Retail Pricing Behavior for Perishable Produce Products in the US with Implications for Farmer Welfare

The typical model of retail pricing for produce products assumes retailers set price equal to the farm price plus a certain markup. However, observations from scanner data indicate a large degree of price dispersion in the grocery retailing market. In addition to markup pricing behavior, we document three alternative leading pricing patterns: fixed (constant) pricing, periodic sale, and high-low pricing. Retail price variations under these alternative pricing regimes in general have little correlation with the farm price. How do retailers’ alternative pricing behaviors affect farmers’ welfare? Using markup pricing as the baseline case, we parameterize the model to reflect a prototypical fresh produce market and carry out a series of simulations under different pricing regimes. Our study shows that if harvest cost is sufficiently low, retail prices adjusting only partially, or not at all, to supply shocks tends to diminish farm income and exacerbate farm price volatility relative to the baseline case. However, we also find that if harvest cost is sufficiently large and the harvest-cost constraint places a lower bound on the farm price, increased farm price volatility induced by retailers’ alternative pricing strategies may result in higher farm income, compared to markup pricing. Our study is the first to evaluate the welfare implications for producers of the diversified pricing strategies that retailers utilize in practice and the resulting attenuation of the relationship between prices at retail and at the farm gate.Agribusiness, Demand and Price Analysis,

Pricing Perishables with Uncertain Demand, Substitutes, and Consumer Heterogeneity

Within the marketing window for perishables such as food products, demand uncertainty is complicated by price sensitivity and propensity to postpone purchase that is heterogeneous across consumers. These features pose substantial challenges to retailers when pricing multiple products over time and across consumer segments. Getting the dynamic profile of prices right has implications for performance of vertical food chains ranging from revenues to food waste. This paper proposes an approach to dynamic pricing that is demonstrated to improve performance within this setting

Consumer prices : effects of learning algorithms and pandemic-related policy measures

