Behavioral analyses of retailers’ ordering decisions

The main objective I pursue in this thesis is to better understand how different factors may independently and in combination influence retailers' ordering decisions under different supply chain structures (single agent and multi agent), different demand uncertainty (deterministic and stochastic), and different interaction among retailers (no interaction, competition and cooperation). I developed three different studies where I build on different formal management model and then run multiple behavioral studies to better understand subjects’ behavior. The first study analyzes order amplification in a single-supplier single-retailer supply chain. I used a behavioral experiment to test retailers’ orders under different ordering delays and different times to build supplier’s capacity. Results provide (i) a better understanding of the endogenous dynamics leading to retailers’ ordering amplification, and (ii) a description of subjects’ biases and deviation from optimal trajectories; despite subjects have full information about the system structure. The second study analyzes how order amplification can also take place when there is fierce retailer competition and limited supplier capacity. I study how different factors (different time to build supplier capacity, different levels of competition among retailers, different magnitudes of supply shortage and different allocation mechanisms) may independently and in combination influence retailers’ order in a system with two retailers under supply competition. Results show that (i) the bullwhip effect persists even when subjects do not have incentives to deviate, (ii) subjects amplify their orders in an attempt to build an unnecessary safety stock to respond to potential deviations from the other retailers, and (iii) retailers’ underperformance varies with the allocation mechanism used by the supplier. In the last study, I analyze retailers’ orders in a system where there is uncertainty in the final customer demand. I experimentally explore the effect of transshipments among retailers in a single-supplier multi-retailer supply chain. Specifically, I explore retailers’ orders under different profit and communication conditions. In addition, I integrate analytical and behavioral models to improve supply chain performance. Results show that (i) the persistence of common biases in a newsvendor problem (pull-to-center, demand chasing, loss aversion, psychological disutility), (ii) communication could improve coordination and may reduce demand chasing behavior, (iii) supply chain performance increases with the use of behavioral strategies embedded within a traditional optimization model, and (iv) dynamic heuristics improve overall coordination, outperforming a simple Nash Equilibrium strategy

A Sustainable Supply Chain Model of Relationship Between Wood Supplier and Furniture Industry in Indonesia

Wooden furniture industry is an important industry sector in Indonesia, because many people\u27s welfare relyon this industry sector and the industry has a big social and environmental impacts. Many wooden furnitureindustries in Indonesia, especially in Central Java Province face problems related to the sustainability. The relationbetween wood suppliers and furniture industry is studied in this paper. A sustainable supply chain management (s-SCM) model is proposed as an approach for solutions for the problems. The approach is chosen due to the characteristics of the problems that related to economic, social, and environmental problems. This aim of this paper is to determine how much supply teak wood must be provided by PP to satisfy furniture industry demand, how much production capacity that must be increased and how large forest area that must be planted in order to achieve environmental and social goals without sacrificing economical goals much. Goal programming (GP) is chosen for solving the problems, because the goals are to maximize the total benefit,minimize the total loss and anticipate the conflicts between goals. Numerical trial based on observation in teak wooden furniture industry in Central Java was used to illustrate our findings. Using pareto efficient principle, the model can satisfy all goals that need to be achieved. Numerical results can be used by decision makers in teak wood industry to analyze the trade-off among several set of alternative solutions

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Choosing a transport contract over multiple periods

We offer a shipper and a carrier the choice among three contracts in which to frame their relationship. Both can also take recourse in the transport spot market. Demand and price on the spot market are dependent exogenous stochastic processes. We model the outcome of this endogenous choice of contract. The results, given in closed form, are different from those presented in the literature. Using numeric instances, we show how a choice is made and which contract would be preferred. Comparison on the variance of the economic returns are offered. The conclusions are applicable when the carrier is not capacity constrained.

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

Three Essays on Irregular Entries to the End-Customer Market

I study irregular entries to the end-customer market and the impact of such entries on suppliers, buyers, and customers. I am particularly interested in the irregularities of supplier encroachment and counterfeiting problems. This dissertation addresses these issues and proposes solutions in the form of three essays. In the first essay, I study a supply chain, consisting of a supplier and a buyer where the supplier can encroach on the end-customer market and keeps private information on its own production capacity. The supplier can decide on its capacity allocation and the buyer can order strategically, hoarding the supply capacity, to remove the competition. I find that the supplier is worse off, and the buyer is better off, when the supplier keeps its capacity information private. Further, I demonstrate that the supplier may no longer encroach on the end-customer market when it has more capacity. The second and third essays are inspired by the counterfeiting problem on online e-commerce platforms. In the second essay, I develop an algorithm that analyzes customers’ reviews on an online platform and provides an authenticity score for the products. I trained context-specific word embedding based on a large corpus of Amazon customer reviews to show that my unsupervised methodology provides good predictive power. Next, I study the effect of customers’ reviews on an e-commerce platform’s anti-counterfeiting strategy against third-party sellers. The platform can provide a tool for customers that analyzes just the product reviews or a more advanced tool that analyzes both the product and seller reviews to help customers determine if products are fake or genuine. On the seller’s side, it can choose to reveal its fake products by charging a lower separating price based on its profit under these two options. I demonstrate that even when the tools are free, the platform does not provide the advanced tool if the seller sells products with a low authenticity score (fake products), and it provides the basic tool if and only if the demand of the genuine product is sufficiently high. Together, these papers provide solutions on how to maximize profits by making informed decisions in the face of market irregularities for the supplier, the buyer, and the customer

Climate Change Reporting: A Resource Based Perspective

Kajian ini dilakukan untuk mengkaji tahap serta faktor yang mengalakkan laporan pemanasan global di antara syarikat-syarikat yang tersenarai di Bursa Malaysia. This study investigates the extent of climate change disclosure among Malaysian public listed companies

Evolution of a supply chain management game for the trading agent competition

TAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt