Protecting Brand Integrity Through Machine Learning: A Strategic Approach to IP Enforcement in E-Commerce

The last-mile delivery problem is one of the most complex and resource-intensive aspects of modern logistics, especially within the growing e-commerce sector. As online shopping continues to expand, companies are under immense pressure to deliver goods more quickly, efficiently, and at lower costs, all while meeting the demands of increasingly time-sensitive customers. This has created a need for innovative solutions that can tackle challenges related to dynamic traffic patterns, fluctuating customer preferences, and operational constraints such as vehicle capacities and delivery windows. In response to these challenges, this paper explores the application of predictive analytics as a tool for optimizing last-mile delivery routes in real-time.The study begins by identifying the core challenges inherent in last-mile logistics, particularly in the U.S. e-commerce landscape, where the cost of last-mile delivery can represent up to 53% of total shipping costs. With traffic congestion, unpredictable customer availability, and delivery time constraints posing significant hurdles, conventional static route planning models often fall short. In this paper, predictive analytics is proposed as a solution to these challenges, utilizing real-time data to inform more efficient routing decisions. By processing vast amounts of real-time traffic data, customer preferences, and delivery constraints, predictive models can offer a more flexible and responsive approach to last-mile delivery.The research then presents a comprehensive literature review of existing route optimization methods, such as the traditional Vehicle Routing Problem (VRP) and its extensions, including VRP with Time Windows (VRPTW), Dynamic VRP (DVRP), and Capacitated VRP (CVRP). While these models have proven useful, their limitations are exposed when faced with real-time operational complexities in the e-commerce sector. Therefore, this study introduces an advanced dynamic routing model that integrates machine learning algorithms—such as decision trees and neural networks—with traditional VRP frameworks. These machine learning models, trained on historical data, are capable of predicting future traffic patterns, customer behavior, and delivery time windows.A case study is conducted using data from U.S.-based e-commerce companies to demonstrate the practical application of predictive analytics in optimizing last-mile delivery. The case study outlines how predictive models are used to dynamically adjust delivery routes based on real-time conditions, leading to significant improvements in efficiency, cost savings, and customer satisfaction. Key performance indicators such as delivery times, fuel consumption, and vehicle utilization are examined before and after the implementation of the predictive models, with the results showing a reduction in delivery time by 20% and fuel costs by 15%, alongside improved on-time delivery rates.The paper concludes by presenting the proposed dynamic route optimization model as a solution that combines the flexibility and responsiveness of predictive analytics with the robust framework of traditional VRP models. Through the integration of machine learning, real-time data processing, and dynamic routing, the model is shown to significantly improve last-mile delivery efficiency. This study's findings highlight the potential for predictive analytics to revolutionize the logistics industry, particularly in the high-demand e-commerce sector, where quick and reliable delivery is paramount. The research suggests that as e-commerce continues to grow, predictive analytics will play an increasingly critical role in ensuring that last-mile delivery is both cost-effective and responsive to the evolving needs of consumers.

