Prepositioning and Local Purchasing for Emergency Operations Under Budget, Demand, and Supply Uncertainty

2021 ◽  
Author(s):  
Mahyar Eftekhar ◽  
Jing-Sheng Jeannette Song ◽  
Scott Webster
Keyword(s):  
Optimal Solution ◽  
Rapid Onset ◽  
Problem Definition ◽  
Supply Uncertainty ◽  
Approximate Methods ◽  
Humanitarian Organizations ◽  
Solution Methods ◽  
Local Supply ◽  
Managerial Implications ◽  
The Impact

Problem definition: Considering a mix of prepositioning and local purchasing, common to cover humanitarian demands in the aftermath of a rapid-onset disaster, we propose policies to determine preposition stock. These formulations are developed in the presence of demand, budget, and local supply uncertainties and for single-items delivery. Academic/practical relevance: The immediate period aftermath of a disaster is the most crucial period during which humanitarian organizations must supply relief items to beneficiaries. Yet, because of many unknowns such as time, place, and magnitude of a disaster, supply management is a significant challenge, and these decisions are made intuitively. The features and complexities we examine have not been studied in the literature. Methodology: We derive properties of the optimal solution, identify exact solution methods, and determine approximate methods that are easy to implement. Results: We (i) characterize the interplay of supply, demand, and budget uncertainties, as well as the impact of product characteristics on optimal prepo stock levels; (ii) show in what conditions the prepo stock is a simple newsvendor solution; and (iii) discuss the value of emergency funds. Managerial implications: We show that budget level is a key determinant of the optimal policy. When it is above a threshold, inventory increases in disaster frequency and severity, but the reverse is true otherwise. When budget is limited, the rate of savings from improved forecasts is amplified (attenuated) for critical (noncritical) items, reflecting opposing directional effects of mismatch cost and cost of insufficient funding. Our model can also be used to estimate the value of initiatives to mitigate constraints on local spend (e.g., a line of credit underwritten by large donors that is available during the immediate relief period).

scholarly journals Nonbinding Goals in Teams: A Real Effort Coordination Experiment

2020 ◽  
Vol 22 (5) ◽  
pp. 1026-1044
Author(s):  
James Fan ◽  
Joaquín Gómez-Miñambres
Keyword(s):  
Energy Use ◽  
Weak Link ◽  
Human Subjects ◽  
Individual Performance ◽  
Problem Definition ◽  
Reference Points ◽  
Team Production ◽  
Team Members ◽  
Managerial Implications ◽  
The Impact

Problem definition: We investigate the impact of nonbinding (wage-irrelevant) goals, set by a manager, on a team of workers with “weak-link” production technology. Can nonbinding goals improve team production when team members face production complementarity? Academic/practical relevance: Nonbinding goals are easy to implement and ubiquitous in practice. These goals have been shown to improve individual performance, but it remains to be seen if such goals are effective in team production when there is production complementarity among workers. Methodology: We first develop a theoretical model where goals act as reference points for workers’ intrinsic motivation to complete the task. We then test our hypotheses in a controlled, human-subjects experiment. In our experiment, participants act as managers or workers, and we examine the impact of nonbinding goals on team outcomes. Results: Consistent with our model, we find evidence that team production does increase when managers are able to set goals. This effect is strongest when goals are challenging but attainable for weak-link workers. However, we also find evidence that many managers assign goals that are too challenging for weak-link workers, resulting in suboptimal team production, lower profits, and higher wasted performance (performance above the weak-link level). Managerial implications: Our analysis indicates that goals are effective motivators in teams, but some managers may have difficulty overcoming personal biases when setting goals. The task of setting team goals is more complex than setting individual goals, and many managers can benefit from training on how to set good goals for the team. Moreover, our finding that suboptimal goals also increase wasted performance suggests that improving goal-setting strategies is especially important in production settings where overperformance is costly for the firm (scrap, energy use, inventory costs, lower prices as a result of oversupply, etc.).

