Stochastic Optimization Models for Perishable Products

For many years, researchers have focused on developing optimization models to design and manage supply chains. These models have helped companies in different industries to minimize costs, maximize performance while balancing their social and environmental impacts. There is an increasing interest in developing models which optimize supply chain decisions of perishable products. This is mainly because many of the products we use today are perishable, managing their inventory is challenging due to their short shelf life, and out-dated products become waste. Therefore, these supply chain decisions impact profitability and sustainability of companies and the quality of the environment. Perishable products wastage is inevitable when demand is not known beforehand. A number of models in the literature use simulation and probabilistic models to capture supply chain uncertainties. However, when demand distribution cannot be described using standard distributions, probabilistic models are not effective. In this case, using stochastic optimization methods is preferred over obtaining approximate inventory management policies through simulation. This dissertation proposes models to help businesses and non-prot organizations make inventory replenishment, pricing and transportation decisions that improve the performance of their system. These models focus on perishable products which either deteriorate over time or have a fixed shelf life. The demand and/or supply for these products and/or, the remaining shelf life are stochastic. Stochastic optimization models, including a two-stage stochastic mixed integer linear program, a two-stage stochastic mixed integer non linear program, and a chance constraint program are proposed to capture uncertainties. The objective is to minimize the total replenishment costs which impact prots and service rate. These models are motivated by applications in the vaccine distribution supply chain, and other supply chains used to distribute perishable products. This dissertation also focuses on developing solution algorithms to solve the proposed optimization models. The computational complexity of these models motivated the development of extensions to standard models used to solve stochastic optimization problems. These algorithms use sample average approximation (SAA) to represent uncertainty. The algorithms proposed are extensions of the stochastic Benders decomposition algorithm, the L-shaped method (LS). These extensions use Gomory mixed integer cuts, mixed-integer rounding cuts, and piecewise linear relaxation of bilinear terms. These extensions lead to the development of linear approximations of the models developed. Computational results reveal that the solution approach presented here outperforms the standard LS method. Finally, this dissertation develops case studies using real-life data from the Demographic Health Surveys in Niger and Bangladesh to build predictive models to meet requirements for various childhood immunization vaccines. The results of this study provide support tools for policymakers to design vaccine distribution networks

Mitigating demand risk of durable goods in online retailing

Purpose An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost. Design/methodology/approach Four e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer. Findings Mixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction. Research limitations/implications Limited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss. Practical implications The analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner. Originality/value The study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information

Revenue Management in the Sport Industry: an Examination of Forecasting Models and Advance Seat Section Inventory in Major League Baseball

Technological advances in data storage and processing have led to more sophisticated ticket pricing strategies in professional sport. Sport organizations are beginning to adopt a form of revenue management known as dynamic ticket pricing. Effective pricing strategies such as dynamic ticket pricing require an in-depth understanding of the nature of advance ticket inventory and accurate forecasting models to predict remaining inventory at various time horizons prior to game time. The purpose of this study was to gain an understanding of the nature of advance seat section ticket inventory. The study built on and contributed to work in sport revenue management. Although studies of sport revenue management have examined the applicability of revenue management in a sport context, there has not been a study of advance seat section ticket inventory despite the fact that sport organizations utilize price discrimination strategies at the seat section level. As such, this study provided additional insight into the applicability and potential effectiveness of a sport revenue management strategy. The methodological focus on forecasting models and accuracy enabled another contribution. A 3x3x6x7 full factorial research design examined the accuracy of various forecasting models under different data strategies, time horizons, model parameters, and levels of the values of T and K used in the moving average and exponential smoothing forecasting models. Statistically reliable differences existed between data strategies with the classical pickup data strategy providing the best forecasts of final game day inventory. Within the classical pickup strategy, no reliable differences in forecast models were detected nor were forecasts found to significantly differ when changing the value of T or K. Finally, forecast accuracy was shown to follow the theoretically predicted best to worst pattern as days out increased. A profile analysis of seat section ticket inventory showed seat sections exhibit different slopes and changes in slope over time. The general pattern of ticket inventory followed a linear trend but with varying slopes. Steeper slopes were found at 20, 10, and 5 days out followed by a leveling out between 5 and 3 days out which was then followed by steeper slopes from 3 days to game day. This finding suggested that optimizing a sport revenue management plan should include forecasting at the seat section level

Hybrid Model for It Investment Analysis: Application to Rfid Adoption in the Retail Sector

One of the major obstacles in Information Technology (IT) adoption is its return on investment analysis. IT benefits in organizations are hard to measure and are usually realized over time. System dynamics approach has been used in IT literature to identify the impact of IT on business processes. Given benefits of any IT system in organizations, however, there is a high degree of uncertainty in achieving such benefits. Managerial flexibility in decision making process of implementing a new IT helps managers to overcome this uncertainty over time. Traditional cost benefit analysis such as NPV that is typically used to value any technology is unable to value managerial flexibilities while real options theory offers a model that can value a new investment as uncertainties about the system decreases over time. In this dissertation, we are proposing a new hybrid model for IT return on investment (ROI) that combines system dynamics and real options as two major techniques in economics of IT. This robust hybrid model takes advantages of both techniques while overcoming their weaknesses. We propose a systems dynamic solution to simulate the way an IT influences and improves an organization to be able to estimate the parameters used in the real options model. The hybrid model is used to find the best time for investing in item-level RFID in the retail sector.The results of return on investment analysis on item-level investment show that the variable cost of investment that is the tag prices dominates the return on investment. Other factors such as product unit price and consequently type of retail stores are important as well. The system dynamics simulation provided some major parameters of the real options model such as the expected payoffs and volatility of the expected payoffs that were hard to find in the literature.Business Administration (MBA

