SEO Publications

Changeover minimization in the production of metal parts for car seats

Erik William Jeffries — Wed, 11 Dec 2024 02:35:54 +0000

Changeover minimization in the production of metal parts for car seats Erik William J… Tue, 12/10/2024 - 19:35

We tackle a capacitated lot-sizing and scheduling problem (CLSP) with the main objective of minimizing changeover time in the production of metal parts for car seats. Changeovers occur when a machine (or production line) is reconfigured to produce a different product or part, leading to production downtime and loss of efficiency. In this study, we first provide a mixed-integer programming (MIP) formulation of the problem. We test the limits of solving the problem with commercial mathematical programming software. We also propose two approaches to tackle instances found in practice for which the mathematical programming model is not a viable solution method. Both approaches are based on partitioning the entire production of a part into production runs (or work slots). In the first approach, the work slots are assigned to machines and sequenced by a metaheuristic that follows the search principles of the GRASP (greedy randomized adaptive procedure) and VNS (variable neighborhood search) methodologies. In the second approach, we develop a Hexaly Optimizer (formerly known as LocalSolver) model to assign and sequence work slots. The study provides insights into how to minimize changeovers and improve production efficiency in metal parts manufacturing for car seats. The findings of this study have practical implications for the auto-part manufacturing industry, where efficient and cost-effective production is critical to meet the demands of the market. • Mathematical model for a real-world industrial optimization problem. • Effective metaheuristic approach based on Variable Neighborhood Search. • Experiments include comparison with a model for general-purpose commercial optimizer. • Computational efficiency achieved with partial computation of the objective function.

Colmenar, J. Manuel; Laguna, Manuel; Martín-Santamaría, Raúl. Changeover minimization in the production of metal parts for car seats. Computers & Industrial Engineering. Dec2024, Vol. 198, pN.PAG-N.PAG.

https://www.sciencedirect.com/science/article/pii/S0360835224007563

Traditional

White

Appropriability risk and knowledge search on digital platforms

Erik William Jeffries — Wed, 11 Sep 2024 00:58:40 +0000

Appropriability risk and knowledge search on digital platforms Erik William J… Tue, 09/10/2024 - 18:58

Research has rarely studied how innovators conduct knowledge search in response to an increased risk that their original ideas may be imitated (i.e., duplicative imitation threat). We address this gap by focusing on a duplicative imitation threat common to digital platforms, which allows for the entry of pirated software at a low cost with rapid distribution and presents a significant appropriability risk to the original software developers. We treat the jailbreak of Apple's iOS 7 that enabled Apple users to install pirated apps as an exogenous shock that increases such a threat, and adopt a quasi-experiment design in our study. Our empirical analysis shows that after the jailbreak, iOS app developers increase their search depth and reduce their search scope compared to Android app developers. Our findings imply that innovators' adjustment in their knowledge search is contingent upon specific characteristics of the imitation threat they face. • There is limited research on how innovators conduct knowledge search in response to the risk of duplicative imitation. • We study the increasing threat of app piracy on Apple's App Store after iOS7 jailbreak. • We treat the jailbreak as an exogenous shock to the threat of duplicative imitation and adopt a quasi-experiment design. • We show that after the jailbreak, iOS app developers increase their search depth and reduce their search scope. • We suggest that changes in innovators' search behavior are contingent upon characteristics of the imitation threat.

Han, Nianchen; Zhang, Yuchen; Tong, Tony W. Appropriability risk and knowledge search on digital platforms. Research Policy. Sep2024, Vol. 53 Issue 7, pN.PAG-N.PAG.

https://www.sciencedirect.com/science/article/abs/pii/S0048733324000775

Traditional

White

Approximate linear programming for a queueing control problem

Erik William Jeffries — Tue, 10 Sep 2024 22:16:22 +0000

Approximate linear programming for a queueing control problem Erik William J… Tue, 09/10/2024 - 16:16

