Pricing Perishables

 A key feature of food products is their perishability. Within the short marketing window that characterizes most food and ag products, demand is typically highly stochastic and difficult to predict. This combination of features poses substantial challenges to retailers when pricing products and has implications for performance that ripples through vertical food chains. For many food products, processing to forms that can be preserved and held in inventory has traditionally been used as a means of coping with these conditions, despite its high costs and ancillary risks introduced such as change in product attributes and deterioration. This paper presents an alternative ERM strategy that focuses on dynamic pricing to control the rate of sale for perishable products. The paper considers a retailer that has market power to price and supplies perishable products to a market with substitute products and demand originating from heterogeneous consumers. Perishability implies a finite horizon for the marketing of the products over which demand across market segments of consumers is both dynamic and stochastic. Faced with uncertainty, we suppose the firm has limited information about the stochastic properties of demand and must choose a pricing strategy that projects over the market horizon. This price trajectory represents a key control mechanism to cope with uncertainty of both the perishability of the product and of demand

Dynamic Pricing for Managing Product Selling on Fruit Supply Chain Management

Recently fresh fruit sector is grown not only due to increasing of demand that spirited by healthy lifestyle but also requirement of quality food should be eaten daily. Its complexity make many research considered fruit in certain supply chain, called as Fruit Supply Chain (FSC). In FSC, customers tend to purchase products with a longer remaining lifetime and avoid the ones which give aging signal. Customer willingness to pay decreases once the product start to be deteriorated, which may cause slower demand for aging fruits. Consequently, retailers should enable discounted price for aging fruits products to retain or improve demand rate. Hence, a solution of this is creating price that dynamically following the condition of goods. This research establishes pricing scheme, which is dynamic pricing to FSC. Main purpose of this research is explaining how to maximize supply chain profit by applying dynamic pricing. Remind that there is deterioration that does exist on FSC product and its customer preferences, dynamic pricing will be close to the real life particularly applied by FSC players. A set of mathematical model is optimized on this research. It addresses dynamic pricing for FSC players to achieve better profitability. The result proves that dynamic pricing is urgent to be done. In order to avoid unsold product due to became deteriorated, FSC players can separate selling period into three periods, which are forward buying period, normal price period, and markdown price period. Moreover, there are several parameters involved on optimization has different impact on FSC profitability, where it should be thoroughly focused on by FSC players collaboratively

Dynamic supply chain decisions based on networked sensor data:An application in the chilled food retail chain

With large volume of product flows and complex supply chain processes, more data than ever before is being generated and collected in supply chains through various tracking and sensory technologies. The purpose of this study is to show a potential scenario of using a prototype tracking tool that facilitate the utilisation of sensor data, which is often unstructured and enormous in nature, to support supply chain decisions. The research investigates the potential benefits of the chilled food chain management innovation through sensor data driven pricing decisions. Data generated and recorded through the sensor network are used to predict the remaining shelf-life of perishable foods. Numerical analysis is conducted to examine the benefit of proposed approach under various operational situations and product features. The research findings demonstrate a way of modelling pricing and potential of performance improvement in chilled food chains to provide a vision of smooth transfer and implementation of the sensor data driven supply chain management. The research finding would encourage firms in the food industry to explore innovation opportunities from big data and develop proper data driven strategies to improve their competitiveness

Illustrations of Price Discrimination in Baseball

Price discrimination of this nature, focused on differing degrees of quality, bundled goods, volume discounts, and other forms of second-degree price discrimination, is commonplace in MLB. Indeed, it is safe to say that every single MLB ticket is sold under some form of price discrimination. As teams grow increasingly sophisticated in their pricing strategies, price discrimination is becoming more precise, more wide-spread, and more profitable, while at the same time providing for more opportunities for more fans to find tickets at a price they are willing to pay. Unlike a baseball game, where one team must lose and one must win, price discrimination allows for win-win economic outcomes for teams and fans alike.price discrimination; bundling; variable pricing; dynamic pricing; secondary ticketing; two-part tariff; loaded ticket

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

Smart Retailing: a model to assess the economic sustainability of smart shelf-enabled dynamic pricing

Smart Retailing, a new approach to retail management that leverages digital technologies, is gaining much attention, as it enables innovation and improvements in consumers’ quality of life. However, the potentialities stemming from the application of such technologies are still not fully explored. Investment analyses addressing specific technologies could be useful to fill the academic gaps and guide retailers in their digital transition. This paper aims thus at evaluating the economic sustainability of investment in smart shelves, which are employed to perform dynamic pricing in presence of perishable goods. A model simulating the pricing variation in different scenarios was built and economic and financial analyses were performed to evaluate the sustainability of the investment. Data to feed the model were collected through semi-structured interviews with a smart shelf technology provider and three grocery retailers. The results show that the employment of smart shelves allows retailers to increase their profits. First, they are always able to assign to the product the price which most accurately reflects the customers’ willingness to pay. Second, the costs related to misplacement issues are reduced. This study contributes to the knowledge in this unexplored field by providing a model that simulates the dynamic pricing policy after the introduction of smart shelf technology and evaluates its economical sustainability. It also provides retailers who want to join the digital transformation of the stores with a useful tool to guide their investments