This paper presents the development of machine learning model to predict key performance indicators (KPIs) in oil and gas Well drilling, including Dry Hole Drilling Days (DHDD), Dry Hole Drilling Cost (DHDC), and Clean Time (CT). It explores diverse input variables to improve accuracy and reliability of these KPI predictions. This paper employs a collaborative approach between academia and industry to develop machine learning models for KPI prediction using CRISP-DM (Cross Industry Standard Process for Data Mining) methodology. Methodology involves compiling and preparing the historical data from previously drilled Wells from multiple fields of geological complex and tectonically stressed region of Pakistan. Subsequently various machine learning algorithms are trained, tested and evaluated for predicting three drilling KPIs. Based on top performing machine learning models, a calculator is prepared and deployed in to order validate the results against the unseen data. The results demonstrate the effectiveness of machine learning models in predicting drilling KPIs; Dry Hole Drilling Days (DHDD), Dry Hole Drilling Cost (DHDC) and Clean Time (CT) to an acceptable level of accuracy. Among the models assessed, Support Vector Machine (SVM), Random Forest (RF), and Stacking employing SVM as the base model and Linear Regression (LR) as the meta-model emerged as the top performing models. SVM demonstrated superior predictive capabilities for DHDD, achieving an Error Percentage of 14.2% on testing data. Whereas, on similar data RF excelled in forecasting DHDC with an Error Percentage of 11.2% and Stacking proved best for CT prediction with an Error Percentage of 10.6%. KPI predictions from machine learning model based calculator for the future planned Wells are compared with in-house cost and time estimation methods and found both methods complementing each other. These findings emphasize the potential of machine learning in optimizing drilling operations and maximizing efficiency. While a single machine learning model for all KPIs is not possible, the accurate prediction of DHDD, DHDC, and CT suggests the viability of implementing machine learning models for better budget planning, optimizing the drilling operations, and ultimately reducing the cost of drilling operations. Machine learning model can accurately predict critical macro-level KPIs of drilling operations, such as DHDD, DHDC and CT. Larger number of machine learning algorithms are explored for this purpose. Previous application of machine learning models in the field of drilling is predominantly focusing on micro-level KPIs like drilling parameters optimization and Rate of Penetration (ROP) improvement. Whereas, this research represents a significant departure from existing literature by exploring the prediction of macro-level Well drilling KPIs.

With the rapid developments of fifth generation (5G) mobile communication networks in recent years, different use cases can now significantly benefit from 5G networks. One such example is high-speed trains found in several countries across the world. Due to the dense deployment of 5G millimetre wave (mmWave) base stations (BSs) and the high speed of moving trains, frequent handovers (HOs) occur which adversely affect the Quality-of-Service (QoS) of mobile users. User association for load balancing is also a key issue in 5G networks. Therefore, HO optimisation and resource allocation are major challenges in the mobility management of high-speed train systems. Handover Margin (HOM) and Time-to-Trigger (TTT) parameters are crucial for the HO process since they affect the key performance indicators (KPIs) of high-speed train systems in 5G networks. To manage system performance from the aspect of predictive analytics, we have modelled system performance of mobility management through machine learning (ML). First, the HO management process of a high-speed train scenario is framed as a supervised ML problem. The inputs for the problem are regression task, HOM and TTT and the outputs are key performance indicators (KPIs). Second, data processing is accomplished after generating a simulation dataset. Several methods are employed for the dataset, such as Adaptive Boosting (AdaBoost), Gradient Boosting Regression (GBR), CatBoost Regression (CBR), Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Kernel Ridge Regression (KRR) and K-Nearest Neighbour Regression (KNNR). Tenfold cross validation is then applied for choosing the best hyperparameters. Finally, the deployed methods are compared in terms of the Mean Absolute Error (MAE), Mean Square Error (MSE), Maximum Error (Max E), and R2 score metrics. From the MAE results, CBR achieves the best outcomes for load level and throughput KPIs with 0.003 and 0.0144, respectively. On the other hand, GBR achieves the best results for call dropping ratio (CDR), radio link failure (RLF) and spectral efficiency KPIs with 0.354, 0.082 and 0.354, respectively. CBR also follows GBR for the three KPIs with 0.356, 0.082 and 0.357, respectively. Only a slight difference in estimations is present. MLP achieves the best results for HO ping-pong (HOPP) and HO probability (HOP) KPIs with 0.0045 and 0.011, respectively. This is followed by GBR and CBR. From the MSE outcomes, CBR and GBR exhibit the best results for load level and throughput KPIs with 2e-5 and 3e-5, respectively. GBR attains the best results for CDR, RLF and spectral efficiency KPIs with 0.25, 0.011 and 0.025, respectively. Accordingly, CBR follows GBR with slightly different errors for the three KPI estimations. MLP achieves the best results for HOPP and HOP KPIs with 5e-5 and 3.6e-5, respectively. Again, this is followed by GBR and CBR for the estimation of these results. This indicates that CBR and GBR can capture relationships between inputs and KPIs for the dataset used in this study, outperforming all other methods generally used for solving this problem.

