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Synchromodal replenishment under non-stationary demand: An illustrative case study
No full textSynchromodal replenishment aligns transport mode decisions with inventory replenishment needs. We present a case study considering the simultaneous use of road and rail transport to replenish a distribution center in Belgium from a supplier in Spain, aiming for a modal shift from road to sustainable rail transport. Product demand is non-stationary, meaning the demand distribution changes over time. Although the underlying demand distribution is not directly observable, demand observations provide partial information. We apply the synchromodal replenishment policy proposed in Yee et al. (2024) that combines a committed, stable rail order with flexible short-term orders on rail and road. The short-term orders are based on inventory levels and partial information about the non-stationary demand. The case study demonstrates the value of adding short-term flexibility to rail orders to induce a modal shift. Our analysis shows how the proposed policy improves the modal shift compared to a benchmark policy without flexible rail orders. The retailer can reduce the carbon footprint of its replenishments without compromising service levels or costs. We also show how offering the flexible rail option increases the rail operator’s revenues. These findings highlight the potential of synchromodal replenishment with flexible rail orders to facilitate a modal shift
Mitigating the effects of global disruptions on supply chains: gaining insights from the dairy industry during Covid-19
No full textThis paper explores the dairy industry during peak Covid-19 disruption. Institutional theory is applied as a lens to investigate resilience factors and capacities at macro (industry), meso (supply chain) and micro (firm) levels. The methodology comprised four stages: in-depth interviews, retrospective literature review, intercoder reliability assessment, and a Delphi panel. Findings demonstrate execution of embedded resilience capacities but also the need for the dairy industry as an institution to dynamically adapt for advanced resilience. At the macro level, technology is highlighted as a critical resilience capacity. At the meso level, findings revealed that both lean and agile were key resilience capacities, exhibited by high levels of coordination, co-dependence, and communication. At the micro level, capacities such as ability to manage risk, skilled workforce, levels of automation, and financial stability were evident. Definition of these capacities and explanation of their adoption through an institutional theoretical lens delivers important contributions for advanced resilience
Leveraging machine learning for strategic performance management
No full textThis dissertation investigates the use of machine learning (ML) in strategic performance management. While ML applications have been widely explored in financial accounting, their use in management accounting remains relatively underexamined. This research aims to fill this gap by demonstrating how ML can provide in different stages of strategic performance management, including identifying strategic groups, performance measurement and resource allocation. The first chapter explores the potential of ML algorithms to mitigate cognitive biases that managers face when analyzing performance data and making strategic resource allocation decisions. Through a computer-simulated business game, this study compares the effectiveness of ML-based budget allocation against human decision-making. The findings indicate that ML algorithms significantly outperform human participants in optimizing budget allocations, leading to improved organizational value creation. However, the results also highlight the complementary nature of ML and human strategic reasoning. While ML efficiently processes large datasets and uncovers complex, nonlinear relationships, human expertise is needed to align the resource allocation with broader strategic objectives. The second chapter applies an unsupervised learning approach to develop a more nuanced classification of business strategies in the airline industry. Existing research typically categorizes airlines into either focused or full-service strategies. However, recent industry trends suggest that some airlines are adopting hybrid strategies that blend elements of both approaches. Using fuzzy clustering, this study identifies such hybrid airlines and evaluates their performance relative to pure strategic positions. The results reveal that hybrid airlines often achieve superior financial performance, but only when they effectively manage their capacity utilization. If they fail to leverage their increased complexity into a better use of their capacity, the benefits dissapear. The third chapter leverages supervised learning techniques to examine the relationship between nonfinancial performance measures and profitability in the airline industry. By applying ML methods, this study takes an exploratory approach to identify key performance indicators that predict airline profitability, taking into account interactions and nonlinearities. The findings suggest that operational efficiency measures, such as load factors, labor productivity, and fuel consumption , are the strongest predictors of financial success. Moreover, the study uncovers interaction effects, such as the moderating impact of capacity utilization on service failures and a U-shaped relationship between customer complaints and profitability. These results highlight the importance of considering both direct and indirect effects of performance metrics in strategic decision-making. By integrating ML techniques into strategic performance management, this dissertation contributes to the management accounting literature by showcasing ML's ability to uncover hidden patterns, enhance decision-making, and optimize resource allocation
How strategic is your sustainability strategy? Really?
