Preetam Basu

Challenges of supply chain finance: A detailed study and a hierarchical model based on the experiences of an Indian firm

Purpose – The purpose of this paper is to examine the different challenges that confront supply chain finance (SCF) and to develop a hierarchical model that analyzes the complex relationship dynamics among them.Design/methodology/approach – An extensive survey is carried out amongst Indian firms to ascertain the perceptions and experiences related to different SCF challenges. After obtaining an overview of the different SCF challenges, an Indian company with global operations was approached and after establishing relationships among the challenges, a hierarchical relationship structure was developed and MIMBI analysis (where MI=measure of influencing; MBI=measure of being influenced) was carried out that helped understand the relationship dynamics of SCF challenges and identify actions at both strategic and tactical levels.Findings – The study reveals that lack of common vision among the supply chain (SC) partners is the most critical challenge confronting SCF. Unpredictable cash‐flows resulting from dela...

Supply Chain Finance enabled early pay: unlocking trapped value in B2B logistics

Recent breakthroughs in Business-to-Business (B2B) trade have opened new avenues for value creation in the financial supply chains. Based on supply chain event triggers, innovative financing is being provided at different points of the supply chains. This new phenomenon, termed as Supply Chain Finance (SCF), is transforming Working Capital Management (WCM). In this study we focus on payables management of a buying firm in a supply chain with SCF enabled early payment program in place. A stochastic dynamic programming model is developed and the value of SCF enabled early payment program under varying future cash-flows are ascertained through extensive numerical analysis.

A decision support system for mean-variance analysis in multi-period inventory control

Traditionally inventory management models have focused on risk-neutral decision making with the objective of maximizing the expected rewards or minimizing costs over a specified time horizon. However, for items marked by high demand volatility such as fashion goods and technology products, this objective needs to be balanced against the risk associated with the decision. Depending on how the product performs vis-a-vis the sellers original forecast, the seller could end up with losses due to either short or surplus supply. Unfortunately, traditional models do not address this issue. Stochastic dynamic programming models have been extensively used for sequential decision making in the context of multi-period inventory management, but in the traditional way where one either minimizes costs or maximizes profits. Risk is implicitly considered by accounting for stock-out costs. Considering risk and reward simultaneously and explicitly in a stochastic dynamic setting is a cumbersome task and often difficult to implement for practical purposes, since dynamic programming is designed to optimize on one variable, not two. In this paper we develop an algorithm, Variance-Retentive Stochastic Dynamic Programming that tracks variance as well as expected reward in a stochastic dynamic programming model for inventory control. We use the mean-variance solutions in a heuristic, RiskTrackr, to construct efficient frontiers which could be an ideal decision support tool for risk-reward analysis.

Pricing and sourcing strategies for competing retailers in supply chains under disruption risk

Supply disruption has become a critical concern for businesses around the world. The extant literature has dealt mainly with the sourcing decision for a price-taking retailer. In this paper, we study how a retailer can use pricing decisions along with sourcing strategies under disruption risk while competing against another retailer with a more reliable supply chain. The retailer uses two decision levers namely, price adjustment, and split of order between reliable but expensive supplier and/or cheap but unreliable supplier to compete in the end market. Our analyses show that the competitive dynamics is shaped by the cost structure of the players, relative market potential and disruption risk. We find that the retailer focuses on reliable supplies with less price adjustment when it enjoys procurement cost advantage and higher market potential. On the other hand, as the procurement cost advantage and market potential shifts to the competitor; the retailer opts for cheaper but risky supplies and relies on drastic price adjustments. These results have important managerial implications and provide critical guidelines for retailers involved in pricing and sourcing decisions under the threat of supply disruptions.

