analyze turnover effects

Quantifying and predicting the impact of dynamically evolving lead-time distributions on forecasting performance remains challenging. Method: This paper translates the theoretical framework of “Lead Times in Flux” into the first open-source, end-to-end reproducible R package. It introduces a normalized L1-distance–based metric to quantify distributional shifts in lead times and establishes a systematic, horizon-dependent linkage between lead-time evolution and upper bounds on prediction error. The design minimizes data dependencies and integrates distributional divergence tracking, error-bound analysis, and standardized evaluation. All code is fully reproducible using synthetic data and includes illustrative simulations and predefined evaluation protocols. Contribution/Results: This work bridges the gap between theory and practice for the first time, enabling direct application of the framework in real-world time-series domains—such as tourism and logistics—thereby substantially improving deployment efficiency and interpretability of forecasting models under nonstationary lead-time dynamics.

Existing process mining benchmarks provide only macro-level performance metrics (e.g., throughput time, completion rate), hindering identification of concrete improvement opportunities. To address this, we propose an executable process execution benchmarking method: it aligns event logs from the target organization and benchmark processes based on behavioral similarity, automatically identifying semantically equivalent and substitutable activity units; then constructs a joint feasibility–performance-impact assessment framework to generate ranked, evidence-driven process modification recommendations. This work pioneers the shift from descriptive benchmark analysis to prescriptive, “actionable” improvement guidance. Evaluated across multiple real-world process scenarios, our approach achieves an average throughput time reduction of 12.7%, significantly enhancing both the precision and implementability of process optimization.

Traditional quantitative investment systems typically optimize a single metric—such as the information ratio—and thus struggle to meet professional investors’ multifaceted objectives, including pure alpha generation, style control, drawdown resilience, and turnover and capacity constraints. This work proposes an Objective-Oriented Quantitative Investment (OOQI) framework that formally encodes investment intent as strategy specifications and compiles them into composable, constraint-satisfying strategy assemblies. Key innovations include establishing a dual lattice structure between specifications and assemblies, designing a satisfaction-driven synthesis mechanism, and introducing rolling recertification via e-process-based validation. Empirical results demonstrate that the specification-driven approach satisfies 100% of target constraints across 32 strategies, at the cost of only a 5.5% reduction in information ratio, whereas conventional outcome-oriented methods—despite higher in-sample information ratios—fulfill merely 25% of the specified requirements.

Business process optimization remains challenging due to fragmented methodologies across process mining, predictive process monitoring, and process-aware recommendation—each operating in isolation without a unified theoretical foundation or integration framework. Method: This paper proposes a closed-loop optimization framework that systematically integrates Alpha algorithm/Inductive Miner for process discovery, LSTM/Transformer for runtime prediction, collaborative filtering/graph neural networks for action recommendation, and explainable AI (XAI) for interpretability—enabling automated bottleneck identification, anomaly forecasting, and prescriptive optimization from event logs. Contribution/Results: We establish the first unified conceptual boundary, evolutionary taxonomy, and synergy paradigm across the three domains; construct a comprehensive classification schema covering 120+ studies; clarify application scopes and standardized evaluation benchmarks; and deliver an industrially actionable methodology selection guide with validated deployment pathways.

Business professionals—non-technical domain experts—lack appropriate tools and methodologies for effective what-if analysis (WIA), hindering data-informed decision-making. Method: We conducted a two-phase mixed-methods user study—comprising contextual interviews and in-situ task-based evaluations—to systematically characterize their analytical behaviors for the first time. Contribution/Results: Based on empirical findings, we propose three domain-grounded design principles: business-contextual data preparation, risk-aware assessment, and domain-knowledge integration. We implemented and validated these principles in an interactive visual analytics prototype. The study identifies three critical support gaps, empirically confirms that six classes of what-if techniques significantly improve decision efficiency and confidence, and yields eight actionable design guidelines for commercial business intelligence systems. This work bridges a key theoretical and practical gap in WIA research concerning non-technical users.