Optimizing lot sizing model for perishable bread products using genetic algorithm

This research addresses order planning challenges related to perishable products, using bread products as a case study. The problem is how to effi­ci­ently manage the various bread products ordered by diverse customers, which requires distributors to determine the optimal number of products to order from suppliers. This study aims to formulate the problem as a lot-sizing model, considering various factors, including customer demand, in­ven­tory constraints, ordering capacity, return rate, and defect rate, to achieve a near or optimal solution, Therefore determining the optimal order quantity to reduce the total ordering cost becomes a challenge in this study. However, most lot sizing problems are combinatorial and difficult to solve. Thus, this study uses the Genetic Algorithm (GA) as the main method to solve the lot sizing model and determine the optimal number of bread products to order. With GA, experiments have been conducted by combining the values of population, crossover, mutation, and generation parameters to maximize the feasibility value that represents the minimal total cost. The results obtained from the application of GA demonstrate its effectiveness in generating near or optimal solutions while also showing fast computational performance. By utilizing GA, distributors can effectively minimize wastage arising from expired or perishable products while simultaneously meeting customer demand more efficiently. As such, this research makes a significant contri­bution to the development of more effective and intelligent decision-making strategies in the domain of perishable products in bread distribution.Penelitian ini berfokus untuk mengatasi tantangan perencanaan pemesanan yang berkaitan dengan produk yang mudah rusak, dengan menggunakan produk roti sebagai studi kasus. Permasalahan yang dihadapi adalah bagaimana mengelola berbagai produk roti yang dipesan oleh pelanggan yang beragam secara efisien, yang mengharuskan distributor untuk menentukan jumlah produk yang optimal untuk dipesan dari pemasok. Untuk mencapai solusi yang optimal, penelitian ini bertujuan untuk memformulasikan masalah tersebut sebagai model lot-sizing, dengan mempertimbangkan berbagai faktor, termasuk permintaan pelanggan, kendala persediaan, kapasitas pemesanan, tingkat pengembalian, dan tingkat cacat. Oleh karena itu, menentukan jumlah pemesanan yang optimal untuk mengurangi total biaya pemesanan menjadi tantangan dalam penelitian ini. Namun, sebagian besar masalah lot sizing bersifat kombinatorial dan sulit untuk dipecahkan, oleh karena itu, penelitian ini menggunakan Genetic Algorithm (GA) sebagai metode utama untuk menyelesaikan model lot sizing dan menentukan jumlah produk roti yang optimal untuk dipesan. Dengan GA, telah dilakukan percobaan dengan mengkombinasikan nilai parameter populasi, crossover, mutasi, dan generasi untuk memaksimalkan nilai kelayakan yang merepresentasikan total biaya yang minimal. Hasil yang diperoleh dari penerapan GA menunjukkan keefektifannya dalam menghasilkan solusi yang optimal, selain itu juga menunjukkan kinerja komputasi yang cepat. Dengan menggunakan GA, distributor dapat secara efektif meminimalkan pemborosan yang timbul akibat produk yang kadaluarsa atau mudah rusak, sekaligus memenuhi permintaan pelanggan dengan lebih efisien. Dengan demikian, penelitian ini memberikan kontribusi yang signifikan terhadap pengembangan strategi pengambilan keputusan yang lebih efektif dan cerdas dalam domain produk yang mudah rusak dalam distribusi roti

Revenue Management In Manufacturing: A Research Landscape

Revenue management is the science of using past history and current levels of order activity to forecast demand as accurately as possible in order to set and update pricing and product availability decisions across various sales channels to maximize profitability. In much the same way that revenue management has transformed the airline industry in selling tickets for the same flight at markedly different rates based upon product restrictions, time to departure, and the number of unsold seats, many manufacturing companies have started exploring innovative revenue management strategies in an effort to improve their operations and profitability. These strategies employ sophisticated demand forecasting and optimization models that are based on research from many areas, including management science and economics, and that can take advantage of the vast amount of data available through customer relationship management systems in order to calibrate the models. In this paper, we present an overview of revenue management systems and provide an extensive survey of published research along a landscape delineated by three fundamental dimensions of capacity management, pricing, and market segmentation