No full textSustainability has become an important topic on the strategic agenda of most firms. Business is facing great demands from all sorts of stakeholders today due to the world’s enormous societal challenges: climate change and its consequences (like water scarcity), social inequality and injustice, poverty, depletion of natural resources, to name just a few. And people are expecting businesses to play a bigger role in addressing these societal problems. Firms have responded by setting up numerous sustainability initiatives – often captured under the label of ESG (Environment, Social, and Governance). But for many firms, the journey towards becoming more sustainable is a tough one. Despite good intentions, the implementation of corporate sustainability programmes has been slow at best, and sloppy and ineffective at worst. We believe that a major reason firms struggle to transform towards sustainability is that these sustainability programmes are insufficiently embedded in the company’s core strategy. In this paper, we analyse why this is a problem and what managers can do about it. More
specifically, we propose a new approach to managing your sustainability initiatives, one that is more grounded in strategy
Schedule risk analysis for project control with risk interactions
No full textDue to the uncertainty and risks in projects, Schedule Risk Analysis (SRA) has been developed to measure the activity sensitivity for taking corrective actions during project control. With the growing complexity of projects, the interaction between risks has received increasing research attention in project management, conceptualized as a risk interaction network. In such a network, the risk can affect the project objective not only through its occurrence but also by triggering interrelated risks. However, previous studies on SRA were mostly dedicated to designing activity sensitivity metrics using the project network, ignoring the source of activity risks, which may result in inaccurate activity sensitivity information and invalid corrective actions. In this paper, we integrate the risk interaction network with the project network for schedule risk analysis and project control under risk interactions. Subsequently, a novel interaction-oriented sensitivity metric is proposed and a simulation-based control model is developed to facilitate corrective action decisions. Finally, a computational experiment is conducted to investigate the ability of the proposed metric in identifying sensitive activities under risk interaction. In addition, the experiment also tests the performance of three activity selection strategies, namely preventive strategy, interventive strategy, and hybrid strategy. The results show the proposed activity sensitivity metric not only enhances the control effectiveness but also exhibits remarkable reliability across both serial projects and parallel projects. The results also indicate that a mix of strategies is preferred depending on the project network structure and the timing of control action taking.(China Scholarship Council
Linking outcomes to costs: A unified measure to advance value-based healthcare
No full text4-step roadmap using DEA to quantify patient value, validated using real-life data.. Multi-dimensional outcomes linked to granular cost data without subjective weights.
Individual value scores can compare value across time, providers and treatments. Clustering and econometric analyses inform value-driven healthcare strategies. Novel approach is adaptable across medical domains.Guided by the Value-Based Healthcare framework, the healthcare sector increasingly aims to maximize patient value by improving the quality of care while containing costs, which requires aligning the interests of patients, health providers and payers. This study addresses the need for advanced patient value measurement techniques to navigate this complex balance by introducing a four-step framework that combines Data Envelopment Analysis (DEA) and Time-Driven Activity-Based Costing. The framework starts by defining Decision-Making Units and specifying the treatment pathway (Step 1), followed by selecting the relevant inputs (i.e., costs) and outputs (i.e., health outcomes) (Step 2). Next, the DEA model is tailored to fit the specific medical context (Step 3), ultimately translating the value equation into unified, individual value scores that rank patients by perceived value (Step 4). Unlike traditional healthcare evaluations, the multiple health outcomes are connected to granular costing information without relying on monetary values or subjective weighting. Using real-life data from a case study focused on psoriasis, we demonstrate that value assessments significantly differ when considering a comprehensive set of health outcomes, rather than relying on a single primary outcome or treating costs and outcomes separately. These holistic value scores are used to pinpoint inefficiencies on an individual level, analyse patterns of health improvements through cluster analysis, and assess the impact of contextual variables on value creation using econometric analysis. Our results revealed the complex interplay between outcomes and costs by identifying factors like the presence of comorbidities, which had no direct influence on costs or outcomes, as overall value driver. In , this research proposes an intuitive metric for value benchmarks across time, health providers and treatments, ultimately contributing to the effective delivery of personalized and value-based healthcare
Duration forecasting in resource constrained projects: A hybrid risk model combining complexity indicators with sensitivity measures