Current data governance techniques are very labor-intensive, as teams of data stewards typically rely on best practices to transform business policies into governance rules. As data plays an increasingly key role in today's data-driven enterprises, current approaches do not scale to the complexity and variety present in the data ecosystem of an enterprise as an increasing number of data requirements, use cases, applications, tools and systems come into play. We believe techniques from artificial intelligence and machine learning have potential to improve discoverability, quality and compliance in data governance. In this paper, we propose a framework for 'contextual intelligence', where we argue for (1) collecting and integrating contextual metadata from variety of sources to establish a trusted unified repository of contextual data use across users and applications, and (2) applying machine learning and artificial intelligence techniques over this rich contextual metadata to improve discoverability, quality and compliance in governance practices. We propose an architecture that unifies governance across several systems, with a graph serving as a core repository of contextual metadata, accurately representing data usage across the enterprise and facilitating machine learning, We demonstrate how our approach can enable ML-based recommendations in support of governance best practices.

Machine learning (ML) approaches have been extensively exploited to model and to improve wireless communication networks in the past few years. Nonetheless, the estimation of key performance indicators (KPIs) and their uncertainties in Long Term Evolution License Assisted Access (LTE-LAA) based coexistence systems is not adequately addressed. For example, it is not clear if an ML method can accurately predict achievable KPIs (e.g. throughput) and the probability of coexistence (PoC) of LTE-LAA coexistence systems based on partial or no information of MAC and physical layer protocols and parameters. In this paper, we develop a novel ML method by combining a neural network with a logistic regression algorithm to track and estimate KPIs and PoC of coexisting LTE-LAA and wireless local area network (WLAN) links. This ML method can be applied when KPI samples at the base stations (BSs) and access points (APs) are available, without using knowledge of MAC and physical layer parameters. Comparison between the ML and simulation results indicate that the proposed ML method can track the system KPIs and predict the system PoC with good accuracy.

Key Performance Indicators in performance management system was attracted by researchers and practitioners. In order to effective implemented KPIs in SMEs, managers must deeply understand about the KPIs, role of KPIs, implemented KPIs. Based on the quantitative method by doing survey with 162 SEMs, author indicated the current situation of the perception of SMEs manager about the fundamental of KPI, roles of KPIs and difficulty implemented KPIs in performance management system. Based on the consistent theory about KPIs , author proposed some solution for manager to enhance their knowledge of KPIs.
 Keywords
 SMEs, KPIs , Performance appraisal
 References
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Transportation agencies utilize key performance indicators (KPIs) to measure the performance of their traffic networks and business processes. To make effective decisions based on these KPIs, there is a need to align the KPIs at the strategic, tactical, and operational decision levels and to set targets for these KPIs. However, there has been no known effort to develop methods to ensure this alignment producing a correlative model to explore the relationships to support the derivation of the KPI targets. Such development will lead to more realistic target setting and effective decisions based on these targets, ensuring that agency goals are met subject to the available resources. This paper presents a methodology in which the KPIs are represented in a tree-like structure that can be used to depict the association between metrics at the strategic, tactical, and operational levels. Utilizing a combination of business intelligence and machine learning tools, this paper demonstrates that it is possible not only to identify such relationships but also to quantify them. The proposed methodology compares the effectiveness and accuracy of multiple machine learning models including ordinary least squares regression (OLS), least absolute shrinkage and selection operator (LASSO), and ridge regression, for the identification and quantification of interlevel relationships. The output of the model allows the identification of which metrics have more influence on the upper-level KPI targets. The analysis can be performed at the system, facility, and segment levels, providing important insights on what investments are needed to improve system performance.