Admission decisions for loss systems accessed by multiple customer classes are a classical queueing control problem with a wide variety of applications. When a server is available, the decision is whether to admit an arriving customer and collect a lump-sum revenue. The system can be modeled as a continuous-time infinite-horizon dynamic program, but suffers from the curse of dimensionality when different customer classes have different service rates. We use approximate linear programming to solve the problem under three approximation architectures: affine, separable piecewise linear and finite affine. The finite affine approximation is a recently proposed generalization of the affine approximation, which allows for non-stationary parameters. For both affine and finite affine approximations, we derive equivalent, but more compact, formulations that can be efficiently solved. We propose a column generation algorithm for the separable piecewise linear approximation. Our numerical results show that the finite affine approximation can obtain the tightest bounds for 75% of the instances among the three approximations. Especially, when the number of servers is large and/or the load on the system is high, the finite affine approximation always achieves the tightest bounds. Regarding policy performance, the finite affine approximation has the best performance on average compared to the other two approximations and the achievable performance region method (Bertsimaset al., 1994, Kumar and Kumar, 1994). Furthermore, the finite affine approximation is 4 to 5 orders of magnitude faster than the achievable performance region method and the separable piecewise linear approximation for large-scale instances. Therefore, considering bounds, policy performance, and computational efficiency, the finite affine approximation emerges as a competitive approximation architecture for the class of problems studied here. • We use approximate LP to solve a queueing control problem under three approximation architectures. • We derive equivalent but more compact formulations for affine and finite affine approximations. • We propose a column generation algorithm for the separable piecewise linear approximation. • We show that the finite affine approximation can obtain the tightest bounds for 75% of the instances. • We show that the finite affine approximation has the best policy performance and efficiency.

Samiedaluie, Saied; Zhang, Dan; Zhang, Rui. Approximate linear programming for a queueing control problem. Computers & Operations Research. Sep2024, Vol. 169, pN.PAG-N.PAG.

https://www.sciencedirect.com/science/article/pii/S0305054824001837

Traditional

White

UCB-Type Learning Algorithms with Kaplan–Meier Estimator for Lost-Sales Inventory Models with Lead Times

Erik William Jeffries — Fri, 02 Aug 2024 00:19:37 +0000

UCB-Type Learning Algorithms with Kaplan–Meier Estimator for Lost-Sales Inventory Models with Lead Times Erik William J… Thu, 08/01/2024 - 18:19

Efficient Learning Algorithms for the Best Capped Base-Stock Policy in Lost Sales Inventory Systems Periodic review, lost sales inventory systems with lead times are notoriously challenging to optimize. Recently, the capped base-stock policy, which places orders to bring the inventory position up to the order-up-to level subject to the order cap, has demonstrated exceptional performance. In the paper "UCB-Type Learning Algorithms with Kaplan–Meier Estimator for Lost Sales Inventory Models with Lead Times," Lyu, Zhang, and Xin propose an upper confidence bound–type learning framework. This framework, which incorporates simulations with the Kaplan–Meier estimator, works with censored demand observations. It can be applied to determine the optimal capped base-stock policy with a tight regret with respect to the planning horizon and the optimal base-stock policy with a regret that matches the best existing result. Both theoretical analysis and extensive numerical experiments demonstrate the effectiveness of the proposed learning framework. In this paper, we consider a classic periodic-review lost-sales inventory system with lead times, which is notoriously challenging to optimize with a wide range of real-world applications. We consider a joint learning and optimization problem in which the decision maker does not know the demand distribution a priori and can only use past sales information (i.e., censored demand). Departing from existing learning algorithms on this learning problem that require the convexity property of the underlying system, we develop an upper confidence bound (UCB)-type learning framework that incorporates simulations with the Kaplan–Meier estimator and demonstrate its applicability to learning not only the optimal capped base-stock policy in which convexity no longer holds, but also the optimal base-stock policy with a regret that matches the best existing result. Compared with a classic multi-armed bandit problem, our problem has unique challenges because of the nature of the inventory system, because (1) each action has long-term impacts on future costs, and (2) the system state space is exponentially large in the lead time. As such, our learning algorithms are not naive adoptions of the classic UCB algorithm; in fact, the design of the simulation steps with the Kaplan–Meier estimator and averaging steps is novel in our algorithms, and the confidence width in the UCB index is also different from the classic one. We prove the regrets of our learning algorithms are tight up to a logarithmic term in the planning horizon T. Our extensive numerical experiments suggest the proposed algorithms (almost) dominate existing learning algorithms. We also demonstrate how to select which learning algorithm to use with limited demand data.

Lyu, Chengyi; Zhang, Huanan; Xin, Linwei. UCB-Type Learning Algorithms with Kaplan–Meier Estimator for Lost-Sales Inventory Models with Lead Times. Operations Research. Jul/Aug2024, Vol. 72 Issue 4, p1317-1332.

https://pubsonline.informs.org/doi/10.1287/opre.2022.0273

Traditional

White

Privacy-Preserving Personalized Revenue Management

Erik William Jeffries — Thu, 11 Jul 2024 01:09:10 +0000

Privacy-Preserving Personalized Revenue Management Erik William J… Wed, 07/10/2024 - 19:09

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm's pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost "for free." That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization.