Shipping Consolidation Across Two Warehouses with Delivery Deadline and Expedited Options for E-commerce and Omni-channel Retailers

2020 ◽  
Author(s):  
Lai Wei ◽  
Roman Kapuscinski ◽  
Stefanus Jasin
Keyword(s):  
Economies Of Scale ◽  
Optimal Solution ◽  
Problem Definition ◽  
Shipment Consolidation ◽  
On Demand ◽  
Numerical Tests ◽  
Optimal Policies ◽  
Shipping Costs ◽  
Multiple Orders ◽  
Managerial Implications

Problem definition: Shipment consolidation (i.e., shipping multiple orders together instead of shipping them separately) is commonly used to decrease total shipping costs. However, when the delivery of some orders is delayed, so they can be consolidated with future orders, a more expensive expedited shipment may be needed to meet shorter deadlines. In this paper, we study the optimal consolidation policy focusing on the trade-off between economies of scale due to combining orders and expedited shipping costs, in the setting of two warehouses. Academic/practical relevance: Our work is motivated by the application of fulfillment consolidation in e-commerce and omni-channel retail, especially with the rise of so-called on-demand logistics services. Sellers have the flexibility to take advantage of consolidation by deciding when to ship the orders and from which warehouse to fulfill the orders, as long as the orders’ deadlines are met. Methodology: We use Dynamic Programming to study the optimal policy and its structure. We also conduct extensive simulation tests to evaluate the performance of heuristics that are based on structures of the optimal policies. Results: The optimal policies and their structures are characterized. Using the insights of these structural properties, we propose two easily implementable heuristics that perform within 1%–2% of the optimal solution and outperform other benchmark consolidation methods in numerical tests. Managerial implications: Consolidation is shown to significantly reduce the outbound shipping costs. Retailers can take advantage of it to effectively improve the standard policies by simply applying the threshold-form heuristics we propose.

AI and Procurement

2021 ◽  
Author(s):  
Ruomeng Cui ◽  
Meng Li ◽  
Shichen Zhang
Keyword(s):  
Price Discrimination ◽  
Recommendation System ◽  
Wholesale Price ◽  
Problem Definition ◽  
Practical Relevance ◽  
Trading Company ◽  
Smart Control ◽  
Managerial Implications ◽  
The Impact ◽  
Price Quotes

Problem definition: In this research, we study how buyers’ use of artificial intelligence (AI) affects suppliers’ price quoting strategies. Specifically, we study the impact of automation—that is, the buyer uses a chatbot to automatically inquire about prices instead of asking in person—and the impact of smartness—that is, the buyer signals the use of a smart AI algorithm in selecting the supplier. Academic/practical relevance: In a world advancing toward AI, we explore how AI creates and delivers value in procurement. AI has two unique abilities: automation and smartness, which are associated with physical machines or software that enable us to operate more efficiently and effectively. Methodology: We collaborate with a trading company to run a field experiment on an online platform in which we compare suppliers’ wholesale price quotes across female, male, and chatbot buyer types under AI and no recommendation conditions. Results: We find that, when not equipped with a smart control, there is price discrimination against chatbot buyers who receive a higher wholesale price quote than human buyers. In fact, without smartness, automation alone receives the highest quoted wholesale price. However, signaling the use of a smart recommendation system can effectively reduce suppliers’ price quote for chatbot buyers. We also show that AI delivers the most value when buyers adopt automation and smartness simultaneously in procurement. Managerial implications: Our results imply that automation is not very valuable when implemented without smartness, which in turn suggests that building smartness is necessary before considering high levels of autonomy. Our study unlocks the optimal steps that buyers could adopt to develop AI in procurement processes.

Supplier Centrality and Auditing Priority in Socially Responsible Supply Chains

2020 ◽  
Vol 22 (6) ◽  
pp. 1199-1214 ◽  
Author(s):  
Jiayu Chen ◽  
Anyan Qi ◽  
Milind Dawande
Keyword(s):  
Child Labor ◽  
Problem Definition ◽  
Socially Responsible ◽  
Supply Networks ◽  
Game Theoretic Analysis ◽  
Managerial Implications ◽  
Game Theoretic ◽  
Negative Publicity ◽  
The Impact ◽  
Number Of Firms

