Nagesh N. Murthy

A framework for assessing value chain agility

Purpose – To gain understanding of value chain (VC) agility in terms of value‐adding processes, this paper seeks to present a VC agility framework and then to develop the involved constructs.Design/methodology/approach – A framework of VC agility and its theoretical underpinnings is presented. Within the framework, drivers and determinants of VC agility are identified as characteristics enabling flexibility within key components of a firms VC. Also, it is posited that information technology (IT) capability impacts the levels of achieved flexibility and agility, and that VC agility impacts business performance.Findings – From scale development, key determinants of flexibility within VC activities are identified. Correlation analysis suggests that firms derive higher levels of agility through integrating information across the VC rather than within VC activities. Firms with flexibility in their VC functions enjoy higher levels of ensuing VC agility and on‐time delivery, ROA, and market share.Research limit...

A Framework for Facilitating Sourcing and Allocation Decisions for Make-to-Order Items

This paper provides a fundamental building block to facilitate sourcing and allocation decisions for make-to-order items. We specifically address the buyers vendor selection problem for make-to-order items where the goal is to minimize sourcing and purchasing costs in the presence of fixed costs, shared capacity constraints, and volume-based discounts for bundles of items. The potential suppliers for make-to-order items provide quotes in the form of single sealed bids or participate in a dynamic auction involving open bids. A solution to our problem can be used to determine winning bids amongst the single sealed bids or winners at each stage of a dynamic auction. Due to the computational complexity of this problem, we develop a heuristic procedure based on Lagrangian relaxation technique to solve the problem. The computational results show that the procedure is effective under a variety of scenarios. The average gap across 2,250 problem instances is 4.65%.

Service Package Switching in Hotel Revenue Management Systems

Revenue Management Systems (RMS) are commonly used in the hotel industry to maximize revenues in the short term. The forecasting-allocation module is a key tactical component of a hotel RMS. Forecasting involves estimating demand for service packages across all stayover nights in a planning horizon. A service package is a unique combination of physical room, amenities, room price, and advance purchase restrictions. Allocation involves parsing the room inventory among these service packages to maximize revenues. Previous research and existing revenue management systems assume the demand for a service package to be independent of which service packages are available for sale. We develop a new forecasting-allocation approach that explicitly accounts for this dependence. We compare the performance of the new approach against a baseline approach using a realistic hotel RMS simulation. The baseline approach reflects previous research and existing industry practice. The new approach produces an average revenue increase of at least 16% across scenarios that reflect existing industry conditions.

Offsetting inventory cycles of items sharing storage

Abstract The ability to determine the optimal frequencies and offsets for independent and unrestricted ordering cycles for multiple items can be very valuable for managing storage capacity constrained facilities in a supply chain. The complexity of this problem has resulted in researchers focusing on more tractable surrogate problems that are special cases of the base problem. This research has focused on developing fundamental properties of the original problem. We exploit the problem structure and present a heuristic for offsetting independent and unrestricted ordering cycles for items to minimize their joint storage requirements. Heuristics of this type may prove useful in solving the more general problem of selecting order quantities to minimize combined holding, ordering, and storage costs.

A Framework for Estimating Benefits of Using Auctions in Revenue Management

We develop a stochastic model to explore the benefits of incorporating auctions in revenue management. To the best of our knowledge the extant literature on modeling in revenue management has not considered auctions. We consider three models, namely, a traditional fixed price (non-auction) model, a pure auction model, and a hybrid auction model and evaluate their revenue performance under a variety of conditions. The hybrid approach outperforms the other two in all 24 scenarios and yields an average revenue increase of 16.1% over the next best. A surprise finding is that there is no significant difference between the performance of the fixed price and pure auction approaches. A sensitivity analysis reveals that the relative superiority of the hybrid revenue management strategy is reasonably robust.

Viability of Auction‐Based Revenue Management in Sequential Markets

The Internet is providing an opportunity to revenue management practitioners to exploit the potential of auctions as a new price distribution channel. We develop a stochastic model for a high-level abstraction of a revenue management system (RMS) that allows us to understand the potential of incorporating auctions in revenue management in the presence of forecast errors associated with key parameters. Our abstraction is for an environment where two market segments book in sequence and revenue management approaches consider auctions in none, one, or both segments. Key insights from our robust results are (i) limited auctions are best employed closest to the final sale date, (ii) counterbalancing forecast errors associated with overall traffic intensity and the proportion of customer arrivals in a segment is more important if an auction is adopted in that segment, and (iii) it is critically important not to err on the side of overestimating market willingness to pay.

