Managers and Students as Newsvendors - How Out-of-Task Experience Matters

We compare how freshmen business students, graduate business students and experienced procurement managers perform on a simple inventory ordering task. We find that, qualitatively, managers exhibit ordering behavior similar to students, including biased ordering towards average demand. Experience, however, affects subjects’ utilization of information. The managers’ work experience seems most valuable when there is only historical demand data to guide decision making, while students better utilize analytical information and task training. As a result, when information necessary to solve the problem to optimality is added to historical information, students catch up to the managers, and students with classroom experience in operations management outperform managers.

On-demand last-mile distribution network design with omnichannel inventory

E-commerce delivery deadlines are getting increasingly tight, driven by a growing ‘I-want-it-now’ instant gratification mindset of consumers and the desire of online and omnichannel retailers to capitalize on the growth of on-demand e-commerce. On-demand deliveries with delivery deadlines as tight as one or two hours force companies to rethink their last-mile distribution network, since tight delivery deadlines require decentralization of order picking and inventory holding to ensure close proximity to consumers. This fundamentally changes the strategic design process of last-mile distribution networks. We study the impact of incorporating inventory order-up-to level decisions into the strategic design process of last-mile distribution networks with tight delivery deadlines. We develop an approximate inventory model by including an estimate of the cost of late delivery and additional transportation due to local stock-outs in a newsvendor formulation. Such local stock-outs require an order to be delivered from a more distant facility, which may lead to late delivery and additional transportation cost. We integrate our approximate inventory model and a location-allocation mixed-integer program that determines optimal facility locations, associated order-up-to inventory levels, and fleet composition, into a metamodel simulation-based optimization approach. Our numerical analyses demonstrate that pooling the additional online inventory with brick-and-mortar (B&M) inventories leads to cannibalization by the B&M network and higher B&M service levels. However, the pooling benefits to the online network outweigh the cost of inventory cannibalization. Furthermore, we show under which circumstances omnichannel retailers may have an incentive to consolidate online inventory in specific B&M facilities

Models of sales assignment to maximize the profits from an economic point of view in FMCG

The sales assignment by manufacturers is an important part of the supply chain at the micro level. It is the most obvious manifestation of dynamism. Also, it has a significant impact on improving the efficiency of the entire supply chain. However, market demand is often uncertain. It makes it more difficult to allocate orders. A typical example of this situation always appears in FMCG (Fast Moving Consumer Goods). Customers are often willing to pay more time to wait with advanced payments compared to other products, such as mobiles, luxury goods and cars. In response, manufacturers often use marketing strategies such as pre-ordering and creating waiting list to gather information. It is a good way of alleviating the information mismatch between demand and production in the supply chain. However, it is not the same situation with cosmetics, daily necessities, and food. They are characterized by fierce competition between manufacturers, high replaceability and relatively open market price information. Therefore, in Fast Moving Consumer Goods industry, manufacturers usually use proportional allocation as the main principle to ensure that resources are maximised at each link in the supply chain. In this project, how managers make centralised decisions through a model if they have all the information will be discussed. They will not only decide how to allocate products quickly, but also get a theoretical maximum value of supply chain profits. Based on the mass nature of the goods in this industry, the sensitivity analysis will be also mentioned to validate its reasonablene

Online marketing:When to offer a refund for advanced sales

Advance selling is a marketing strategy commonly used by online retailers to increase sales by exploiting consumer valuation uncertainty. Recently, some online retailers have started to allow refunds on products sold in advance. On the one hand this reduces the net advance sales, but on the other hand it allows a higher advance sales price. This research is the first to explore the overall effect of allowing a refund on profits from advance sales, identifying conditions where advance selling with or without refunds (or no advance selling at all) is best. We analytically compare the profits of three advance selling strategies: none, without refund, and with refund. We show that selling in advance and allowing a refund is optimal for products with a relatively small profit margin and small strategic market size, and that the added profit can be considerable. Our results guide managers in selecting the right advance selling strategy. To facilitate this, we graphically display, based on the two dimensions of regular profit margin and strategic market size, under what conditions the different strategies are optimal

Dynamic Stochastic Inventory Management in E-Grocery Retailing: The Value of Probabilistic Information