Problem definition: A key question in socially responsible supply networks is as follows: When firms audit some, but not all, of their respective suppliers, how do the degree centralities of the suppliers (i.e., the number of firms to which they supply) affect their auditing priority from the viewpoint of the firms? To investigate, we consider an assembly network consisting of two firms and three suppliers; each firm has one independent supplier that uniquely supplies to that firm and one common supplier that supplies to both. Academic/practical relevance: Most supply networks are characterized by firms that source from multiple suppliers and suppliers that serve multiple firms, thus resulting in suppliers who differ in their degree centrality. In such networks, any negative publicity from suppliers’ noncompliance with socially responsible practices—for example, employment of child labor, unsafe working conditions, and excessive pollution—can significantly damage the reputation of the buying firms. To mitigate this impact, firms preemptively audit suppliers although resource and time considerations typically restrict the number of suppliers a firm can audit. Consequently, it becomes important to understand the impact of the degree centralities of the suppliers on the priority with which firms audit them. Methodology: Game-theoretic analysis. Results: Downstream competition between the firms drives them away from auditing the supplier with higher centrality, that is, the common supplier, in equilibrium, despite the fact that auditing this supplier is better for the aggregate profit of the firms. We show that this inefficiency is corrected when the firms cooperate (via a stable coalition) to jointly audit the suppliers and share the auditing cost in a fair manner. We also identify conditions under which joint auditing improves social welfare. Managerial implications: We have two main messages: (i) individual incentives can lead firms to deprioritize the auditing of structurally important suppliers, which is inefficient; (ii) the practice of joint auditing can correct this inefficiency.

Cross-Category Retailing Management: Substitution and Complementarity

2021 ◽  
Author(s):  
Chenxu Ke ◽  
Ruxian Wang
Keyword(s):  
Choice Model ◽  
Problem Definition ◽  
Substitution Effects ◽  
Sequential Choice ◽  
Secondary Products ◽  
Estimation Algorithms ◽  
Managerial Implications ◽  
Complementarity Effects ◽  
Sequential Choice Model ◽  
The Impact

Problem definition: This paper studies pricing and assortment management for cross-category products, a common practice in brick-and-mortar retailing and e-tailing. Academic/practical relevance: We investigate the complementarity effects between the main products and the secondary products, in addition to the substitution effects for products in the same category. Methodology: In this paper, we develop a multistage sequential choice model, under which a consumer first chooses a main product and then selects a secondary product. The new model can alleviate the restriction of the independence of irrelevant alternatives property and allows more flexible substitution patterns and also takes into account complementarity effects. Results: We characterize the impact of the magnitude of complementarity effects on pricing and assortment management. For the problems that are hard to solve optimally, we propose simple heuristics and establish performance guarantee. In addition, we develop easy-to-implement estimation algorithms to calibrate the proposed sequential choice model by using sales data. Managerial implications: We show that ignoring or mis-specifying complementarity effects may lead to substantial losses. The methodologies on modeling, optimization, and estimation have potential to make an impact on cross-category retailing management.

Unraveling Behavioral Ordering: Relative Costs and the Bullwhip Effect

2021 ◽  
Author(s):  
Brent B. Moritz ◽  
Arunachalam Narayanan ◽  
Chris Parker
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Human Behavior ◽  
Bullwhip Effect ◽  
Problem Definition ◽  
Supply Chain Costs ◽  
Cost Impact ◽  
Managerial Implications ◽  
The Cost ◽  
The Impact

Problem definition: We study the bullwhip effect and analyze the impact of human behavior. We separate rational ordering in response to increasing incoming orders from irrational ordering. Academic/practical relevance: Prior research has shown that the bullwhip effect occurs in about two-thirds of firms and impacts profitability by 10%–30%. Most bullwhip mitigation efforts emphasize processes such as information sharing, collaboration, and coordination. Previous work has not been able to separate the impact of behavioral ordering from rational increases in order quantities. Methodology: Using data from a laboratory experiment, we estimate behavioral parameters from three ordering models. We use a simulation to evaluate the cost impact of bullwhip behavior on the supply chain and by echelon. Results: We find that cost increases are not equally shared. Human biases (behavioral ordering) at the retailer results in higher relative costs elsewhere in the supply chain, even as similar ordering by a wholesaler, distributor, or factory results in increased costs within that echelon. These results are consistent regardless of the behavioral models that we consider. The cognitive profile of the decision maker impacts both echelon and supply chain costs. We show that the cost impact is higher as more decision makers enter a supply chain. Managerial implications: The cost of behavioral ordering is not consistent across the supply chain. Managers can use the estimation/simulation framework to analyze the impact of human behavior in their supply chains and evaluate improvement efforts such as coordination or information sharing. Our results show that behavioral ordering by a retailer has an out-sized impact on supply chain costs, which suggests that upstream echelons are better placed to make forecasting and replenishment decisions.