Managing demand uncertainty: probabilistic selling versus inventory substitution

Demand variability is prevailing in the current rapidly changing business environment, which makes it difficult for a retailer that sells multiple substitutable products to determine the optimal inventory. To combat demand uncertainty, both strategies of inventory substitution and probabilistic selling can be used. Although the two strategies differ in operation, we believe that they share a common feature in combating demand uncertainty by encouraging some customers to give up some specific demand for the product to enable demand substitution. It is interesting to explore which strategy is more advantageous to the retailer. We endogenize the inventory decision and demonstrate the efficiency of probabilistic selling through demand substitution. Then we analyze some special cases without cannibalization, and computationally evaluate the profitability and inventory decisions of the two strategies in a more general case to generate managerial insights. The results show that the retailer should adjust inventory decisions depending on products' substitution possibility. The interesting computational result is that probabilistic selling is more profitable with relatively lower product similarity and higher price-sensitive customers, while inventory substitution outperforms probabilistic selling with higher product similarity. Higher demand uncertainty will increase the profitability advantage of probabilistic selling over inventory substitution.Peer ReviewedPostprint (author's final draft

Pricing and promotion: A literature review

This article intends to carry out a systematic review of the literature on pricing and promotion, as variables that impact profitability in organizations. To achieve this purpose, a systematic review was performed upon the most relevant academic journals (according to Scimago and Country Rank), for the period between 2018 and 2020. The article puts into evidence the correlation between pricing and promotion, as well as the different price-promotion tactics employed by organizations (including coupons, free samples, loyalty programs, discounts and cross selling, among other practices). An array of external factors was also found that affected pricing and promotion performance, making the study more complex. Therefore, despite the correlation existing between the variables at issue, it can be concluded that the success of a price-promotion strategy does not depend exclusively upon itself, but that the results of a monetary discount can be affected by multiple environmental phenomena. Finally, the text concludes with a presentation of certain endogenous factors that can impact the results of price-promotion strategies

Coordinated planning in revenue management

Revenue management has been applied in service industries for more than thirty years. Since then, revenue management has been transferred to other industries like manufacturing or e- fulfillment. Short-term revenue management decisions are taken based on other, longer-term decisions such as decisions about actual capacity, segment-based prices or the price fences in place. While optimization approaches have been developed for each of these planning tasks in isolation, existing approaches typically do not consider interactions between planning tasks. This thesis considers coordinated planning in revenue management, that is the interaction of revenue management decisions with other planning tasks. First, we provide an overview of both the literature on coordinated decision making in the context of revenue management in different industries, and the literature on existing frameworks, which aim to structure the planning tasks around revenue management. We find that the planning tasks relevant to revenue management differ across the industries considered. Moreover, planning tasks are relevant on different hierarchical levels in different industries. We discuss an approach for an industry-independent framework. Based on the relevant planning tasks identified, we investigate the long-term performance of revenue management and therefore the integration of revenue management and customer relationship management. We present a stochastic dynamic programming approach, where the firm’s allocation decision impacts future customer demands by influencing the repurchase probabilities of customers, depending on whether their request has been accepted or rejected. We show that a protection level policy is not necessarily optimal in a two-period setting. In a numerical study, we find that the value of looking ahead in time is low on average but may be substantial in some scenarios. However, the benefit from regular demand updates is considerably higher than the additional value of looking ahead in time on average. Lastly, we investigate the interaction of revenue management and fencing. We account for the trade-off between price-driven demand leakage on the one hand and costs for fencing on the other hand. We show that fencing decisions have an impact on the optimal capacity allocation, but that this is not the case vice versa as the fencing decision does not depend on the allocation decision. Taking both decisions sequentially is therefore optimal. We extend our approach in order to account for additional stock-out-based demand substitution. Then, both decisions depend on each other and firms should take both decisions simultaneously

Bundling retailing under stochastic market

Ph.DDOCTOR OF PHILOSOPH

Reducing transportation costs and inventory shrinkage in the Washington State tree fruit industry

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.Includes bibliographical references (leaves 91-95).Perishability and stock-outs are two sources of inventory inefficiency in the Washington State tree fruit industry. This thesis measures the size of these inefficiencies in terms of dollars per box, and describes five solutions, four qualitative and one quantitative, that seek to address them. To establish the magnitude of the inefficiencies, I regress various fruit characteristics on a set of sales data, thereby ascertaining the relationship between a fruit's price and its age. I find that the industry loses 5% to 12% of potential revenue due to perishability and propose four qualitative policies designed to reduce these losses. Next, I develop an operational management tool in the form of a mixed-integer optimization model which can be used to make optimal sourcing decisions during stock-out events. I find that the potential savings from improved sourcing decisions are between $0.01 and 0.02 per box. These results confirm that the costs and foregone revenue associated with inventory management are significant and merit the tree fruit industry's attention.by Jame Sterling Foreman.M.Eng.in Logistic