Membership organizations often try to give members what they think members need, but what do members really need?To find out, conduct a "member needs assessment."Sheri Jacobs, president and CEO of Avenue M Group, helps organizations determine their members' needs using, among other tools, benchmarking.Leaders can use benchmarking to "set realistic and achievable goals and make informed decisions about where to invest."But to benchmark, you need information on what other groups are doing.This is where Avenue M Group has an edge with its own database of organizations.The focus should be on key performance indicators (KPIs), says Jacobs.How your organization performs on these KPIs "can identify weaknesses that, if left unchecked, could result in a drop in membership and a loss in revenue," says Jacobs.What Avenue M can bring to the table is aggregated research data from more than 300 associations across many industries and sectors over the past 15 years.This information can help the company "provide relevant benchmarking data and recommendations based on survey findings."Here's what Jacobs means by a survey: A survey is a quantitative form of market research conducted with members to gather their feedback, opinions, and information.The results of a survey help an organization better understand members' needs, challenges, motivations, and, potentially, their future behavior.Benchmarks against which you could compare your organization include: types and rates for different memberships (how do you determine dues), benefits (maybe you're giving away too much, or too little), and special events (are these loss leaders or just losses?).It's also helpful to connect clients to one another, helping them expand their network and gain additional insights from other association executives, says Jacobs."Benchmarking is particularly useful when there are some similarities among the organizations," she notes.This may include industry, organization size, IMO [Individual Membership Organization] whether trade or hybrid, and other factors.Needs, expectations, and satisfaction are intertwined, says Jacobs."When an individual joins or renews their membership, it is often to fulfill a need they have.An organization's promise will likely set expectations for what they will receive."Association executives "need to adapt and modify their programs, products, and services to stay competitive and relevant," Jacobs concludes."By surveying members and nonmembers, they will be better equipped with the data and insights to support adjustments to their current benefits or invest in new ones.

This article presents a new approach of quality control to vibro ground improvement techniques based on hybrid machine learning (ML), i.e., a combination of classical analysis and ML techniques. The process is monitored with an instrumented rig equipped with multiple sensors. Key performance indicators (KPIs) are used to identify anomalous foundation columns. As the foundation columns are sub‐surface, there is no direct access to ground truth; consequently, unsupervised ML is applied to the recorded time‐series data. The risk of not detecting defective elements is reduced by the combination of two independent methods for anomaly detection, KPI‐ and ML‐based classification. The ML is used to gain a deeper process understanding and to detect anomalies which were not considered in the design phase of the KPI. New pre‐processing techniques were derived from the insights gained from the ML classifier; this led to a more robust classifier. It is shown how unsupervised ML, using a multi‐channel variational autoencoder (VAE) with long short‐term memory (LSTM) layers, can be utilized in a knowledge discovery process (KDP).

Rugby union is an intermittent high-intensity contact sport requiring the analysis of various training and match metrics. Time-motion analysis and video analysis have enhanced the understanding of the interplay between these two factors. However, limited studies have investigated the effect of workload on key performance indicators (KPIs) during matches. In this study, data collected from the global positioning system (GPS) were used to calculate cumulative workload values over 7, 14, and 21 days prior to each game. After dimensionality reduction through principal component analysis (PCA), these workload values were employed as features, with game KPIs as target variables. Modeling was conducted using linear regression (LR), support vector regression (SVR), random forest regression (RFR), and light gradient boosting machine (LightGBM) for regression tasks. The superiority of the model was assessed by coefficient of determination ( ), root mean square error ( ), and correlation coefficient ( ). The findings revealed that although individual GPS metrics exhibited weak correlations with KPIs, machine learning (ML) models particularly RFR, successfully captured complex interactions and nonlinear relationships. These models achieved significantly improved predictive performance, with values ranging from 0.40 to 0.72 for certain KPIs. Using SHapley Additive exPlanations (SHAP) analysis and partial dependence plots, this study enhanced the interpretability of ML models by identifying the influence of GPS features on KPIs and exploring their underlying mechanisms. These findings offer actionable insights for workload management, emphasizing critical factors that affect player performance.