This study addresses the limitation of traditional demand forecasting models that rely on statistical metrics such as MAE and RMSE, which often fail to capture their real-world impact on inventory key performance indicators (KPIs)—particularly total cost and service level—in intermittent demand contexts like automotive aftermarket spare parts. To bridge this gap, the authors propose a decision-centric simulation framework that integrates a synthetic demand generator, a plug-and-play forecasting module, and an inventory control simulator. This framework systematically establishes, for the first time, a mapping between forecast errors and inventory KPIs, enabling end-to-end evaluation of any forecasting model under realistic inventory policies. It reveals that improvements in statistical accuracy do not necessarily translate into better operational performance and provides a cost–service trade-off–oriented basis for model selection.

This study addresses the lack of a systematic framework for identifying critical input variables and conducting sensitivity analysis under uncertainty in complex simulations, particularly in military decision-making contexts. The authors propose a unified sensitivity analysis framework that integrates local and global methods—including variance-based, derivative-based, screening, and uncertainty quantification techniques—and strategically maps these approaches to specific decision objectives such as factor prioritization, fixing, variance reduction, and mapping. Innovatively, the framework introduces a “sensitivity audit” mechanism to enhance traceability of model assumptions and promote responsible model usage. By providing a structured guide for high-dimensional, complex simulation systems, this work significantly improves model interpretability, transparency, and the credibility of decisions derived from such models.

This study addresses the long-standing lack of systematic measurement of “implementation risk” in quantitative investment backtesting—the performance discrepancies arising from differences in backtesting engine implementations. The work formally defines this risk for the first time and proposes four metrological metrics alongside a taxonomy of five failure modes. These are derived from parallel execution of 15 benchmark strategies across five open-source backtesting engines, incorporating transaction cost modeling, non-overlapping stratified asset buckets, and source code defect analysis. Experiments reveal that while engine outputs converge under zero-cost assumptions, performance divergence can reach up to 3.71% when transaction costs are introduced. Crucially, however, the relative ranking of strategy efficacy remains unchanged across engines (conclusion stability index = 1), indicating that implementation risk affects performance attribution but does not alter investment decisions.

Existing Bitcoin mining evaluations predominantly rely on ex-post proxy metrics, failing to adequately capture uncertainty and dynamic adjustments. This paper introduces the first ex-ante statistical model grounded in the fundamental premise that hash computations constitute Bernoulli trials. It establishes a closed-form analytical framework incorporating Bitcoin’s difficulty adjustment mechanism to jointly quantify—per unit of computational power—the expected revenue, downside risk (Value-at-Risk and Expected Shortfall), and upside profit probability. Methodologically, the approach integrates Bayesian modeling, stochastic process analysis, empirical calibration, and sensitivity analysis, enabling comparable assessments across hardware types, mining pools, and operational conditions. The model accurately reproduces historical mining performance and provides an analytically tractable risk–return trade-off tool. It delivers a robust quantitative foundation for grid load forecasting and miner behavioral modeling.

This paper addresses the challenge of quantifying business impact from predictive model improvements in insurance pricing. We establish, for the first time, an analytical relationship between model performance and loss ratio. A novel metric—Loss Ratio Error (LRE)—is introduced, linking prediction accuracy to actual financial loss via Pearson correlation, thereby enabling quantitative mapping from model-level metrics (e.g., RMSE) to business-level KPIs. We develop a unified analytical framework integrating frequency, severity, and pure premium models, combining closed-form derivations with Monte Carlo simulation to achieve high-accuracy loss ratio estimation under realistic assumptions; model performance degrades gracefully under assumption shifts, ensuring decision robustness. Our key contribution is the formal identification of diminishing marginal returns in model optimization: incremental accuracy gains yield progressively smaller reductions in loss ratio. This insight shifts pricing model evaluation from heuristic judgment toward cost-benefit-driven, quantitative decision-making.