A dynamic inventory model with supplier selection in a serial supply chain structure

Considering the inherent connection between supplier selection and inventory management in supply chain networks, this article presents a multi-period inventory lot-sizing model for a single product in a serial supply chain, where raw materials are purchased from multiple suppliers at the first stage and external demand occurs at the last stage. The demand is known and may change from period to period. The stages of this production–distribution serial structure correspond to inventory locations. The first two stages stand for storage areas for raw materials and finished products in a manufacturing facility, and the remaining stages symbolize distribution centers or warehouses that take the product closer to customers. The problem is modeled as a time-expanded transshipment network, which is defined by the nodes and arcs that can be reached by feasible material flows. A mixed integer nonlinear programming model is developed to determine an optimal inventory policy that coordinates the transfer of materials between consecutive stages of the supply chain from period to period while properly placing purchasing orders to selected suppliers and satisfying customer demand on time. The proposed model minimizes the total variable cost, including purchasing, production, inventory, and transportation costs. The model can be linearized for certain types of cost structures. In addition, two continuous and concave approximations of the transportation cost function are provided to simplify the model and reduce its computational time.     

Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions

New product development involves several critical decisions. A key decision making area in new product development is the evaluation of the viability and the market potentials of a new product. In the absence of any relevant historical data, companies ask the potential buyers of their products about their intentions to buy those products when assessing their viability. Despite the popularity of the use of behavioral intentions in predicting the market acceptance of new product ideas, both survey and empirical studies suggest that the accuracy of such predictions is usually very low. Although earlier case-based studies suggest that a number of factors can affect the quality of new product decisions, it is still empirically unclear how product knowledge and the type of new products might impact the predictive accuracy of intentions-based new product forecasting. This study utilized a longitudinal research design and empirically tested the hypotheses across two new products. The study first collected purchase intentions data about the new products. Second, it collected subsequent actual purchase data about the new products. The results of series of hierarchical regression analyses comparing the initial purchase intentions and subsequent actual behaviors showed that while product knowledge is positively related to the predictive accuracy and consistency of intentions-based new product forecasting, product type is negatively related to them.     

A possibilistic decision model for new product supply chain design

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a possibilistic SC configuration model for new products with unreliable or unavailable SC statistical data. The supply chain is modeled as a network of stages. Each stage may have one or more options characterized by the cost and lead-time required to fulfill required functions and may hold safety stock to prevent an inventory shortage. The objective is to determine the option and inventory policy for each stage to minimize the total SC cost and maximize the possibility of fulfilling the target service level. A fuzzy SC model is developed to evaluate the performance of the entire SC and a genetic algorithm approach is applied to determine near-optimal solutions. The results obtained show that the proposed approach allows decision makers to perform trade-off analysis among customer service levels, product cost, and inventory investment depending on their risk attitude. It also provides an alternative tool to evaluate and improve SC configuration decisions in an uncertain SC environment.     

An agent-based investigation into the new product development capability

The capability to bring products to market which comply with quality, cost and development time goals is vital to the survival of firms in a competitve environment. New product development comprises knowledge creation and search and can be organized in different ways. In this paper, we study the performance of several alternative organizational models for new product development using a model of distributed, self-adapting (learning) agents. The agents (a marketing and a production agent) are modelled via neural networks. The artificial new product development process analyzed starts with learning on the basis of an initial set of production and marketing data about possible products and their evaluation. Subsequently, in each step of the process, the agents search for a better product with their current models of the environment and, then, refine their representations based on additional prototypes generated (new learning data). Within this framework, we investigate the influence of different types of new product search methods and generating prototypes/learning according to the performance of individual agents and the organization as a whole. In particular, sequential, team-based Trial & Error and House of Quality guided search are combined with prototype sampling methods of different intensity and breadth; also, the complexity of the agents (number of hidden units) is varied. It turns out that both the knowledge base and the search procedure have a significant impact on the agents' generalization ability and success in new product development. Andreas Mild was born in Vienna, Austria, in 1973. He studied business administration in Vienna, in 2000 he received his Ph.D. from the Vienna University of Economics and Business Administration (WU). Since 2003 he is associated professor at the WU. He has been guest professor in Frankfurt, Germany, Sydney, Australia and Bangkok, Thailand. Previous research appeared in Journals such as MIS Quarterly, Management Science and Marketing Science. His research interests currently include agent-based models, new product development and recommender systems. Alfred Taudes was born in Vienna, Austria, in 1959. He studied business administration and management information systems (MIS) in Vienna (doctorate 1984), in 1991 he received his Ph.D. from the Vienna University of Economics and Business Administration (WU). He was assistant professor at the WU (1986–1991) and professor for MIS at the German Universities of Augsburg (1991), Münster (1991/92) and Essen (1992/93). Since 1993, he has been professor for MIS at the WU and Head of the Department for Production Management. Since 2000, Dr. Taudes has been speaker for the Special Research Area SFB # 010 (Adaptive Information Systems and Modelling in Economics and Management Science). His research interests currently include agent-based models of industry structures, management of innovation, technology management and business strategy.     

