Elsevier

Omega

Volume 88, October 2019, Pages 248-262
Omega

Optimal quality improvements and pricing strategies with active and passive product returns

https://doi.org/10.1016/j.omega.2018.09.007Get rights and content

Highlights

  • We consider pricing and quality investment with product returns.

  • We determine and compare optimal dynamic prices and constant price.

  • Two returns policy are evaluated, i.e., passive returns and active returns.

  • The manufacturer is always better off implementing an active return policy.

  • Constant pricing is near optimal when the effect of quality is high.

Abstract

A manufacturer invests in product quality to encourage consumers who have purchased in the past to substitute their current product version with a new release. Since price deters the adoption of an upgraded quality product, consumers evaluate both the quality improvements and the new release price before deciding whether to return a good. The returns can be either voluntary (passive returns) or dependent on the firm’s controls (active returns), while the pricing strategies can be either fixed (constant intertemporal pricing) or varying over time (updated intertemporal pricing) depending on the quality improvements. By combining these two ingredients (return type and pricing policy) we formulate a two-period model in which a manufacturer invests in quality improvements and sets the product prices over time. Our results show that when consumers passively return old product versions, the manufacturer should always update its pricing strategies according to the quality improvements. However, when consumer returns are sensitive to quality improvements and price, the manufacturer can be indifferent between setting a constant or an updated pricing policy depending on the effect that quality has on returns. If the manufacturer can choose between a market in which consumer returns are passive or active, it decides according to how quality impacts the returns: When the consumers’ willingness to return according to the quality effect is negligible, the manufacturer prefers to work in a market with passive attitudes towards returns. While the choice of updating the price is always dominant from an economic point of view, it turns out to be suboptimal from an environmental perspective when the effects of quality and price on returns are balanced. When the price effect on returns also depends on the discount granted to consumers, then the discrepancy between economic and environmental returns is amplified.

Introduction

Returning used products such as batteries and cellphones to the seller or to a dedicated collection center is an environmental friendly act, and consumers are encouraged to do so through a variety of communication campaigns and monetary incentives, e.g., a buyback price. Consumers often buy the new version of the product (if available) when returning the used one. Although this statement applies to the two above mentioned products (batteries and cellphones), these products differ in at least one respect, namely, consumers do not usually accelerate the repurchase timing of a battery but will do so for a cellphone. When a battery is discharged, it becomes useless and must be changed. Intuitively, no consumer will return a still-functional Duracell battery only because the company happens to introduce a new “greener” one that is made with 4% recycled materials and lasts longer than traditional batteries (http://www.duracell.com). However, a consumer may not wish to wait till her iPhone t breaks down before buying an iPhone t+1. This accelerated repurchasing may be due to the higher quality of the latest generation, e.g., additional features, a nicer design, etc., which match the consumer’s needs better than the older version did. Further, it is safe to state that the decision to replace also depends on the difference in price between the two versions. If the gap is too great with respect to the perceived quality improvement, then the consumer will not rush to change her product.

In brief, the paper’s main argument (and contribution) is as follows: If quality improvement and price levels are indeed drivers of a product’s return rate, then this endogenous effect must be properly accounted for in the manufacturer’s optimization problem. We shall call the returns active in this context, and refer to them as passive when the consumer’s decision to bring back the product is independent of the firm’s policies.

To be able to account for product returns in the decision process, we must naturally adopt a dynamic modeling approach. To keep it as parsimonious as possible while still being able to highlight the impact of active returns on pricing and quality decisions, we retain a two-period model in which some consumers who purchased in the first period return their product in the second period and buy the newly released version. We assume that the product’s quality in the first period is given (or equivalently normalized to zero), and the manufacturer chooses the quality improvement of the new version as well as the prices to charge in both periods. The manufacturer can adopt a dynamic pricing policy, which allows for the implementation of either a penetration or a skimming pricing strategy, or a constant pricing policy over the two periods. From an optimization point of view, it is clear that a constant pricing strategy can yield at best a profit that is equal to the profit obtained under dynamic pricing. The reason is that a constant price optimization problem is a constrained instance of the dynamic price optimization problem, and hence, cannot be profit improving. It is still conceivable that a firm prefers constant pricing for reasons that are not fully accounted for in the above reasoning. For instance, if changing the price causes high administrative and operational costs or leads to a strongly negative reaction from consumers because they perceive it as unfair price discrimination over time, then constant pricing may become an acceptable option. A higher price for the newly released product is, of course, easier for consumers to accept if it is accompanied by a visible improvement in product quality.