Introducing Autonomous Vehicles: Adoption Patterns and Impacts on Social Welfare

2021 ◽  
Author(s):  
Opher Baron ◽  
Oded Berman ◽  
Mehdi Nourinejad
Keyword(s):  
Social Welfare ◽  
Autonomous Vehicles ◽  
Positive Impact ◽  
Network Connectivity ◽  
Optimal Level ◽  
Problem Definition ◽  
Travel Times ◽  
Managerial Implications ◽  
Sharing Patterns ◽  
The Impact

Problem definition: Autonomous vehicles (AVs) are predicted to enter the consumer market in less than a decade. There is currently no consensus on whether their presence will have a positive impact on users and society. The skeptics of automation foresee increased congestion, whereas the advocates envision smoother traffic with shorter travel times. We study the automation controversy and advise policymakers on how and when to promote AVs. Academic/practical relevance: The AV technology is advancing rapidly and there is a need to study its impact on social welfare and the likelihood of its adoption by the public. Methodology: We use supply-demand theory to find the equilibrium number of trips for autonomous and regular households. We develop a simulation model of peer-to-peer AV sharing. We compare the socially optimal level of automation with the selfish adoption patterns where households independently choose their vehicle type. Results: We establish that the optimal social welfare is influenced by: (i) the network connectivity, that is, the ability of the infrastructure to serve AVs, (ii) the additional comfort provided by AVs that allows passengers to engage in other productive activities instead of driving, and (iii) the AV sharing patterns that reduce ownership costs, but create empty vehicle trips that increase congestion. Managerial implications: We investigate the impact of AVs in a case study of Toronto and show that partial automation maximizes social welfare. We show that the comfort of AVs may add traffic that compromises social welfare. Moreover, although traffic increases with automation, travel times may decrease because of significant improvements in traffic flow caused by AV connectivity in the network.

Setting Retail Staffing Levels: A Methodology Validated with Implementation

2020 ◽  
Author(s):  
Marshall Fisher ◽  
Santiago Gallino ◽  
Serguei Netessine
Keyword(s):  
Historical Analysis ◽  
Problem Definition ◽  
Labor Management ◽  
Staffing Level ◽  
Staffing Levels ◽  
Industry Practice ◽  
Managerial Implications ◽  
The Individual ◽  
The Right ◽  
The Impact

Problem definition: How should retail staffing levels be set? While cost of labor is well understood, the revenue implications of having the right staffing level are hard to estimate. Moreover, these implications vary by store; hence, staffing levels should vary as well. Academic/practical relevance: We provide a novel method for setting store associate staffing at the individual store level. We discuss a field implementation that tested this methodology. Methodology: We use historical data on revenue and planned and actual staffing levels by store to estimate how revenue varies with the staffing level at each store. We disentangle the endogeneity between revenue and staffing levels by focusing on randomly occurring deviations between planned and actual labor. Using historical analysis as a guide, we validate these results by changing the staffing levels in a few test stores. We implement the results chain-wide and measure the impact in a large specialty retailer. Results: We find that the implementation validates predictions of the historical analysis. The implementation in 168 stores over six months produces a 4.5% revenue increase and a nearly $7.4 million annual profit increase. The impact of staffing level on revenue varies greatly by store. Managerial implications: Our paper makes three contributions to academic literature and to retail practice. First, we describe a process by which retailers can improve the most common industry practice: set store labor to be proportional to forecasted store revenue. Our proposed approach systematically sets the labor level in each store. Second, we demonstrate the effectiveness of that process via a field test and then via chain-wide implementation over a six-month time period. Finally, most retailers set store labor at the same level across stores, proportionate to revenue. We show that this is not the best approach because the revenue impact of store labor varies by store. The stores in our study that could benefit from relatively more labor were those with high potential demand, closely located competition for that demand, and experienced store managers. Overall, we provide the first simple but rigorous, field-tested approach that any retailer can use to increase revenue and profitability through better labor management.

