting practices we observe in practice.


2017 ◽  
Vol 9 (1) ◽  
pp. 1-19
Author(s):  
Deepak Annasaheb Vidhate

This article gives a novel approach to cooperative decision-making algorithms by Joint Action learning for the retail shop application. Accordingly, this approach presents three retailer stores in the retail marketplace. Retailers can help to each other and can obtain profit from cooperation knowledge through learning their own strategies that just stand for their aims and benefit. The vendors are the knowledgeable agents to employ cooperative learning to train in the circumstances. Assuming a significant hypothesis on the vendor's stock policy, restock period, and arrival process of the consumers, the approach was formed as a Markov model. The proposed algorithms learn dynamic consumer performance. Moreover, the article illustrates the results of cooperative reinforcement learning algorithms by joint action learning of three shop agents for the period of one-year sale duration. Two approaches have been compared in the article, i.e. multi-agent Q Learning and joint action learning.


2013 ◽  
Vol 37 (6) ◽  
pp. 4464-4473 ◽  
Author(s):  
N. Anbazhagan ◽  
Jinting Wang ◽  
D. Gomathi

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