A game theoretic analysis of multichannel retail in the context of “showrooming”

Showrooming as a market phenomenon in multichannel retailing has grown in importance over the last few years. Consumers nowadays use the brick-and-mortar store to research about a product before purchasing it online. This leads to the offline stores being converted into showrooms for the online retailers. Therefore, popular notion suggests that showrooming should benefit the online retailer. In this paper, our objective is to analyze multichannel retailing under showrooming and determine the veracity of the popularly held belief. We develop a series of game theoretic models that involve a traditional retailer and an online retailer under showrooming. We determine optimal pricing strategies for each player and also the sales effort expended by the traditional retailer based on the interplay of power dynamics, market potential and the impact of showrooming. Our results indicate that profit for the traditional as well as the online retailer decreases with rising levels of showrooming. Hence, high levels of showrooming are not beneficial from the perspective of the online retailer. Thus, contrary to popular intuition, lessening of showrooming benefits not only the traditional retailer but also the online retailer. Nevertheless, from the consumers point of view showrooming is beneficial as it leads to overall reduction in retail prices. We also analyze the viability of a click-and-mortar model as a strategy of the traditional retailer to counter the threat of showrooming. We analyze multi-channel retailing under showrooming in a game theoretic set up.We study the impact of showrooming on the traditional as well as the online retailer.We study the interaction of market potential and power dynamics with showrooming.We develop a strategy matrix for pricing decisions under showrooming.

Analysis of Back-Office Outsourcing Contracts for Financial Services Operations

Managing back-office operations for financial services is a challenging task because of highly volatile and dynamic demand requirements. Lack of service inventories, the inability to backlog demand and significant shortage and overage costs complicate the problem. In such situations, outsourcing all or part of the demand to third-party vendors provides a viable and cost effective option for the firm. Motivated by the remittance processing operations of a Fortune 100 company we examine the usefulness of complementing in-house staffing with different outsourcing arrangements. We study capacity-based and volume-based contracts between a financial services firm and an outsourcing vendor. We examine the impact of demand characteristics on the parameters of contract choice. Through extensive numerical analysis, we ascertain that neither contract is universally preferred, but cost and revenue structures along with demand characteristics determine contract choice.

Supply chain management using put option contracts with information asymmetry

We study the problem of hedging demand uncertainty in a supply chain consisting of a risk-neutral supplier and a risk-averse retailer under a buyback contract. We use semi-variance of the possible profit values as a measure of the retailer’s risk attitude. We first study the setting where the supplier can observe the risk type of the retailer and find that in this case the supplier can design a buyback contract that extracts the maximum profit for the supplier. When the retailer’s type is unobservable, a new contract needs to be designed (the ‘option buyback contract’) and we show that in this case the retailers will self-select and chose an order quantity that maximises the total supply chain profit. Through numerical computations, we analyse the dynamics between the benefits of hedging risk, information rent and the retailer’s type, and outline cases when, depending on the shape of the reservation utilities of the retailers, it is too costly for the supplier to manage risk. In conclusion, our results show that whereas semi-variance has appealing properties as a measure of risk, its use introduces analytical challenges that can only be overcome through numerical computation.

Analyzing operational risk-reward trade-offs for start-ups

Abstract In many managerial situations it is important to consider both risk and reward simultaneously. This is a challenging task using standard techniques that are applied for solving sequential stochastic optimization problems since these techniques are designed to consider only one objective at a time—either maximizing reward or minimizing risk. In applications such as operational decisions for start-ups, this can be particularly restricting, since managers need to make trade-offs between profitability driven growth and the risk of bankruptcy. We extend in several ways prior work that has addressed the inventory issue for start-ups to minimize the risk of bankruptcy. The primary contribution of this paper is to present a novel approach to track mean as well as variance of a set of policies in a dynamic stochastic programming model and using the mean-variance solutions in a simple heuristic for creating efficient risk-reward frontiers. This is a challenging task from an implementation standpoint, since this requires carrying information on both risk and reward simultaneously for each state, which standard stochastic programming solution methods are not designed to do. We also illustrate the use of our methodology in a richer model of start-up operations where, in addition to inventory issues, advertising decisions are also considered.