Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories

Digitization and artificial intelligence are radically changing virtually all areas across business and society. These developments are mainly driven by the technology of machine learning (ML), which is enabled by the coming together of large amounts of training data, statistical learning theory, and sufficient computational power. This technology forms the basis for the development of new approaches to solve classical planning problems of Operations Research (OR): prescriptive analytics approaches integrate ML prediction and OR optimization into a single prescription step, so they learn from historical observations of demand and a set of features (co-variates) and provide a model that directly prescribes future decisions. These novel approaches provide enormous potential to improve planning decisions, as first case reports showed, and, consequently, constitute a new field of research in Operations Management (OM). First works in this new field of research have studied approaches to solving comparatively simple planning problems in the area of inventory management. However, common OM planning problems often have a more complex structure, and many of these complex planning problems are within the domain of capacity planning. Therefore, this dissertation focuses on developing new prescriptive analytics approaches for complex capacity management problems. This dissertation consists of three independent articles that develop new prescriptive approaches and use these to solve realistic capacity planning problems. The first article, “Prescriptive Analytics for Flexible Capacity Management”, develops two prescriptive analytics approaches, weighted sample average approximation (wSAA) and kernelized empirical risk minimization (kERM), to solve a complex two-stage capacity planning problem that has been studied extensively in the literature: a logistics service provider sorts daily incoming mail items on three service lines that must be staffed on a weekly basis. This article is the first to develop a kERM approach to solve a complex two-stage stochastic capacity planning problem with matrix-valued observations of demand and vector-valued decisions. The article develops out-of-sample performance guarantees for kERM and various kernels, and shows the universal approximation property when using a universal kernel. The results of the numerical study suggest that prescriptive analytics approaches may lead to significant improvements in performance compared to traditional two-step approaches or SAA and that their performance is more robust to variations in the exogenous cost parameters. The second article, “Prescriptive Analytics for a Multi-Shift Staffing Problem”, uses prescriptive analytics approaches to solve the (queuing-type) multi-shift staffing problem (MSSP) of an aviation maintenance provider that receives customer requests of uncertain number and at uncertain arrival times throughout each day and plans staff capacity for two shifts. This planning problem is particularly complex because the order inflow and processing are modelled as a queuing system, and the demand in each day is non-stationary. The article addresses this complexity by deriving an approximation of the MSSP that enables the planning problem to be solved using wSAA, kERM, and a novel Optimization Prediction approach. A numerical evaluation shows that wSAA leads to the best performance in this particular case. The solution method developed in this article builds a foundation for solving queuing-type planning problems using prescriptive analytics approaches, so it bridges the “worlds” of queuing theory and prescriptive analytics. The third article, “Explainable Subgradient Tree Boosting for Prescriptive Analytics in Operations Management” proposes a novel prescriptive analytics approach to solve the two capacity planning problems studied in the first and second articles that allows decision-makers to derive explanations for prescribed decisions: Subgradient Tree Boosting (STB). STB combines the machine learning method Gradient Boosting with SAA and relies on subgradients because the cost function of OR planning problems often cannot be differentiated. A comprehensive numerical analysis suggests that STB can lead to a prescription performance that is comparable to that of wSAA and kERM. The explainability of STB prescriptions is demonstrated by breaking exemplary decisions down into the impacts of individual features. The novel STB approach is an attractive choice not only because of its prescription performance, but also because of the explainability that helps decision-makers understand the causality behind the prescriptions. The results presented in these three articles demonstrate that using prescriptive analytics approaches, such as wSAA, kERM, and STB, to solve complex planning problems can lead to significantly better decisions compared to traditional approaches that neglect feature data or rely on a parametric distribution estimation.

In the rapidly evolving landscape of data mining, the effective extraction of valuable insights from large datasets is paramount. This survey paper investigates the pivotal roles of data visualization and analytics in the intricate process of data mining abstraction. We delve into the symbiotic relationship between these two components, examining how they synergistically contribute to the extraction, representation, and interpretation of meaningful patterns and trends within complex datasets. The survey begins by elucidating the fundamental concepts of data mining abstraction and the significance of distilling actionable knowledge from raw data. It subsequently explores the multifaceted benefits of data visualization, elucidating its role in pattern identification, insight generation, and seamless communication of findings to diverse stakeholders.In parallel, the paper navigates through the landscape of data analytics, unraveling its diverse methods such as descriptive analytics, predictive analytics, and prescriptive analytics. Emphasis is placed on how these analytical techniques enhance the abstraction process, providing statistical rigor and predictive power to unveil hidden insights.The integration of data visualization and analytics is a focal point, showcasing their collective impact on various stages of data mining. From exploratory data analysis (EDA) for initial dataset understanding to the evaluation of mining models, the survey illuminates the collaborative nature of these components. Interactive dashboards emerge as a powerful tool, allowing users to dynamically explore datasets, visualize trends, and perform real-time analytics. Key Words:Data visualization , Data analytics ,Data mining , Tools and Software , Metrics and Key Performance Indicators (KPIs).