When it comes to product prices, two major topics have dominated the public debate in recent years: One is pricing with the help of artificial intelligence, and the other is the price level, which has risen more than usual with the onset of the COVID-19 pandemic. Higher prices create a loss of consumer surplus and possibly total welfare, which is the reason this topic has become ubiquitous in political discussions. This dissertation contributes to the debate by extending the existing literature on algorithmic pricing, which is said to facilitate personalized pricing, as well as collusive behavior and to enhance the general understanding of how government measures enforced during the COVID-19 pandemic contributed to (short-time) price developments. Thereby, the first part of the thesis addresses the concern that tacit collusion might occur if firms employ learning algorithms, as several simulation studies have demonstrated that algorithms using reinforcement learning are able to coordinate their pricing behavior and, as a result, achieve a collusive outcome without having been programmed for it. We discuss several conceptual challenges as well as challenges in the real-world application of algorithms and show by or own simulations that resulting market prices strongly depend on the type of algorithm or heuristic that is used by the firms to set prices. In the subsequent part of the thesis we examine how a self-learning pricing algorithm performs when faced with inequity-averse consumers. From our simulations we can conclude that consumers sense of fairness, which have prevented firms from engaging in price discrimination in the past years, can be incorporated into firms pricing decisions with the help of learning algorithms, making differential pricing strategies more feasible. The discussion surrounding the above-average price levels in many countries during the COVID-19 pandemic is extended in the third part of the thesis. We present empirical evidence for the impact of government-imposed restrictions and, as a consequence of their enforcement, reduced mobility on consumer prices during the COVID-19 pandemic. We show that the stringency of government measures had a positive and significant impact on consumer prices mainly in the food sector, which means that more stringent measures induced higher consumer prices in these categories.Beim Thema Verbraucherpreise haben in den letzten Jahren vor allem zwei große Themen die öffentliche Debatte dominiert: Zum einen die Preisgestaltung mit Hilfe künstlicher Intelligenz und zum anderen das hohe Preisniveau, welches mit dem Ausbruch der COVID-19-Pandemie stärker als üblich angestiegen ist. Höhere Preise führen zu einem Verlust an Konsumentenrente und möglicherweise auch an Gesamtwohlfahrt, weshalb dieses Thema in der politischen Diskussion allgegenwärtig wurde. Die Dissertation leistet einen Beitrag zu dieser Debatte, indem sie die vorhandene Literatur zu algorithmischer Preisbildung erweitert, von der angenommen wird, dass sie eine personalisierte Preisbildung sowie kollusives Verhalten begünstigt, und indem sie das allgemeine Verständnis dafür verbessert, wie die während der COVID-19-Pandemie durchgesetzten staatlichen Maßnahmen zur (kurzfristigen) Preisentwicklung beigetragen haben. Der erste Teil der Arbeit befasst sich mit den Befürchtungen, dass es zu stillschweigenden Absprachen kommen könnte, wenn Unternehmen lernende Algorithmen einsetzen, da mehrere Simulationsstudien gezeigt haben, dass Algorithmen, die sogenanntes Reinforcement Learning einsetzen, in der Lage sind, ihr Preisverhalten zu koordinieren und infolgedessen ein kollusives Ergebnis zu erzielen, ohne dafür programmiert worden zu sein. Wir erörtern verschiedene konzeptionelle Herausforderungen sowie Hürden bei der realen Anwendung von Algorithmen und zeigen anhand eigener Simulationen, dass die resultierenden Marktpreise stark von der Art des Algorithmus oder der Heuristik abhängen, die von den Unternehmen zur Preisbildung verwendet wird. Im anschließenden Teil der Arbeit wird untersucht, wie sich ein selbstlernender Preisalgorithmus gegenüber ungleichheitsaversen Konsumenten verhält. Aus unseren Simulationen können wir schließen, dass das Fairnessempfinden der Verbraucher, das die Unternehmen in den vergangenen Jahren von Preisdiskriminierung abgehalten hat, mit Hilfe von lernenden Algorithmen in die Preisentscheidungen der Unternehmen einfließen kann, sodass differenzierte Preisstrategien wahrscheinlicher werden. Die Diskussion über das überdurchschnittliche Preisniveau in vielen Ländern während der COVID-19-Pandemie wird im dritten Teil der Dissertation vertieft. Es wird empirisch untersucht, inwieweit die Auswirkungen staatlich verordneter Beschränkungen und - als Folge ihrer Durchsetzung die eingeschränkte Mobilität die Verbraucherpreise während der COVID-19-Pandemie beeinflusst haben. Es wird gezeigt, dass die Strenge der staatlichen Maßnahmen einen positiven und signifikanten Einfluss auf die Verbraucherpreise vor allem im Lebensmittelsektor hatten, was bedeutet, dass strengere Maßnahmen zu höheren Verbraucherpreisen in diesen Kategorien geführt haben

Dynamic pricing models for electronic business

Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing

Grocery omnichannel perishable inventories: performance measures and influencing factors

Purpose- Perishable inventory management for the grocery sector has become more challenging with extended omnichannel activities and emerging consumer expectations. This paper aims to identify and formalize key performance measures of omnichannel perishable inventory management (OCPI) and explore the influence of operational and market-related factors on these measures. Design/methodology/approach- The inductive approach of this research synthesizes three performance measures (product waste, lost sales and freshness) and four influencing factors (channel effect, demand variability, product perishability and shelf life visibility) for OCPI, through industry investigation, expert interviews and a systematic literature review. Treating OCPI as a complex adaptive system and considering its transaction costs, this paper formalizes the OCPI performance measures and their influencing factors in two statements and four propositions, which are then tested through numerical analysis with simulation. Findings- Product waste, lost sales and freshness are identified as distinctive OCPI performance measures, which are influenced by product perishability, shelf life visibility, demand variability and channel effects. The OCPI sensitivity to those influencing factors is diverse, whereas those factors are found to moderate each other's effects. Practical implications- To manage perishables more effectively, with less waste and lost sales for the business and fresher products for the consumer, omnichannel firms need to consider store and online channel requirements and strive to reduce demand variability, extend product shelf life and facilitate item-level shelf life visibility. While flexible logistics capacity and dynamic pricing can mitigate demand variability, the product shelf life extension needs modifications in product design, production, or storage conditions. OCPI executives can also increase the product shelf life visibility through advanced stock monitoring/tracking technologies (e.g. smart tags or more comprehensive barcodes), particularly for the online channel which demands fresher products. Originality/value- This paper provides a novel theoretical view on perishables in omnichannel systems. It specifies the OCPI performance, beyond typical inventory policies for cost minimization, while discussing its sensitivity to operations and market factors