Lei, Yanzhe; Miao, Sentao; Momot, Ruslan. Privacy-Preserving Personalized Revenue Management. Management Science. Jul2024, Vol. 70 Issue 7, p4875-4892.

https://pubsonline.informs.org/doi/10.1287/mnsc.2023.4925

Traditional

White

Causal inference under selection on observables in operations management research: Matching methods and synthetic controls

Erik William Jeffries — Thu, 11 Jul 2024 00:27:01 +0000

Causal inference under selection on observables in operations management research: Matching methods and synthetic controls Erik William J… Wed, 07/10/2024 - 18:27

The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as observational data lacks the benefit of random treatment assignment, estimating causal effects poses challenges. In the specific scenario where one can reasonably assume that all confounding factors are observed—referred to as selection on observables—matching methods and synthetic controls can assist researchers to replicate a randomized experiment, the most desirable setting for drawing causal inferences. In this paper, we first present an overview of matching methods and their utilization in the OM literature. Subsequently, we establish the framework and provide pragmatic guidance for propensity score matching and coarsened exact matching, which have garnered considerable attention in recent OM studies. Following this, we conduct a comprehensive simulation study that compares diverse matching algorithms across various scenarios, providing practical insights derived from our findings. Finally, we discuss synthetic controls, a method that offers unique advantages over matching techniques in specific scenarios and is expected to become even more popular in the OM field in the near future. We hope that this paper will serve as a catalyst for promoting a more rigorous application of matching and synthetic control methodologies.

Yılmaz, Övünç; Son, Yoonseock; Shang, Guangzhi; Arslan, Hayri A. Causal inference under selection on observables in operations management research: Matching methods and synthetic controls. Journal of Operations Management. Jul2024, Vol. 70 Issue 5, p831-859.

https://onlinelibrary.wiley.com/doi/10.1002/joom.1318

Traditional

White

Isolating the Effect of Social Risk on MNEs’ CSR Reporting: A New Approach Based on China’s Belt & Road Initiative

Erik William Jeffries — Tue, 11 Jun 2024 01:27:33 +0000

Isolating the Effect of Social Risk on MNEs’ CSR Reporting: A New Approach Based on China’s Belt & Road Initiative Erik William J… Mon, 06/10/2024 - 19:27

This article highlights CSR disclosure as a strategic response of Chinese multinational enterprises (MNEs) to the social risk they face in host countries. Deviating from prior research that aims to directly measure social risk, we offer a new approach to isolate the effect of social risk by leveraging China's Belt & Road Initiative (BRI) as the research context, under which Chinese MNEs are largely protected from political risk in membership countries but are exposed to substantial social risk from local nongovernment stakeholders. Results from difference-in-differences analyses show that after the enactment of the BRI, Chinese MNEs investing in BRI countries significantly increases their likelihood of CSR disclosure than that of their counterparts investing in non-BRI countries. Further, such effects are more pronounced for state-owned MNEs and MNEs in natural resource industries. This research enriches the international business literature on the relationship between political risk and social risk, and that between corporate political actions and corporate social responsibility.

Zhao, Jing; Zhu, Limin; He, Wenlong; Tong, Tony W. Isolating the Effect of Social Risk on MNEs' CSR Reporting: A New Approach Based on China's Belt & Road Initiative. Management & Organization Review. Jun2024, Vol. 20 Issue 3, p425-465.

Traditional

White

The Costs They Are a-Rising: Commercialization Costs and the Innovation Process in Drug Development.

Erik William Jeffries — Mon, 10 Jun 2024 19:47:01 +0000

The Costs They Are a-Rising: Commercialization Costs and the Innovation Process in Drug Development. Erik William J… Mon, 06/10/2024 - 13:47

Commercialization is a crucial phase in the innovation process and its associated costs significantly influence R&D decisions. Yet our understanding of how commercialization costs impact various stages of innovation remains underdeveloped. In this study, I investigate the effects of commercialization costs on early- and late-stages of the innovation process in a quasi- experimental setting. Specifically, I leverage sudden policy shifts in the US Food and Drug Administration (FDA) that increased commercialization costs for drugs in certain therapeutic areas. Employing a difference-in-differences methodology, I trace the impacts of these elevated costs on discovery and clinical trial advancement of 3,357 drug candidates between 1997 and 2015. My research places emphasis on the contrasting roles that startups and established firms have in innovation. My findings reveal that while commercialization costs diminish the late-stage efforts in commercializing innovations, especially by established firms, they stimulate an environment conducive to early-stage entrepreneurial drug discovery efforts. Furthermore, I find that the disruption that commercialization costs create in markets for technology drives these opposite findings: new discoveries remain without buyers in technology markets, failing to complete their development process as commercialized products.