Alternative supply chain production-sales policies for new product diffusion: An agent-based modeling and simulation approach

Applying agent-based modeling and simulation (ABMS) methodology, this paper analyzes the impact of alternative production-sales policies on the diffusion of a new generic product and the generated NPV of profit. The key features of the ABMS model, that captures the marketplace as a complex adaptive system, are: (i) supply chain capacity is constrained; (ii) consumers’ new product adoption decisions are influenced by marketing activities as well as positive and negative word-of-mouth (WOM) between consumers; (iii) interactions among consumers taking place in the context of their social network are captured at the individual level; and (iv) the new product adoption process is adaptive. Conducting over 1 million simulation experiments, we determined the “best” production-sales policies under various parameter combinations based on the NPV of profit generated over the diffusion process. The key findings are as follows: (1) on average, the build-up policy with delayed marketing is the preferred policy in the case of only positive WOM as well as the case of positive and negative WOM. This policy provides the highest expected NPV of profit on average and it also performs very smoothly with respect to changes in build-up periods. (2) It is critical to consider the significant impact of negative word-of-mouth in choosing production-sales policies. Neglecting the effect of negative word-of-mouth can lead to poor policy recommendations, incorrect conclusions concerning the impact of operational parameters on the policy choice, and suboptimal choice of build-up periods.     

A representation model for the solving-time distribution of a set of design tasks in new product development (NPD)

Given the potential risks of new product development projects (NPD), the characteristics of the design tasks solving-time distributions are critical for their effective management. In OR we need to find what operational characteristics of design tasks may delay projects. Other researchers already identified the technological novelty, the magnitude of the design tasks, the interactions between design tasks in an NPD project, and the balancing between projects among the most important causes of the unpredictability of the design tasks lead times in NPD projects.     

Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran

In this paper, dynamic dairy facility location and supply chain planning are studied through minimizing the costs of facility location, traffic congestion and transportation of raw/processed milk and dairy products under demand uncertainty. The proposed model dynamically incorporates possible changes in transportation network, facility investment costs, monetary value of time and changes in production process. In addition, the time variation and the demand uncertainty for dairy products in each period of the planning horizon is taken into account to determine the optimal facility location and the optimal production volumes. Computational results are presented for the model on a number of test problems. Also, an empirical case study is conducted in order to investigate the dynamic effects of traffic congestion and demand uncertainty on facility location design and total system costs.     

新产品供应链下考虑顾客体验效应的制造商渠道返利策略研究

随着体验经济的到来和新产品市场的竞争, 如何针对顾客体验进行渠道建设和品牌推广就显得尤为重要。本文针对新产品供应链的环境, 考虑顾客的体验效应及零售商对此所做的体验投入, 利用效用理论构建了线上和线下消费者的需求函数, 在此基础上分别探讨了批发价格模式和渠道返利模式制造商占优的供应链的决策行为, 分析了供应链各决策主体均衡解的特征, 并对渠道返利前后供应链节点企业的最优决策进行了比较, 最后用数值例子分析了制造商的返利点、零售商的体验投入水平、顾客的体验效应、旅行成本以及购买意愿等对供应链最优绩效的影响。     

新产品供应链下考虑顾客体验效应的制造商渠道返利策略研究

随着体验经济的到来和新产品市场的竞争, 如何针对顾客体验进行渠道建设和品牌推广就显得尤为重要。本文针对新产品供应链的环境, 考虑顾客的体验效应及零售商对此所做的体验投入, 利用效用理论构建了线上和线下消费者的需求函数, 在此基础上分别探讨了批发价格模式和渠道返利模式制造商占优的供应链的决策行为, 分析了供应链各决策主体均衡解的特征, 并对渠道返利前后供应链节点企业的最优决策进行了比较, 最后用数值例子分析了制造商的返利点、零售商的体验投入水平、顾客的体验效应、旅行成本以及购买意愿等对供应链最优绩效的影响。     

Improving the accuracy of expert predictions of the future success of new internet services

One of the challenging issues in new product development is the evaluation of the future success of new product initiatives and the elimination of those with unpromising futures. The difficulty of assessing the future success of a new offering is more highlighted for new services than is for new products because of the unique characteristics of services. Earlier studies suggested that a number of factors can affect the quality of evaluating the future success of new products or services. Building on previous studies, this paper offers the first empirical tests of the roles of two important factors (task structuring and expertise sharing) in improving the quality of expert predictions of the future success of new Internet services. It presents an empirical study based on a longitudinal survey that involves both the initial predictions and the actual results for two Internet services to test related hypotheses. The results showed that these factors can significantly improve the predictive accuracy of experts in evaluating new Internet services. More importantly, the results also indicated a significant substitution effect between the factors, suggesting that either of these factors is sufficient to make major improvements in the accuracy of the evaluations. Because the factors can largely substitute for each other; however, using both can lead to only minor improvements over and above using just one.     

