Pricing and production decisions in dual-channel supply chains with demand disruptions

This paper develops a two-period pricing and production decision model in a one- manufacturer-one-retailer dual-channel supply chain that experiences a disruption in demand during the planning horizon. While disruption management has long been a key research issue in supply chain management, little attention has been given to disruption management in a dual-channel supply chain once the original production plan has been made. Generally, changes to the original production plan induced by a disruption may impose considerable deviation costs throughout the supply chain system. In this paper, we examine how to adjust the prices and the production plan so that the potential maximal profit is obtained under a disruption scenario. We first study the scenario where the manufacturer and the retailer are vertically integrated with demand disruptions. Then we further assume that the manufacturer bears the deviation costs and obtain the manufacturer’s and the retailer’s individual optimal pricing decision, as well as the manufacturer’s optimal production quantity in a decentralized decision-making setting. We derive conditions under which the maximum profit can be achieved. The results indicate that the optimal production quantity has some robustness under a demand disruption, in both centralized and decentralized dual-channel supply chains. We also find that the optimal pricing decisions are affected by customers’ preference for the direct channel and the market scale change, in both centralized and decentralized dual-channel supply chains.     

Supporting agile supply chains using a service-oriented shop floor

The globalized nature of current business environments led to the emergence of new networked enterprise organizational paradigms (supply chains, extended enterprises, virtual enterprises, collaborative networks, etc.) to meet changing requirements and tackle profitable but volatile opportunities overall agility is required.Eventually the shop floor will have to react and accommodate (re)adjustments in the supply chain making it an important piece in the competitiveness puzzle. So far, the research focus has been in high level aspects of supply chain management and the integration of shop floor activities in the process has been left relatively unattended.However, shop floor data is increasingly required in business tools that support decision making. In this context, failing to support agility at shop floor level can compromise the agility of the supply chain.Recent developments in networked information technologies and embedded devices allow enabling intelligence in shop floor rendering it an active and live entity that further enhances the dynamics of the supply chain.The goal of the present work, supported by an implemented test case in the assembly domain, is to demonstrate how one is able to seamless integrate the shop floor with external tools and achieve a highly reconfigurable environment that adapts to changing production requirements and disturbances using service-oriented technology.     

Electronic commerce front-end in apparel supply chain

Information technology has been used increasingly to enhance the global competitiveness in various industries through the widespread application of cost effective electronic commerce. Many Internet-based systems have been designed and developed for supply chain management in many areas, such as publishing, computer, and garment industries. Most of them mainly center on information communication infrastructure, coordination between production and distribution, and purchasing functions with security mechanism. In this paper, we focus on the most common modules in electronic commerce front-end to conjoin various business organizations and to strengthen business competence, namely (1) an efficient web page and catalogue generator; (2) a hybrid search engine specifically for product, merchandise, equipment, and other enabling technologies; and (3) a set of tools to monitor usage patterns to further improve the web page organization. We also address the issues on implementation, examples and future directions.     

Price and leadtime competition,and coordination for make-to-order supply chains

This paper develops a game theoretic model of a one-manufacturer and one-retailer supply chain facing an outside integrated chain (manufacturer) to study the price and leadtime competition and investigate coordination of the supply chain, where the make-to-order production mode is employed and consumers are sensitive to retail price and leadtime. We find that decentralization of the supply chain increases its leadtime while decreases the rival’s leadtime; and the decentralization increases the retail prices. The existence of the outside competitor raises the leadtime. A higher reservation price or brand differentiation increases the retail prices but decreases the leadtimes; a higher transportation cost or lower leadtime sensitivity increases the retail prices and the leadtimes. The coordination of the supply chain facing integrated rival harms the integrated rival. We design contracts to coordinate the supply chain under leadtime-decision-first scenario and wholesale-price-decision-first scenario, respectively. Further, we find that the sequence of decisions affects the validity of the all-unit quantity discount scheme in coordinating the supply chain.     