In a nutshell, the objective of this paper is to characterize and contrast optimal dynamic and constant pricing strategies, in the presence of quality improvements, in the contexts of both active and passive returns. More specifically, we wish to answer the following research questions:

  • 1.

    What are the optimal constant and dynamic prices when returns are passive, and how do they compare?

  • 2.

    What are the optimal constant and dynamic prices when returns are active, and how do they compare?

  • 3.

    What is the optimal quality improvement level in each scenario, that is, active/passive returns and constant/dynamic pricing? How the resulting levels compare?

  • 4.

    Are there any circumstances related to a product’s returns that make a constant pricing policy (near) optimal?

To motivate the relevance of looking at both constant and dynamic pricing policies, let us consider two well-known firms, namely, Renault and Apple. In Table 1, we provide some information about the Renault Scenic car. We note, in particular, that the price increases over time (and versions), along with the quality (e.g., lower fuel consumption). Of course, the difference in real terms between the successive prices may be small, but it still shows up. However, in the case of the Apple iPhone (Table 2), the release price is constant over time and independent of the quality improvements and upgrades.

Our distinctive contribution to the literature is in letting the return function depend on the manufacturer’s quality improvement and pricing decisions. It is indeed likely that additional features, creative design, more gadgets, etc., in a new version of a product (e.g., cellphone, tablet), to which we shall generically refer by “quality improvement” , induce some consumers to accelerate the replacement of their product. The firm can further incentivize consumers to buy earlier by offering a salvage value on the old version. Deciding when to rebuy also depends on the cost, that is, consumers will accelerate replacing of their old product version only if they feel that the quality of the new release justifies the price. As highlighted by Orsdemir et al. [20], the literature on closed-loop supply chain (CLSC) has mainly focused on quantity and price when dealing with returns, and has disregarded quality as an endogenous variable. In this regard, quality improvement aims at increasing the demand while having no (direct) impact on returns, which are determined optimally by the collector. A similar approach is adopted in [2] where an exogenous return rate influences the investment in quality, while an endogenous return rate induces a higher product quality. Diverging from [2] and [20], our return flow is a function of quality improvement and price. Xiong et al. [28] analyze a similar problem where a manufacturer must decide whether to invest in quality in the second period to sell an upgraded version. In [28], the return rate is exogenously set at 100%, whereas we endogenize this rate. Debo et al. [9] and Robotis et al. [23] consider quality at the returns remanufacturability level, with an eye on the remanufacturing cost. In our work, quality is a marketing tool that enhances both the demand and the returns. In [13], the exogenous level of innovation (quality) influences the production costs, thus playing an operational role, while the number of returns is set by the firm. In our model, quality improvement plays an active role in determining both the sales and the returns.

The literature on closed-loop supply chains and reverse logistics can be schematically divided into three streams when it comes to modeling the quantity (or percentage) of previously sold products that are brought back to a collector. To be brief, we shall refer to this quantity as returns, unless an ambiguity arises.