Inventory management optimisation in a multi-period setting with dependent demand periods requires the determination of replenishment order quantities in a dynamic stochastic environment. Retailers are faced with uncertainty in demand and supply for each demand period. In grocery retailing, perishable goods without best-before-dates further amplify the degree of uncertainty due to stochastic spoilage. Assuming a lead time of multiple days, the inventory at the beginning of each demand period is determined jointly by the realisations of these stochastic variables. While existing contributions in the literature focus on the role of single components only, we propose to integrate all of them into a joint framework, explicitly modelling demand, supply shortages, and spoilage using suitable probability distributions learned from historic data. As the resulting optimisation problem is analytically intractable in general, we use a stochastic lookahead policy incorporating Monte Carlo techniques to fully propagate the associated uncertainties in order to derive replenishment order quantities. We develop a general inventory management framework and analyse the benefit of modelling each source of uncertainty with an appropriate probability distribution. Additionally, we conduct a sensitivity analysis with respect to location and dispersion of these distributions. We illustrate the practical feasibility of our framework using a case study on data from a European e-grocery retailer. Our findings illustrate the importance of properly modelling stochastic variables using suitable probability distributions for a cost-effective inventory management process

Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs.Manufacturing;Revenue Management;Software;Advanced Planning Systems;Demand Fulfillment

Demand Estimation at Manufacturer-Retailer Duo: A Macro-Micro Approach

This dissertation is divided into two phases. The main objective of this phase is to use Bayesian MCMC technique, to attain (1) estimates, (2) predictions and (3) posterior probability of sales greater than certain amount for sampled regions and any random region selected from the population or sample. These regions are served by a single product manufacturer who is considered to be similar to newsvendor. The optimal estimates, predictions and posterior probabilities are obtained in presence of advertising expenditure set by the manufacturer, past historical sales data that contains both censored and exact observations and finally stochastic regional effects that cannot be quantified but are believed to strongly influence future demand. Knowledge of these optimal values is useful in eliminating stock-out and excess inventory holding situations while increasing the profitability across the entire supply chain. Subsequently, the second phase, examines the impact of Cournot and Stackelberg games in a supply-chain on shelf space allocation and pricing decisions. In particular, we consider two scenarios: (1) two manufacturers competing for shelf space allocation at a single retailer, and (2) two manufacturers competing for shelf space allocation at two competing retailers, whose pricing decisions influence their demand which in turn influences their shelf-space allocation. We obtain the optimal pricing and shelf-space allocation in these two scenarios by optimizing the profit functions for each of the players in the game. Our numerical results indicate that (1) Cournot games to be the most profitable along the whole supply chain whereas Stackelberg games and mixed games turn out to be least profitable, and (2) higher the shelf space elasticity, lower the wholesale price of the product; conversely, lower the retail price of the product, greater the shelf space allocated for that product

The Impact of Green Metrics on Inventory Transshipment

Green is associated with life and is becoming increasingly engrained in not just life, but the way people do business as well. In recent years, a growing number of business operations have adopted various green metrics to limit their carbon footprints and environmental pollution, drive sustainable operations, contribute more to sustainability projects, and appear more socially responsible within the industry and host communities. While these initiatives target reducing carbon footprints, their impact on daily operations in a sharing economy is yet to be explored. In this thesis, I performed a thorough review of green supply chains, recent green practices, and metrics adopted in various organizations, followed by a comparative study to analyze the impact of operational decisions in inventory and transshipment when green metrics are considered. I extended the classical inventory transshipment model with two newsvendors’ retailers by allowing the retailers to incorporate direct or indirect green metrics as part of the objective function. In this setting, I explored three central research questions: 1) How would the adoption of green metrics impact the expected profit and equilibrium order quantities under inventory transshipment? 2) Would green metrics negatively or positively impact the coordinating transshipment prices? 3) What is the impact of direct vs. indirect green metrics on expected profit and equilibrium order quantities? Based on extensive numerical simulation, I find that when the profit margin is high, the impact of green metrics is limited—there is almost no change to a slight decrease in expected profit and the equilibrium order quantity when green metrics are considered. However, when the profit margin is low, the green metrics may improve the expected profits while reducing equilibrium order quantities. Interestingly, introducing green metrics does not affect coordinating transshipment prices, irrespective of profit margins. Direct versus indirect metrics have a limited impact on equilibrium order quantity and expected profit. My study contributes to the research by identifying the operational benefits of adopting green metrics. As an extension, this work may create a foundation for further work to determine the cost and benefits of implementing green metrics in practice and the key trade-offs in sustainability or social responsibility