VMI for single-vendor multi-retailer supply chains under stochastic demand

This paper discusses how a vendor and multiple retailers interact in a vendor managed inventory (VMI) system under stochastic demand. It is assumed that the vendor replenishes all the retailers at the same time. The vendor replenishment cycle is taken to be an integer multiple of the retailer replenishment cycle. In case of a shortage at the vendor, the available stock is allocated to the retailers on the basis of equal stock out probability. Approximate expressions for minimizing the expected total cost for the VMI system have been developed. Various levers affecting the performance of the system have been analyzed. The validity of the approximate model has been tested through simulation.     

The impact of reputation on supply chains. An analysis of permanent and discounted reputation

In long-term recurring contractual relationships, which are common in the B2B-arena, reputation and trust play a crucial role. This analysis investigates the joint impact of reputation and price-based ranking of suppliers on the material flow in the supply chain. Positive reputation proves to be a key factor in reaching dominating market positions, which illustrates the importance of building brand awareness in all stages of a supply chain. Through our simulation, it will be observed that the ranking of suppliers by reputation-based choice has a stabilizing effect on the material flow in the supply chain. A strong reputation component in the individual choice stimulates the formation of monopolies, while the discount of reputation imposes a countertendency on this effect. The Bullwhip Effect, another phenomenon that carries a countertendency to the reputation-based monopoly effect, is observed to be even stronger for members of tiers with a high fluctuation of order rates.     

Contract analysis: A performance measures and profit evaluation within two-echelon supply chains

Coordination is regarded as key in managing dependencies between distinctive members of a supply chain through the benefits of coordination mechanisms. Such coordination mechanisms are contracts, implemented to increase total supply chain profit, reduce costs and share risk among supply chain members. However, by contract implementation the retailer is constrained in his purchase by bearing the entire risk of holding the inventory (wholesale price contract) or by limited risk allocated to the supplier (buyback, revenue sharing and quantity flexibility contracts). By implementing an advanced purchase system the risk of inventory is fairly divided between the supplier and the retailer. In order to observe inventory implications on the supply chain bottom line, this article is directed towards the evaluation of performance measures and supply chain profit behavior under buyback, revenue sharing, quantity flexibility and advanced purchase discount contracts versus no coordination and wholesale price systems.     

Price competition in duopoly supply chains with stochastic demand

In the literature, substantial researches have been carried out on supply chain coordination. The majority of these studies suggest a mechanism that enforces the supply chain members to follow the strategies that produce the equilibrium of an integrated supply chain. Moreover, most of researches do not consider the competition among supply chains.In this study, we consider an industry consisting of two distinct supply chains which compete with each other over price. Three algorithms are presented to calculate the equilibrium of three possible industry structures. It is assumed that demand is stochastic with additive form whose random component has a uniform distribution. Furthermore, the effect of competition and demand uncertainty intensity on the Nash equilibrium of the structures and supply chains’ profits are discussed in a numerical example.     

A best-matching protocol for order fulfillment in re-configurable supply networks

Order fulfillment is a process which encompasses all the activities from the inquiry of goods by the customer to the final delivery of goods to the customer. The most important activity of the order fulfillment process is the selection of the order fulfilling agent in the supply network. The selection of the agent involves multiple criteria based on quantitative and qualitative metrics and requires several self-interested agents and organizations to dynamically form and configure supply chain. This article describes a methodology for selection of an order fulfillment agent in a collaborative, geographically distributed network by developing a Best Matching Protocol (BMP). The BMP developed, enables better matching of fulfillment agents with customers in a given supply network, by determining which agent best satisfies the pre-defined quality and cost requirements of the customer. The protocol enables collaboration between the agents of the Supply Network (SN) and provides a scalable solution for the increasing size of the SN.     