Key Factors for Green Product Line Design: Opposing Consumer Perceptions, Cost Implications, Price and Quality Optimization

We consider the price and quality optimization (i.e., product line design) problem of a monopolist selling (at most) two product variants, a base product and a green variant that comprises recycled/reused content, to a market of two distinct consumer types with heterogeneous valuations for the base product. Our setting features three distinguishing elements, which had hitherto not been considered together in the literature: (i) Consumer segments demonstrate opposing perceptions of the green variant, i.e., “conventionals” associate dis-utility with a green variant that has more recycled/reused content, whereas “naturalites” have a higher willingness-to-pay for the same. (ii) Including a green variant yields diseconomies-in-scope for the firm’s production costs, which increases non-linearly as product variants become more vertically (and environmentally) differentiated. (iii) Using more recycled/reused content for the green variant permits input material cost savings. Using an endogenous demand model and non-linear programming theory, we characterize the economic conditions under which a monopolist can profitably cover the entire market by maintaining a uniformly green product line, i.e., by selling only the green product variant. When such equilibrium outcomes result, the firm’s traditional profit maximization objective coincides with an environmentally-conscious outcome. We also assess the demand segmentation and firm profit consequences of underestimating the naturalites’ (conventionals’) marginal utility (dis-utility) for a green variant with more recycled/reused content, and show that such missteps may yield adverse implications for both firm profits and the environment.We consider the price and quality optimization (i.e., product line design) problem of a monopolist selling (at most) two product variants, a base product and a green variant that comprises recycled/reused content, to a market of two distinct consumer types with heterogeneous valuations for the base product. Our setting features three distinguishing elements, which had hitherto not been considered together in the literature: (i) Consumer segments demonstrate opposing perceptions of the green variant, i.e., “conventionals” associate dis-utility with a green variant that has more recycled/reused content, whereas “naturalites” have a higher willingness-to-pay for the same. (ii) Including a green variant yields diseconomies-in-scope for the firm’s production costs, which increases non-linearly as product variants become more vertically (and environmentally) differentiated. (iii) Using more recycled/reused content for the green variant permits input material cost savings. Using an endogenous demand model and non-linear programming theory, we characterize the economic conditions under which a monopolist can profitably cover the entire market by maintaining a uniformly green product line, i.e., by selling only the green product variant. When such equilibrium outcomes result, the firm’s traditional profit maximization objective coincides with an environmentally-conscious outcome. We also assess the demand segmentation and firm profit consequences of underestimating the naturalites’ (conventionals’) marginal utility (dis-utility) for a green variant with more recycled/reused content, and show that such missteps may yield adverse implications for both firm profits and the environment.

Timing and Signaling Considerations for Recovery from Supply Chain Disruption

We explore the supplier’s and buyer’s reactions to supply disruption under information asymmetry about the severity of disruption. Upon disruption, the supplier strategically quotes a due date for supply recovery and decides the completion time for recovery. Given the quoted due date, the buyer decides whether to wait for recovery or invoke the backup option. In the case where the buyer has perfect information about the severity of disruption, we find that the supplier can be strategically tardy in recovery while, interestingly, benefiting itself and the buyer. We also find that an increase in tardiness penalty benefits the supplier. Under asymmetric information, we model the supplier’s and buyer’s interaction as a signaling game, in which the supplier’s quote of due date is an operational signal for the severity of disruption. We find that costly signaling occurs when the buyer’s cost for backup is low. In some situations, the supplier may not be able to credibly signal the severity of disruption to the buyer. In other situations, while the supplier can credibly signal the severity, information asymmetry pushes the supplier to quote an earlier due date. Interestingly, in both situations we find that the buyer benefits from information asymmetry. When the buyer’s backup cost is medium or high, information asymmetry does not affect either the buyer or the supplier vis-a-vis the case of perfect information.