A Dynamic Pricing Model for Coordinated Sales and Operations

Recent years have seen advances in research and management practice in the area of pricing, and particularly in dynamic pricing and revenue management. At the same time, researchers and managers have made dramatic improvements in operations and supply chain management. The interactions between pricing and operations/supply chain performance, however, are not as well understood. In this paper, we examine this linkage by developing a deterministic, finite-horizon dynamic programming model that captures a price/demand effect as well as a stockpiling/consumption effect – price and market stockpile influence demand, demand influences consumption, and consumption influences the market stockpile. The decision variable is the unit sales price in each period. Through the market stockpile, pricing decisions in a given period explicitly depend on decisions in prior periods. Traditional operations models typically assume exogenous demand, thereby ignoring some of the market dynamics. Herein, we model endogenous demand, and we develop analytical insights into the nature of optimal prices and promotions. We develop conditions under which the optimal prices converge to a constant. In other words, price promotion is suboptimal. We also analytically and numerically illustrate cases where the optimal prices vary over time. In particular, we show that price dynamics may be driven by both (a) revenue effects, due to nonlinear market responses to prices and/or inventory, and (b) cost effects, due to economies of scale in operations. The paper concludes with a discussion of directions for future research

Hybrid Simulation-based Planning Framework for Agri-Fresh Produce Supply Chain

The ever-increasing demand for fresh and healthy products raises the economic importance of managing Agri-Fresh Produce Supply Chain (AFPSC) effectively. However, the literature review has indicated that many challenges undermine efficient planning for AFPSCs. Stringent regulations on production and logistics activities, production seasonality and high yield variations (quantity and quality), and products vulnerability to multiple natural stresses, alongside with their critical shelf life, impact the planning process. This calls for developing smart planning and decision-support tools which provides higher efficiency for such challenges. Modelling and simulation (M&S) approaches for AFPSC planning problems have a proven record in offering safe and economical solutions. Increase in problem complexity has urged the use of hybrid solutions that integrate different approaches to provide better understanding of the system dynamism in an environment characterised by multi-firm and multi-dimensional relationships. The proposed hybrid simulation-based planning framework for AFPSCs has addressed internal decision-making mechanisms, rules and control procedures to support strategic, tactical and operational planning decisions. An exploratory study has been conducted using semi-structured interviews with twelve managers from different agri-fresh produce organisations. The aim of this study is to understand management practices regarding planning and to gain insights on current challenges. Discussions with managers on planning issues such as resources constraints, outsourcing, capacity, product sensitivity, quality, and lead times have formed the foundation of process mapping. As a result, conceptual modelling process is then used to model supply chain planning activities. These conceptual models are inclusive and reflective to system complexity and decision sensitivity. Verification of logic and accuracy of the conceptual models has been done by few directors in AFPSC before developing a hybrid simulation model. Hybridisation of Discrete Event Simulation (DES), System Dynamics (SD), and Agent-Based Modelling (ABM) has offered flexibility and precision in modelling this complex supply chain. DES provides operational models that include different entities of AFPSC, and SD minds investments decisions according to supply and demand implications, while ABM is concerned with modelling variations of human behaviour and experience. The proposed framework has been validated using Table Grapes Supply Chain (TGSC) case study. Decision makers have appreciated the level of details included in the solution at different planning levels (i.e., operational, tactical and strategic). Results show that around 58% of wasted products can be saved if correct hiring policy is adopted in the management of seasonal labourer recruitment. This would also factor in more than 25% improved profits at packing house entity. Moreover, an anticipation of different supply and demand scenarios demonstrated that inefficiency of internal business processes might undermine the whole business from gaining benefits of market growth opportunities