Multi-tag and multi-owner RFID ownership transfer in supply chains

In any supply chain, there is a high likelihood for individual objects to change ownership at least once in their lifetime. As RFID tags enter the supply chain, these RFID-tagged objects should ideally be able to seamlessly accommodate ownership transfer issues while also accomplishing their primary intended purpose. Physical ownership transfer does not translate to strict ownership transfer in the presence of RFID tags given the wireless nature of communication with these tags. Moreover, whereas existing protocols implicitly assume a single tag that is owned by a single entity, it is not uncommon to encounter scenarios where tag ownership is shared among multiple entities. A dual of this is the case of an object with multiple tags. We consider ownership transfer scenarios for shared ownership transfer and single object with multiple RFID tags. In the multiple-tagged object case, we consider the possibility where objects gain and lose tags over time. We also present a protocol for simultaneous transfer of ownership of multiple tags between owners. Since ownership transfer without a trusted third party (TTP) is difficult to achieve, we propose a shared ownership sharing protocol and evaluate its properties.     

Co-op advertising and pricing models in manufacturer–retailer supply chains

Cooperative (co-op) advertising plays a significant role in marketing programs in conventional supply chains and makes up the majority of promotional budgets in many product lines for both manufacturers and retailers. Nevertheless, most studies to date on co-op advertising have only assumed that the market demand is only influenced by the advertising level but not in any way by the retail price. That is why our work is concerned with co-op advertising and pricing strategies in distribution channels consisting of a manufacturer and a retailer. Four different models are discussed which are based on three non-cooperative games (i.e., Nash, Stackelberg retailer and Stackelberg manufacturer) and one cooperative game. We identify optimal co-op advertising and pricing strategies for both firms mostly analytically but we have to resort to numerical simulations in one case. Comparisons are then made about various outcomes, especially the profits, for all cases. This leads to consider more specifically the cooperation case in which profits are the highest for both the retailer and the manufacturer, and how they should share the extra joint profit achieved by moving to cooperation. We solve this bargain problem using the Nash bargaining model.     

An improved demand forecasting method to reduce bullwhip effect in supply chains

Accurate forecasting of demand under uncertain environment is one of the vital tasks for improving supply chain activities because order amplification or bullwhip effect (BWE) and net stock amplification (NSAmp) are directly related to the way the demand is forecasted. Improper demand forecasting results in increase in total supply chain cost including shortage cost and backorder cost. However, these issues can be resolved to some extent through a proper demand forecasting mechanism. In this study, an integrated approach of Discrete wavelet transforms (DWT) analysis and artificial neural network (ANN) denoted as DWT-ANN is proposed for demand forecasting. Initially, the proposed model is tested and validated by conducting a comparative study between Autoregressive Integrated Moving Average (ARIMA) and proposed DWT-ANN model using a data set from open literature. Further, the model is tested with demand data collected from three different manufacturing firms. The analysis indicates that the mean square error (MSE) of DWT-ANN is comparatively less than that of the ARIMA model. A better forecasting model generally results in reduction of BWE. Therefore, BWE and NSAmp values are estimated using a base-stock inventory control policy for both DWT-ANN and ARIMA models. It is observed that these parameters are comparatively less in case of DWT-ANN model.     

Manufacturing production plan optimization in three-stage supply chains under Bass model market effects

The Bass model is a very successful parametric approach to forecast the diffusion process of new products. In recent years, applications of the Bass model have been extended to other operational research fields such as managing customer demands, controlling inventory levels, optimizing advertisement strategies, and so forth. This study attempts to establish an application for optimizing manufacturers’ production plans in a three-stage supply chain under the Bass model’s effects on the market. The supply chain structure considered in this research is similar to other common supply chains comprised of three stages, namely retailer, distributor and manufacturer. The retailer stage has to handle customer demands following the Bass diffusion process. Market parameters and essential information are assumed to be available and ready for access. Each stage is expected to determine its inventory policy rationally. That is, each stage will attempt to maximize its own profits. These decisions will back-propagate their effects to upper stages. This study adopts a dynamic programming approach to determine the inventory policies of each stage so as to optimize manufacturers’ production plans.     

