Order Admission and Optimal Pricing for Regular Jobs and Big Deals at a Service Company

Order Admission and Optimal Pricing for Regular Jobs and Big Deals at a Service Company

Murat Erkoc (Industrial Engineering Department, University of Miami, Coral Gables, FL, USA) and Salvador Romo-Fragoso (Industrial Engineering Department, University of Miami, Coral Gables, FL, USA)
DOI: 10.4018/ijoris.2015010101

Abstract

This paper studies optimal pricing and demand management policies for a firm that faces two streams of order types: one is composed of recurring regular jobs with pre-determined prices (exogenous prices) and the other involves big deals that require pricing proposals (endogenous prices). The probability to secure the big deals diminishes with the quoted price. The authors develop and compare optimization models for different demand management settings. Specifically, we consider two distinct strategies: a pure strategy in which the firm commits to bid for deals only and a mixed strategy where the firm switches its allocation of capacity between regular jobs and deals. The authors compare optimal pricing strategies under two demand management strategies that differ in how they allocate capacity across regular jobs and deals, and order acceptance policies that they adopt. The authors observe that the differences between two strategies in terms of pricing and average gain are in accord. Under any given set of systems parameters, one of the strategies leads to both higher prices and average gains. Typically, the preferable strategy depends on the exogenous price of regular orders and the price sensitivity of the deals. The authors conclude that the threshold values for these two parameters are determined primarily by the demand rate of the deals and the service rate of the standard jobs.
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Introduction

Traditional research on pricing and demand management focuses on tangible products. In an increasingly competitive service economy, there is a substantial need to direct research in this area towards pricing services and projects. Since service is usually an intangible product, typically, it is a challenge to price and to manage its demand. Service providers carry out their consulting, construction, and maintenance pricing typically through bidding. This is especially the case for major service projects that require substantial resources and time commitment. Arrivals of such project requests are typically infrequent yet generate considerable revenues for service companies. As such they constitute big deals for such firms.

Service providers that may have steady but small size business flow may attempt to bid for big deals that show up at times. For example, a roof contractor that works on individual residential houses may face a request for proposals for maintenance of all houses in a large residential community, or a printer producer who satisfies its regular and steady demand through retailers may choose to bid for a big-deal that asks for replenishment, installation, and maintenance of all printers operated by a government branch. In fact, some firms may choose to drop all their small regular business and concentrate on chasing big deals only. When such opportunities arise, a service provider should develop an optimal pricing policy as well as the optimal business strategies to allocate its resources across regular jobs and big deals.

This paper studies optimal pricing and demand management policies for a firm that faces deals with random arrivals. We consider two streams of order types: one is composed of recurring standard or regular jobs with pre-determined prices (exogenous prices) and the other involves big deals that require pricing proposals (endogenous prices). The probability to secure the big deals diminishes with the quoted price. In the former case, the prices are determined by the market conditions. Typically, these jobs arrive more frequently with relatively lower service times. They are analogous to commodity products. On the other hand the big jobs typically arrive less frequently and require longer service times. The service provider needs to bid for the service prices to win these deals as they usually are competitive. The probability to secure the big deals diminishes with the quoted price.

The objective of our study is to develop and compare optimization models for different demand management settings. Specifically, we consider two distinct strategies: 1) a pure strategy in which the firm commits to bid for deals only and 2) mixed strategy where the firm switches its allocation of capacity between regular jobs and deals.

Our study is primarily motivated by our research involvement with a major information technology (IT) firm that provides a variety of services including consultation for their customers. The firm maintains under its payroll teams of skilled experts so as to deliver such services. As such, the firm incurs high cost labor that it must utilize efficiently. Consequently, it often times faces dilemmas in allocating its capacity across low margin yet steady flowing regular orders and “deals” usually generating high revenue yet displaying lumpy arrivals.

Using Markov Processes, we derive optimal pricing decisions and expected revenues for both the pure and the mixed strategies. We present a detailed analysis for the impact of demand and service rates of both types of orders on the optimal policies and revenues for both strategies. We compare both strategies to seek answers to some questions that are relevant for a service firm’s strategic fit and selection of markets to serve:

  • 1.

    How do the optimal pricing policies differ under two demand management policies?

  • 2.

    What are the conditions under which the service company prefers one policy over the other?

  • 3.

    How do the system parameters impact the appeal of either policy for the service provider?

The paper is organized as follows. Section 2 provides a survey of the relevant literature. The basic setting for the model is presented in Section 3. Section 4 discusses the pure strategy model that focuses on the case where the capacity is exclusively allocated for the big deals only. The pure strategy model is then extended to the mixed strategy model in Section 5, where the capacity is shared by the deals and regular jobs. Outcomes of both models in terms of prices and revenues are then compared and discussed in Section 6. Section 7 concludes the paper.

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