Competition Between Two-Sided Platforms Under Demand and Supply Congestion Effects

2020 ◽  
Author(s):  
Fernando Bernstein ◽  
Gregory A. DeCroix ◽  
N. Bora Keskin
Keyword(s):  
Service Providers ◽  
Supply And Demand ◽  
Relative Number ◽  
Sharing Economy ◽  
Problem Definition ◽  
Ride Sharing ◽  
Managerial Implications ◽  
The Impact ◽  
Number Of Customers ◽  
Congestion Effects

Problem definition: This paper explores the impact of competition between platforms in the sharing economy. Examples include the cases of Uber and Lyft in the context of ride-sharing platforms. In particular, we consider competition between two platforms that offer a common service (e.g., rides) through a set of independent service providers (e.g., drivers) to a market of customers. Each platform sets a price that is charged to customers for obtaining the service provided by a driver. A portion of that price is paid to the driver who delivers the service. Both customers’ and drivers’ utilities are sensitive to the payment terms set by the platform and are also sensitive to congestion in the system (given by the relative number of customers and drivers in the market). We consider two possible settings. The first one, termed “single-homing,” assumes that drivers work through a single platform. In the second setting, termed “multihoming” (or “multiapping,” as it is known in practice), drivers deliver their service through both platforms. Academic/practical relevance: This is one of the first papers to study competition and multihoming in the presence of congestion effects typically observed in the sharing economy. We leverage the model to study some practical questions that have received significant press attention (and stirred some controversies) in the ride-sharing industry. The first involves the issue of surge pricing. The second involves the increasingly common practice of drivers choosing to operate on multiple platforms (multihoming). Methodology: We formulate our problem as a pricing game between two platforms and employ the concept of a Nash equilibrium to analyze equilibrium outcomes in various settings. Results: In both the single-homing and multihoming settings, we study the equilibrium prices that emerge from the competitive interaction between the platforms and explore the supply and demand outcomes that can arise at equilibrium. We build on these equilibrium results to study the impact of surge pricing in response to a surge in demand and to examine the incentives at play when drivers engage in multihoming. Managerial implications: We find that raising prices in response to a surge in demand makes drivers and customers better off than if platforms were constrained to charge the same prices that would arise under normal demand levels. We also compare drivers’ and customers’ performance when all drivers either single-home or multihome. We find that although individual drivers may have an incentive to multihome, all players are worse off when all drivers multihome. We conclude by proposing an incentive mechanism to discourage multihoming.

scholarly journals Budgeting in International Humanitarian Organizations

2021 ◽  
Author(s):  
Milad Keshvari Fard ◽  
Ivana Ljubić ◽  
Felix Papier
Keyword(s):  
Utility Function ◽  
Operations Management ◽  
Benders Decomposition ◽  
International Committee ◽  
Problem Definition ◽  
Red Cross ◽  
Solution Approach ◽  
Humanitarian Organizations ◽  
Managerial Implications ◽  
Using Data

Problem definition: International humanitarian organizations (IHOs) prepare a detailed annual allocation plan for operations that are conducted in the countries they serve. The annual plan is strongly affected by the available financial budget. The budget of IHOs is derived from donations, which are typically limited, uncertain, and to a large extent earmarked for specific countries or programs. These factors, together with the specific utility function of IHOs, render budgeting for IHOs a challenging managerial problem. In this paper, we develop an approach to optimize budget allocation plans for each country of operations. Academic/practical relevance: The current research provides a better understanding of the budgeting problem in IHOs given the increasing interest of the operations management community for nonprofit operations. Methodology: We model the problem as a two-stage stochastic optimization model with a concave utility function and identify a number of analytical properties for the problem. We develop an efficient generalized Benders decomposition algorithm as well as a fast heuristic. Results: Using data from the International Committee of the Red Cross, our results indicate 21.3% improvement in the IHO’s utility by adopting stochastic programming instead of the expected value solution. Moreover, our solution approach is computationally more efficient than other approaches. Managerial implications: Our analysis highlights the importance of nonearmarked donations for the overall performance of IHOs. We also find that putting pressure on IHOs to fulfill the targeted missions (e.g., by donors or media) results in lower beneficiaries’ welfare. Moreover, the IHOs benefit from negative correlation among donations. Finally, our findings indicate that, if donors allow the IHO to allocate unused earmarked donations to other delegations, the performance of the IHO improves significantly.

Sign in / Sign up

Export Citation Format

Share Document