In mobile networks, handover (HO) is one of the most important and complex KPIs (Key Performance Indicators), which directly affect to Quality of Service (QoS), Quality of Experience (QoE), and mobility performance. Moreover, its configuration parameters such as handover thresholds and handover neighbor lists are the key factors for implementing network optimization such as load balancing and energy saving. In a study before, the authors proposed clustering and forecasting models using ML algorithms and Time Series models to cluster, forecast, and manage the HO behavior of a huge number of cells. In this study, on the other hand, the authors firstly investigated more network KPIs to analyze their relationship with HO KPIs, and then, proposed new clustering, forecasting, and abnormal detection models that are expected to make them much more comprehensive. Finally, the performances of the proposed models were evaluated by applying them to a real dataset collected from the HO KPIs and other KPIs of more than 6000 cells of a real network during the years, 2016 and 2017. The results showed that the study was successful in identifying the relationship among network KPIs and significantly improving the performance of the HO clustering, forecasting, abnormal detection models. Moreover, the study also introduced the integration of emerging technologies such as machine learning (ML), big data, software-defined network (SDN), and network functions virtualization (NFV) to establish a practical and powerful computing platform for future self-organizing networks (SON).

Diabetes mellitus stands out as one of the most prevalent chronic conditions affecting pediatric populations. The escalating incidence of childhood type 1 diabetes (T1D) globally is a matter of increasing concern. Developing an effective model that leverages Key Performance Indicators (KPIs) to understand the incidence of T1D in children would significantly assist medical practitioners in devising targeted monitoring strategies. This study models the number of monthly new cases of T1D and its associated KPIs among children aged 0 to 14 in Saudi Arabia. The study involved collecting de-identified data (n=377) from diagnoses made between 2010 and 2020, sourced from pediatric diabetes centers in three cities across Saudi Arabia. Poisson regression (PR), and various machine learning (ML) techniques, including random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN), were employed to model the monthly number of new T1D cases using the local data. The performance of these models was assessed using both numbers of KPIs and metrics such as the coefficient of determination ([Formula: see text]), root mean squared error (RMSE), and mean absolute error (MAE). Among various Poisson and ML models, both model considering birth weight over 3.5 kg, maternal age over 25 years at the child's birth, family history of T1D, and nutrition history, specifically early introduction to cow milk and model taking into account birth weight over 3.5 kg, maternal age over 25 years at the child's birth, and nutrition history (early introduction to cow milk) emerged as the best-reduced models. They achieved [Formula: see text] of (0.89,0.88), RMSE (0.82, 0.95) and MAE(0.62,0.67). Additionally, models with fewer KPIs, like model that considers maternal age over 25 years and early introduction to cow milk, achieved consistently high [Formula: see text] values ranging from 0.80 to 0.83 across all models. Notably, this model demonstrated smaller values of RMSE (0.92) and MAE (0.67) in the KNN model. Simplified models facilitate the efficient creation and monitoring of KPIs profiles. The findings can assist healthcare providers in collecting and monitoring influential KPIs, enabling the development of targeted strategies to potentially reduce, or reverse, the increasing incidence rate of childhood T1D in Saudi Arabia.