No full textThis study combines complexity measures from the project scheduling literature and sensitivity measures from the risk analysis literature to improve project duration forecasts in resource constrained projects. A hybrid risk model is proposed incorporating project network measures, resource-related indicators, and risk sensitivity metrics. The hybrid risk model is then used for forecasting the duration of unseen projects. The study contributes to the existing literature by integrating newly proposed activity sensitivity metrics and network and resource related indicators in project forecasting. Additionally, it conducts a large-scale experiment to compare the accuracy of the hybrid risk model against benchmark methods, including Monte Carlo simulations and relevant machine learning algorithms. The results show that inclusion of resource-related variables within the hybrid risk model significantly improves the accuracy, validating recently proposed metrics. The hybrid risk model outperforms most of the benchmark methods in high-uncertainty projects, emphasizing the importance of accurately estimating the flexibility in activity start times. Furthermore, the hybrid risk model of this paper is particularly effective for parallel projects, demonstrating a better performance under various uncertainty and flexibility conditions. Finally, the results are validated using empirical project data
Towards implementing new payment models for the reimbursement of high-cost, curative therapies in Europe: insights from semi-structured interviews
No full textBackground: New ways of reimbursement for high-cost, one-shot curative therapies such as advanced therapy medicinal products (ATMPs) are a growing area of interest to stakeholders in market access such as industry representatives, legislative and accounting experts, physicians, hospital managers, hospital pharmacists, patient representatives, policymakers, and sickness funds. Due to the complex nature of ATMPs, new payment models and reimbursement modalities are proposed yet not widely applied across Europe.
Objectives: This study aimed to elicit opinions on and insights into the governance aspect of implementing outcome-based spread payments (OBSP) in Belgium for the reimbursement of innovative therapies. Stakeholders' responsibilities and roles were analysed and proposed solutions or general beliefs were assessed to identify necessary or sufficient conditions to establish outcome-based spread payments.
Methods: Semi-structured interviews (n = 33) were conducted with physicians (n = 2), hospital pharmacists (n = 4), hospital managers (n = 2), Belgian policymakers (n = 6), legislative experts (n = 2), accounting experts (n = 5), representatives of patients (n = 3), of industry (n = 5), and sickness funds (n = 4). The interviews took place between July 2020 and October 2020. The framework method analysis was performed using Nvivo software (version 20.4.1.851). Statements were allocated into six main topics: payment structure, spread payments, outcome-based agreements, governance, transparency, and regulation.
Results: Interviews revealed the necessary conditions that, fulfilled together, are seen to be sufficient for the successful implementation of OBSP, including consensus on pricing, payment logistics, robust data infrastructure and financing, clear agreement terms (duration, outcome parameters, payment triggers), long-term patient follow-up solutions, an external multi-stakeholder governance body, and transparency regarding agreement types.
Conclusion: Despite the interest, the effective implementation of OBSP falls behind due to a lack of consensus on how this new reimbursement method can be a sustainable solution. By stating the necessary conditions that, when fulfilled together, are deemed sufficient for successful OBSP implementation, this study provides a framework towards overcoming implementation barriers and realizing the potential of OBSP in transforming healthcare reimbursement practices
Negotiate your space, global narratives of women entrepreneurs dealing with social norms
No full textA genetic algorithm for seafood processing with flexible flow shops and sequence-dependent setups
No full textThis paper studies a variant of the flexible Flow Shop Scheduling Problem as encountered at a large-scale Belgian seafood processing plant. The operations are conducted in two sequential stages as the seafood products are first filleted or prepared on specialised machines and then packaged through parallel machines. Since the packaging is product-specific, sequence-dependent setup times should be considered in the second stage. Improved scheduling of the operations would require fewer setups and thus efficiently planning the operations on the machines at the packaging station will be an important objective of this research. Furthermore, since the end product quality is crucial in the food industry and this is mainly determined by the speed of processing, the makespan will be minimised in this study. However, we further contribute to the existing literature by investigating several objectives that were relevant to the company’s management. The scheduling problem is solved using a single- and multi-pass algorithms that can easily be implemented in the seafood processing plant. Furthermore, a genetic algorithm with a focus on various diversity measures and problem-specific crossover and mutation operators is developed. Although the genetic algorithm is more difficult to implement, it allowed us to solve real world cases with over 100 orders daily within a reasonable computational time, resulting in an improved solution quality.This work was supported by the Fonds of Wetenschappelijk Onderzoek (FWO) under Grant No. 12A4222