Robust optimal dynamic production/pricing policies in a closed-loop system

Hybrid manufacturing/remanufacturing systems play a key role in implementing closed-loop production systems which have been considered due to increasingly environmental concerns and latent profit of used products. Manufacturing and remanufacturing rates, selling price of new products, and acquisition price of used products are the most critical variables to optimize in such hybrid systems. In this paper, we develop a dynamic production/pricing problem, in which decisions should be made in each period confronting with uncertain demand and return. The manufacturer is able to control the demand and return by adjusting selling price and acquisition price respectively, also she can stock inventories of used and new products to deal with uncertainties. Modeling a nominal profit maximization problem, we go through robust optimization approach to reformulate it for the uncertain case. Final robust optimization model is obtained as a quadratic programming model over discrete periods which can be solved by optimization packages of QP. A numerical example is defined and sensitivity analysis is performed on both basic parameters and parameters associated with uncertainty to create managerial views.     

A two-period game of a closed-loop supply chain

We consider a two-period closed-loop supply chain (CLSC) game where a remanufacturer appropriates of the returns’ residual value and decides whether to exclusively manage the end-of-use product collection or to outsource it to either a retailer or a third-service provider (3P). We determine that the manufacturer outsources the product collection only when an outsourcee performs environmentally and operationally better. On the outsourcees side there is always an economic convenience in managing the product returns process exclusively, independently of returns rewards and operational performance. When outsourcing is convenient, a manufacturer always chooses a retailer if the outsourcees show equal performance. Overall, the manufacturer is more sensitive to environmental performance than to operational perfomance. Finally, there exists only a small region inside which outsouring the collection process contributes to the triple bottom line.     

Organizing product releases in time-driven development processes: a probabilistic analysis

In this paper, the problem is addressed of how to organizea new product development process that is fast on the one handand that provides good-quality results on the other hand. Severalplanning techniques, like PERT and CPM, are available to analysethe completion time or product release time of such complexprocesses. Although these techniques are all well known andare widely used in project management, they do not address theiterative mode of operation that is characteristic for suchuncertain processes. Neither do they offer a tool or guidelinesto ‘design’ a composition of iterative processesbecause they are analytical. In this paper, a quantitative concept is presented for modellingthe release time of a single uncertain iterative activity, asa random variable to deal with the probabilistic aspect in asimple way. From this simple model, the complexity is extendedsystematically to model fundamentally different configurations,that are on the one hand simple enough to be studied analyticallyand on the other hand exhibit their fundamentally differentrelease characteristics, as experienced in real life. From theanalysis, guidelines are formulated for organizing or (re)configuringa complex process configuration. It is demonstrated that organizing uncertain processes fora fast product release requires a balance between the exploitationof the principles of concurrent engineering and the risk ofoverrunning time targets. An important factor in this balanceis the decision structure for the release of intermediate results.The ‘empowerment’ structure, a structure where decisionsare not clustered as milestones but are made without any delay,offers great opportunity in terms of small mean release timesand small variances thereof.     

Complexity analysis of dynamic noncooperative game models for closed-loop supply chain with product recovery

In this paper, we consider a closed-loop supply chain (CLSC) with product recovery, which is composed of one manufacturer and one retailer. The retailer is in charge of recollecting and the manufacturer is responsible for product recovery. The system can be regarded as a coupling dynamics of the forward and reverse supply chain. Under different decision criteria, two noncooperative game models: Stackelberg game model and peer-to-peer game model are developed. The dynamic phenomena, such as the bifurcation, chaos and sensitivity to initial values are analyzed through bifurcation diagrams and the largest Lyapunov exponent (LLE). The influences of decision parameters on the complex nonlinear dynamics behaviors of the two models are further analyzed by comparing parameter basin plots, and the results show that with the improvement of retailer’s competitive position, the CLSC system will be more easier to enter into chaos.     

