Mamata Jenamani

A stochastic model of e-customer behavior

Abstract Web usage mining techniques are increasingly used today to understand e-customers’ within-site behavior. We propose a data mining model that considers e-customers’ activities as a discrete-time semi-Markov process and explains their behavior. An algorithm is proposed to compute transition probability matrix and holding time mass functions from the site navigation data. Finally, the model is used to explain customer behavior in an example site. A software agent, implemented in the site, collects and stores navigation data in the required form and thus helps to avoid data preprocessing. The model results helped to improve the site design and judge its performance.

Sourcing decision under disruption risk with supply and demand uncertainty: A newsvendor approach

Multi-sourcing is considered as a common practice to hedge against supply disruption risk. In this context, this paper proposes two models for optimal order allocation in newsvendor setting, where both supply and demand are uncertain. The first model considers a risk neutral decision maker who maximizes the total expected profit under disruption risk. The second one is for a risk averse decision maker who does so under service level constraints. Analytical closed form solutions for both the models are derived. To overcome the computational complexity of the exact optimal solution, two algorithms are developed to generate optimal order quantity and the corresponding set of suppliers. The solutions with exact optimization algorithms and the proposed ones are illustrated and compared with numerical examples. The results show that the proposed algorithms give the exact optimal solution while being tractable. Finally, a case study is used to illustrate the applicability of the proposed model.

GA Based Winner Determination in Combinatorial Reverse Auction

Combinatorial auction provides efficient resource allocations than traditional auction mechanisms in multi-item auctions, where the valuation of the items are not additive. However, solving winner determination problem so as to minimize procurement cost in combinatorial auction is Incomplete. In this paper, we consider a procurement scenario where the buyer wants to procure single unit of multiple items from a set of suppliers using single round sealed bid auction. The suppliers provide XOR bids for all the combinations of items. We propose Genetic Algorithm for solving winner determination problem for combinatorial allocation. A concept of repairing infeasible solution is used during the solution. We show that the algorithm is able to solve the problem within reasonable time.

COUNTERACTING SHILL BIDDING IN ONLINE ENGLISH AUCTION

The popularity of online auctions and the associated frauds have led to many auction sites preferring English auction over other auction mechanisms. The ease of adopting multiple fake identities over the Internet nourishes shill bidding by fraudulent sellers in English auction. In this paper, we derive an equilibrium bidding strategy to counteract shill bidding in an online English auction. An algorithm based on this strategy is developed. We conduct experiments to evaluate the strategy in a simulated eBay like auction environment. Five popular bidding strategies are compared with the proposed one. In the simulation, bidders compete to buy a product in the presence of a shill. Each bidder is randomly assigned a bidding strategy. She draws her valuation from a uniform distribution. The experiments show hat the average expected utility of agents with proposed strategy is the highest when the auction continues for a longer duration.

Online customized index synthesis in commercial Web sites

An algorithm that can generate and display helpful links while users navigate a site can increase a Web sites usability and help Web designers and the user achieve their goals.

Mean-variance analysis of sourcing decision under disruption risk

This study considers a mean-variance (MV) framework for managing disruption risk in a two-echelon supply chain with a risk-averse buyer and multiple unreliable suppliers under newsvendor (NV) setting. An MV objective function is designed to maximize the buyers expected profit while minimizing its variance. Study of the structural property of the problem proves the existence of a global maxima and a set of efficient portfolios consisting of dominating mean-variance pairs. We demonstrate the effect of model parameters through comparative statics analysis. An algorithm is developed to overcome the computational complexity of the higher dimensional problem. Numerical studies on model behavior show that the proposed algorithm gives the exact optimal solution while being tractable.

Supply chain coordination under retail competition using stock dependent price-setting newsvendor framework

Short life-cycle products which are characterized by uncertain demand, short selling season and long lead times have been posing many challenges to supply chain members. Demand of these products depends on several factors such as price, quality, service etc. Apart from these, many business practices have revealed that presence of a larger quantity of goods displayed at retail level also attract customers considerably. This paper captures the stock dependency phenomenon and investigates the role of quantity discounts and returns policies in the coordination of a supply chain. Here, the manufacturer in addition to returns policy provides quantity discounts to two competing retailers who face price-sensitive, stock dependent and uncertain demand. Using the newsvendor framework, a combined contract model is developed and sensitivity analysis is performed to analyze the impact of various parameters on supply chain coordination. The result shows that proposed contract mechanism fails to coordinate when the value of price sensitivity factor approaches the value of cross price sensitivity factor. Further, price-sensitivity and cross-price sensitivity have little effect on coordination benefit at lower values of stock dependency whereas there is a significant impact at higher values of stock dependency.

Risk assessment of outsourcing e-procurement services: integrating SWOT analysis with a modified ANP-based fuzzy inference system

Abstract With electronic procurement solutions becoming increasingly sophisticated, many firms opt to source these services from third-party providers, effectively transferring (outsourcing) significant responsibility to these services companies. This action, however, entails certain risks, which are oftentimes difficult to assess. To guide managerial practice and to advance academic inquiry in this domain, we identify e-procurement risk factors through a strengths, weaknesses, opportunities, threats analysis, grounded in transaction cost economics (TCE), and propose a risk assessment framework based on the opinions of a group of experts. The approach taken is that of a modified analytic network process methodology, combined with a fuzzy inference system, which is versatile enough to accept the expert opinions in different input formats (such as linguistic variables and ranges). The proposed framework has the capability to aggregate expert judgements’ to estimate risk likelihoods, risk severities and risk factor indices, and derive overall risk magnitudes. The multi-method approach was motivated and is illustrated by a real-life case study of an Indian manufacturing company currently in the process of contract renewal with its existing e-procurement service provider.

Make‐or‐buy: a case study at an Indian automobile company

Purpose – This paper aims to present a strategic framework for make‐or‐buy (MoB) decision‐making process and a case study based on the framework. This framework can be applied to all the scenarios of MoB decision‐making that includes making decisions for new components and reevaluating the decisions for presently in‐sourced or outsourced components.Design/methodology/approach – Based on this study of an Indian Automobile Company and review of the literature the paper proposes a strategic framework for MoB decision‐making. Unlike previous academic attempts, where a framework is developed first based on the existing theories followed by illustration through case studies, our approach starts with a case study, the paper then proposes a framework that best suits the companys need. Later the paper discusses the specific features of the framework that makes it applicable to other manufacturing sectors.Findings – The sourcing decision‐making for any firm is a complex and dynamic process where, after stipulated ...

Sustainability in Supply Chain Through E-Procurement—An Assessment Framework Based on DANP and Liberatore Score

Sustainable procurement aims at integrating specifications and criteria in the purchasing process in favor of environment protection while ensuring resource efficiency, improvement in product or service quality, and cost minimization. Although e-procurement is originally adopted by the companies for cost and cycle time reduction, more recently, it has been also used as a tool to attain sustainable procurement. However, not much effort has been made so far to explicitly identify the factors that portray e-procurement as a tool for sustainable procurement and assess the level of sustainability after adoption of necessary practices. We have identified 26 driving factors based on the review of literature and analyze six major dimensions that help in managing sustainable purchasing programs through the use of information and communication technologies. To handle the interdependence among the criteria, we develop an assessment framework using a hybrid model that combines two multicriteria decision making techniques, namely, Decision Making Trial and Evaluation Laboratory based analytic network process and Liberatore score. The effectiveness of the proposed model is demonstrated with a real-life case study.