Khoshsokhan, Sina. The Costs They Are a-Rising: Commercialization Costs and the Innovation Process in Drug Development. Academy of Management Annual Meeting Proceedings. 2024, Vol. 2024 Issue 1, p1-6.

https://journals.aom.org/doi/full/10.5465/AMPROC.2024.292bp

Traditional

White

Real Options: Connecting with Other Perspectives and Exploring New Frontiers.

Erik William Jeffries — Mon, 10 Jun 2024 19:16:35 +0000

Real Options: Connecting with Other Perspectives and Exploring New Frontiers. Erik William J… Mon, 06/10/2024 - 13:16

Real options have gained considerable traction among management scholars and practitioners, yet some challenges remain regarding their strategic value and appropriate use. This article seeks to add clarity to the nature and application of real options and suggests several emerging strategy contexts in which a real options approach can add value. First, we describe the nature of real options and discuss the value of real options with regard to various firm strategic initiatives. Second, we briefly highlight recent efforts to incorporate real options with other well-known and frequently used management perspectives. Finally, we propose several novel contexts that would benefit from the application of real options by scholars, managers, and policymakers: platform strategy, nonmarket strategy, and environmental shocks.

Chintakananda, Asda; McIntyre, David P.; Tong, Tony W. Real Options: Connecting with Other Perspectives and Exploring New Frontiers. Academy of Management Perspectives. Jun2024, p1-17.

Read the Full Article Here.

Traditional

White

Striking Out Swinging: Specialist Success Following Forced Task Inferiority

Erik William Jeffries — Thu, 11 Apr 2024 01:07:51 +0000

Striking Out Swinging: Specialist Success Following Forced Task Inferiority Erik William J… Wed, 04/10/2024 - 19:07

Organizing work around specialized professionals leverages their deep expertise and mastery of particular skills. However, as work becomes more flexible, organizations often require specialists to perform some work outside their specialization. These tasks, which distance specialists from the area of their greatest contribution, could diminish their performance by being distracting or tiring or by creating negative comparisons with others who are more proficient in that work. Contrary to these perspectives, we find robust evidence that specialists' performance can be enhanced, rather than diminished, after work outside their specialization. Using archival data from 22 years of Major League Baseball (MLB) games and interviews with former MLB players and coaches, we find that specialized players perform better in their specialty after obligatory tasks outside of their specialization. We argue that this occurs through a process we call forced task inferiority, in which underperformance in tasks outside their specialty frustrates specialists, generating heightened arousal and drive that they can channel into better performance in their specialty work. The results are robust to alternative mechanisms, such as tasks outside a specialist's area of specialization leading to learning, breaking monotony, or threatening the specialist's professional identity. This research advances knowledge about managing specialists and flexible work arrangements by showing that when tasks are particularly sequenced, specialists' performance can be enhanced, rather than diminished, by doing work outside their specialty.

Bond, Brittany; Poskanzer, Ethan. Striking Out Swinging: Specialist Success Following Forced Task Inferiority. Organization Science. Mar/Apr2024, Vol. 35 Issue 2, p698-718.

https://pubsonline.informs.org/doi/10.1287/orsc.2023.1680

Traditional

White

SEO Publications

Changeover minimization in the production of metal parts for car seats

Related Articles

Appropriability risk and knowledge search on digital platforms

Related Articles

Approximate linear programming for a queueing control problem

Related Articles

UCB-Type Learning Algorithms with Kaplan–Meier Estimator for Lost-Sales Inventory Models with Lead Times

Related Articles

Privacy-Preserving Personalized Revenue Management

Related Articles

Causal inference under selection on observables in operations management research: Matching methods and synthetic controls

Related Articles

Isolating the Effect of Social Risk on MNEs’ CSR Reporting: A New Approach Based on China’s Belt & Road Initiative

Related Articles

The Costs They Are a-Rising: Commercialization Costs and the Innovation Process in Drug Development.

Related Articles

Real Options: Connecting with Other Perspectives and Exploring New Frontiers.

Related Articles

Striking Out Swinging: Specialist Success Following Forced Task Inferiority

Related Articles