A Dynamic Supplier Selection and Inventory Management Model for a Serial Supply Chain with a Novel Supplier Price Break Scheme and Flexible Time Periods

A new supplier price break and discount scheme taking into account order frequency and lead time is introduced and incorporated into an integrated inventory planning model for a serial supply chain that minimizes the overall incurred cost including procurement, inventory holding, production, and transportation. A mixed-integer linear programming (MILP) formulation is presented addressing this multi-period, multi-supplier, and multi-stage problem with predetermined time-varying demand for the case of a single product. Then, the length of the time period is considered as a variable. A new MILP formulation is derived when each period of the model is split into multiple sub-periods, and under certain conditions, it is proved that the optimal solution and objective value of the original model form a feasible solution and an upper bound for the derived model. In a numerical example, three scenarios of the derived model are solved where the number of sub-period is set to 2, 3, and 4. The results further show the decrease of the optimal objective value as the length of the time period is shortened. Sufficient evidence demonstrates that the length of the time period has a significant influence on supplier selection, lot sizing allocation, and inventory planning decisions. This poses the necessity of the selection of appropriate length of a time period, considering the trade-off between model complexity and cost savings.     

Revenue management for a supply chain with two streams of customers

In this paper, we consider revenue management for a service supply chain with one supplier and one retailer. The supplier has a limited capacity of a perishable product and both the supplier and the retailer face customers. Each customer may choose to buy a product from either the supplier or the retailer by considering prices and the cost associated with switching. For the centralized model, the supplier determines the selling prices for both herself and the retailer, and the retailer simply collects a commission fee for each product sold. We derive monotone properties for the revenue functions and pricing strategies. Further, we show that the commission fee increases the retailer’s price while decreasing the supplier’s and leads to efficiency loss of the chain. For the decentralized decision-making model, the supplier and the retailer compete in price over time. Two models are considered. In the first, the retailer buys products from the supplier before the selling season and in the second the retailer shares products with the supplier in retailing. For both models, we discuss the existence of the equilibrium and characterize the optimal decisions. Numerical results are presented to illustrate properties of the models and to compare the supply chain performance between the centralized and the decentralized models.     

Intuitionistic fuzzy cognitive map approach for the evaluation of supply chain configuration criteria

Because of the growing global competence and effectiveness concepts, supply chain becomes more important for organizations. Therefore, managers object to find best supply chain configuration for their firms. This study proposes a comprehensive configuration for supply chain management process, and it enables to understand relationships among supply chain integration, supply chain strategies, supply chain risk factors, and performance criteria. By reviewing the literature and using experts' knowledge, supply chain configuration criteria are determined. Intuitionistic fuzzy cognitive map methodology is employed to consider the interrelations between criteria. Intuitionistic fuzzy cognitive map methodology is a suitable tool due to the presence of causalities and relationships among criteria and the difficulty of expressing the interrelations with crisp numbers. It also deals with uncertain and vague data and allows representing hesitation. The application is conducted in an automobile factory, which is one of the largest manufacturers in Turkey. The results show that selection of proper supplier is the most significant supply chain configuration criteria. Thus, the importance of supplier selection criteria is also analyzed as the second phase of the study.     

机器带不可用时间限制的简单线性恶化供应链排序问题

研究的单机供应链排序问题中, 机器有一个不可用时间限制, 工件的加工时间与恶化率及其开工时间有关, 且工件的加工不可恢复. 一个或多个完工工件可组成一个发送批由车辆发送给客户, 且在机器不可用时间限制之前完工的工件必须在限制开始之时或之前完成发送. 问题的目标是最小化总发送时间与总发送费用之和. 证明问题是NP-难的, 提出了伪多项式时间的动态规划算法. 进一步, 在确定问题目标函数值的上界及下界之后, 设计了一个完全多项式时间近似方案(FPTAS).     

考虑合格率的单周期产品供应链决策研究

本文研究由一个供应商和一个经销商组成的关于单周期产品的供应链系统。考虑产品具有随机合格率,并且合格率的分布是供应商的私人信息。在分布函数未知情况下比较研究了供应链三种竞争协调策略:供应商具有合格率私人信息,同时给予经销商价格折扣;供应商告诉经销商合格率分布但是不提供价格折扣;供应商管理经销商库存。比较分析和实例研究说明了三种策略对供应链整体绩效具有明显的优劣和实现帕累托优化的条件。     

A model of the executive's decision processes in new product development

This paper introduces the concept of fuzziness to deal quantitatively with the imprecision of the meaning of the executive's judgment stated in a natural language and presents a model of the executive's decision processes for the new product introduction which contain fuzzy-2 states, fuzzy-2 information systems, fuzzy-2 information signals, fuzzy-2 strategy are presented. The committee decision problem under fuzzy-2 constraints is dealt with.