面向电子商务的供应链管理系统的研究

介绍了电子商务与供应链的概念,然后结合两者的特点,提出了一种面向电子商务的供应链管理系统,最后以企业为背景,提出了系统的应用。     

Multiscale production routing in multicommodity supply chains with complex production facilities

In this work, we introduce the multiscale production routing problem (MPRP), which considers the coordination of production, inventory, distribution, and routing decisions in multicommodity supply chains with complex continuous production facilities. We propose an MILP model involving two different time grids. While a detailed mode-based production scheduling model captures all critical operational constraints on the fine time grid, vehicle routing is considered in each time period of the coarse time grid. In order to solve large instances of the MPRP, we propose an iterative MILP-based heuristic approach that solves the MILP model with a restricted set of candidate routes at each iteration and dynamically updates the set of candidate routes for the next iteration. The results of an extensive computational study show that the proposed algorithm finds high-quality solutions in reasonable computation times, and in large instances, it significantly outperforms a standard two-phase heuristic approach and a solution strategy involving a one-time heuristic pre-generation of candidate routes. Similar results are achieved in an industrial case study, which considers a real-world industrial gas supply chain.     

An intelligent adaptive system for the optimal variable selections of R&D and quality supply chains

The cost of research & development (R&D) and quality management are always regarded as two major parts of total cost. The variable performance of R&D and quality design is an important index that will reflect the effectiveness of the cost reduction. This research has attempted to simultaneously vary all of the variables to achieve the global optimum for the optimal variable selections of R&D and quality design. Genetic algorithm (GA) can treat all of the variables for the global search. In this study, fuzzy refinement with orthogonal arrays was effective in improving the performance of the GA, and also showed the benefits of a good chromosome structure on the behavior of GA. It is also proposed the postponement design with temporal concept, to select the effective variables for the cost reduction of R&D and quality management design. The experimental results showed that tempo-postponement design will increase the flexibility and quick response for supply chain management. Hence, this approach can act as a useful guideline for researchers working on the optimization of the key variable selections for R&D and quality model design.     

System for supply network management

Strategies and algorithms for operational planning and control are quite important to successful operations of a supply network. Implementation of a strategy requires substantial information system support, but few detailed designs of such systems have appeared in the literature. We designed an information system for a centralized management concept that could handle any type of strategy or algorithm. We developed algorithms for two different strategies deemed important by researchers and practitioners, push and pull, and implemented them in system modules. This information system performed well in simulations of the management of operations of an example six-stage supply network. This system provides an example for operational development as well as a platform for laboratory experiments.     

Multi-behavior agent model for planning in supply chains: An application to the lumber industry

Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for planning systems to present planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, agent-based technology takes advantage of the ability of agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, planning agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior planning agent model using different planning strategies when decisions are supported by a distributed planning system. The implementation of this solution is realized through the FOR@C experimental agent-based platform, dedicated to supply chain planning for the lumber industry.     

A review of supply chain complexity drivers

Studies on supply chain complexity mainly use the static and dynamic complexity distinction. While static complexity describes the structure of the supply chain, the number and the variety of its components and strengths of interactions between these; the dynamic complexity represents the uncertainty in the supply chain and involves the aspects of time and randomness. This distinction is also valid when classifying the drivers of supply chain complexity according to the way they are generated. Supply chain complexity drivers (e.g., number/variety of suppliers, number/variety of customers, number/variety of interactions, conflicting policies, demand amplification, differing/conflicting/non-synchronized decisions and actions, incompatible IT systems) play a significant and varying role in dealing with complexity of the different types of supply chains (e.g., food, chemical, electronics, automotive).     

Business analytics in supply chains - The contingent effect of business process maturity

The paper analyzes the effect of the use of business analytics on supply chain performance. It investigates the changing information processing needs at different supply chain process maturity levels. The effects of analytics in each Supply Chain Operations Reference areas (Plan, Source, Make and Deliver) are analyzed with various statistical techniques. A worldwide sample of 788 companies from different industries is used. The results indicate the changing impact of business analytics use on performance, meaning that companies on different maturity levels should focus on different areas. The theoretical and practical implications of these findings are thoroughly discussed.