A first stream of studies models the returns as an exogenous parameter or the realization of a random process. In this case, the firm pursues a passive returns policy by simply taking back the previously sold products that consumers voluntarily decide to return [21]. Here, the firm does not attempt to influence the returns through, e.g., a communications program or monetary incentives. Dobos [10] models the return rate as a constant fraction of the past-sold products, while Minner and Kleber [18] characterize the return rate as a constant percentage of sales. Atasu et al. [1] hypothesize a 100% return rate while also focusing on the price and cost differences between new and remanufactured goods. Similarly, [28] assume that all consumers who have purchased a new product in one period return it in the second period. Geyer et al. [14] highlight the need to coordinate the return rate with cost savings to properly assess the benefits of a remanufacturing system. Ferrer and Swaminathan [12] model a two-period game wherein a remanufacturer optimally decides on price and quantity, with the return rate being a fixed parameter. Ramani and De Giovanni [22] model a CLSC game in which firms compete on returns modeled as an exogenous parameter. The firm that gets the largest part of the returns will always be economically better off, even in presence of the cannibalization effect between used and new goods. Yenipazarli [29] and Wu [27] assume that the returns are a given fraction of the products sold in the first period. De Giovanni and Ramani [8] use an exogenous return rate to investigate how the cannibalization effect can be mitigated through an ad hoc service strategy.

The second stream of the literature treats the returns either as a control variable or a function of the firms’ strategies. Wang et al. [26] model the return rate as a function of the quality threshold, to qualify returns for remanufacturing in-house or via outsourcing. Savaskan et al. [24] and Savaskan and Van Wassenhove [25] let the manufacturer, the retailer, or a collector decide on the fraction of returns to be collected from the market, according to some promotional efforts. Hong et al. [16] extend this formulation to a case where competition exists along with technology licensing. In all cases, the return rate is a decision variable. In [11], an original equipment manufacturer (OEM) decides on the number of cores to be collected and those to be remanufactured. De [6]) formulate a return function that depends on investments in green programs, while Ramani and De Giovanni [22] retain a return rate that depends on advertising efforts. Sheu and Gao (2012) consider a return rate that depends on the amount recycled and the production quantity, set by CLSC members, as well as on the recycling rate determined by the government. Kaya [17] proposes a return function that depends linearly on an incentive controlled by the manufacturer. Govindan and Popiuc [15] use a similar endogenous return function in a CLSC aiming at coordination through a revenue-sharing contract. Zhao and Zhu [30] model a return quantity that linearly depends on the acquisition price, which is decided by the retailer.

The third stream of the literature models the returns as a dynamic process whose evolution is influenced by given control variables. De Giovanni and Zaccour [5] assume that the returns dynamics depend on a green activity program established by a manufacturer. In [4], the evolution of the return rate depends on all CLSC members’ environmental policies. Using a similar approach, De Giovanni et al. [4] links the return rate to the manufacturer’s corporate social responsibility attitude. In [3], [7], the return flow depends on the consumers’ environmental consciousness.

The remainder of this study is organized as follows. Section 2 presents the model. Section 3 characterizes the optimal policies and profits in the different scenarios, and Section 4 compares the constant and dynamic pricing policies. Section 5 briefly concludes.

Section snippets

Model

Consider a manufacturer selling new products over two periods t=1,2. Denote by pt the price of the product in period t, and let the demand in the first period be specified as follows:D1(p1)=αβp1,where α > 0 is the market potential and β is the consumers’ sensitivity to price.

In period 2, the manufacturer invests in quality improvements, denoted by Q, at a convex increasing costC(Q2)=μQ22,where μ is a positive parameter capturing the operational efficiency of this investment.

Demand in the

Optimal pricing and quality decisions

In this section, we derive the optimal pricing and quality decisions for both scenarios.

Comparing the two scenarios

We now compare the strategies, returns and profits obtained in the two pricing scenarios. We first consider the case where the returns are passive and next look at the active returns case.

Conclusions

In this article, we study the quality improvements and pricing strategies of a manufacturer who can choose between setting a new release price at the same level as that of its predecessors (e.g., Apple’s iPhones) or updating that price according to its quality-improvement investments (e.g., Renault’s cars). We investigate these two pricing policies in two frameworks, namely, passive and active returns. In the former, the consumers’ returns are not influenced by the manufacturer’s quality

Acknowledgement

We would like to thank the two anonymous Reviewers and the Associate Editor for very helpful comments. Research supported by Natural Sciences and Engineering Research Council of Canada, Canada, grant RGPIN/37525-2011.

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    This manuscript was processed by Associate Editor X. He.

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