Anomaly detection is a critical component of Self-Organizing Networks (SON), enhancing network efficiency and resilience. This paper proposes a novel framework for detecting collective anomalies in univariate Key Performance Indicators (KPIs) in mobile networks. By leveraging data mining and Machine Learning (ML) techniques, the framework enables timely anomaly detection without requiring expert-labeled data. The proposed framework starts by clustering time series from 11 distinct KPIs into four groups. Then, representative KPIs from each cluster are selected to evaluate the anomaly detection performance using two algorithms: the Smart Trouble Ticket Management (STTM) and STUMPY. The STTM algorithm is applied to KPIs with low variability, such as Call Setup Success Rate and Service Drop Rate, showing high accuracy in anomaly detection. For KPIs with higher variability, such as User Downlink (DL) Average Throughput and DL Resource Block Utilization Rate, the STUMPY algorithm is employed, yielding similarly accurate results. The framework, composed of the STTM and the STUMPY algorithms, demonstrates effective anomaly detection, achieving high precision (0.94), recall (0.86), and an F1-score of 0.90, with minimal False Positives (FP). These results underline the framework’s reliability across different types of KPIs, providing a robust solution for anomaly detection in mobile network monitoring, outperforming benchmark algorithms in all key metrics.

Key performance indicators (KPIs) are crucial for managing business performance and optimization strategies. However, traditional KPIs are inflexible and cannot adapt to changes in staff, business units, functions, and processes. To address this issue, this paper proposes a method that combines statistics, machine learning (ML), and artificial intelligence (AI) to augment traditional KPIs with the flexibility of data-driven automation (DDA) techniques. This study builds a model that takes traditional KPIs generated by an integrated ecosystem as input data and assesses the suitability and correlation of the data using statistical techniques, such as Bartlett’s test of sphericity and the Kaiser–Meyer–Olkin (KMO) test of sampling adequacy. The model then employs exploratory Factor Analysis (FA) techniques to identify correlations and patterns, prioritize KPIs, and automatically generate smart KPIs for business optimization. The model is designed to adapt automatically by creating new KPIs as the business evolves and data change. A case study evaluation validates this approach, showing that DDA techniques can effectively create smart KPIs for business optimization. This approach provides a flexible and adaptable way to manage business performance and optimization strategies, enabling organizations to stay ahead of the competition and achieve their goals.

A data-driven workflow was developed to monitor electrical submersible pump (ESP) health using an anomaly detection method with high-frequency sensor data. The workflow would help maximize the run life of ESPs while reducing the cost of maintenance. The new workflow contrasts with conventional field maintenance which is often reactive and incurs additional downtime in logistics and inventory management in diagnosing the issues and taking the recommended actions. In contrast, using machine learning (ML) concepts can save operating costs, especially in the case of the ESPs widely used for artificial lift. Many operators augment ESPs with high-frequency (HF) sensors to monitor their performance, but much of this information remains either unused or partially used in post-failure analysis. The application of ML concepts in understanding ESP operational behavior complements the existing domain practice. The workflow we describe in this paper begins with domain knowledge and exploratory statistical analysis to find the key performance indicators (KPIs) related to ESP failure. Feature engineering and advanced ML techniques are used to build and test healthy ESP models for each selected KPI. Multiple health signals are fused to improve the performance of anomaly detection using historical ESP failure data and pullout reports as benchmarks. In a test of the workflow, the model was trained on the data from a group of active producing wells with reported historical events, failures, and pullout reports. The data contained several well events and several reported failures. This information was used to fine-tune the alarm thresholds for the health indicators. The model was able to detect approximately 70% of failure events (true positive rate) in the data set. The false alarm rates for the configured model were approximately at 20% (false positive rate). The solution can be implemented in a dashboard to monitor ESP KPIs and show health alarms. These alarms can be further prioritized based on the failure probability and remaining useful life of the ESP. The health signal degradation patterns can be captured and learned to predict the remaining useful life of the ESPs, thus enabling operators to allocate and prioritize maintenance resources. In addition, the analysis of ESP pullout reports can provide insight into the relationship between health signals and root causes of the failure, which can be structured into a formal Bayesian network to provide automatic root cause interpretation The data-driven approach takes advantage of the vast amount of reservoir, production, and facilities data and provides insights into nonlinear multidimensional relationships between parameters to better understand and optimize field development and to adopt a proactive approach toward equipment maintenance.