In the manufacturing industry, technologies of The Fourth Industrial Revolution have been integrated into Smart Factory and is evolving into an intelligent factory. In other words, smart factories create an optimal economic production environment by exchanging information through the Internet of Things and by providing real-time monitoring, analysis of all processes. The recent decline in birthrate, aging population and rising labor costs require automated production plants. At the same time, the more diversi- fied demands of consumers become, the shorter the life cycle of products becomes and the capability of customized mass production has emerged as the core of competitiveness. Therefore, smart factories are an essential product of innovation to adapt to these socioeconomic changes. In addition, smart factories provide breakthrough results such as high quality, reduced defect rate and improved productivity. In this study, we will first explore the cases of the operations of German automated factories, the place of origin of the Fourth Industrial Revolution and the world’s strongest manufacturer, to derive elements that can be benchmarked to Korean small and medium-sized enterprises (SME’s). Based on this, the ultimate purpose of this study is to suggest implications for the governmental projects supporting compa- nies that want to build, maintain and spread smart factories in Korea. In this study, prior literary studies on domestic and foreign papers and research reports and empirical case studies through corporate visits were used as research methodologies. While the existing research is an indirect study through domestic and international literature or information on the Internet, this study is differentiated in that it is an empirical case study of visiting demonstration. The implication is that in terms of corporate strategy, smart factories should first commence with manufacturing processes that require automation and then spread them sequentially in line with the corporate management environment (financial, market competition structure, and growth potential). Furthermore, various information and new technologies in the smart manufacturing process should be actually integrated to create optimal synergy, and various internal and external management resources such as human resources and environmental energy should be planned and implemented in an integrated manner. In terms of policy support, the focus should be on customized modular support that companies need. At the same time, it is also necessary to standardize the process of smart manufacturing through joint cooperative support for smart factories among the same companies and induce mutual utilization of generated data, minimization of use costs and maximum synergy efficiency. In particular, it is necessary to seek ways to utilize idle smart factories when ordering small or excessive amounts of orders. At the same time, expansion of smart factory supporting platforms and managemental aid are also required through a consortium between professional companies that supply new technologies to smart factories (e.g. Robots, Sense, AI, Total solution SW, VR etc.). Environment-friendly smart manu- facturing and optimal utilization of energy and materials should also be considered comprehensively. To this end, it is necessary to have a smart management organization, or a research advisory group, that studies human and environment-friendly smart manufacturing within the “Smart Manufacturing Innovation Promotion Group” to induce social innovation. It will be necessary to minimize the waste of policy support by thoroughly managing and supervising the implementation of the beneficiary entities. Finally, support for the introduction and application of smart factories for SME’s and Midsize Business should be strengthened to promote the smart manufacturing of SME’s to increase their competitiveness.

Motivated by the real-world problem of a logistics company, this paper proposes a novel distribution-free prescriptive analytics approach—termed kernelized empirical risk minimization (kernelized ERM)—to solve a complex two-stage capacity planning problem with multivariate demand and vector-valued capacity decisions and compares this approach both theoretically and numerically with an extension of the well-known sample average approximation (SAA) approach termed weighted SAA. Both approaches use integrated machine learning algorithms to prescribe capacities directly from historical demand and numerous features (covariates) without having to make assumptions about the underlying multivariate demand distribution. We provide extensive analytical insights into both approaches. Most important, we prove the universal approximation property for the kernelized ERM approach when using a universal (data-independent) kernel and show how out-of-sample guarantees can be derived for various kernels. We demonstrate the applicability of both approaches to a real-world planning problem and evaluate their performance relative to traditional parametric approaches that first estimate a multivariate demand distribution and then solve a stochastic optimization problem and a nonparametric approach (SAA). Our results suggest that the two prescriptive analytics approaches can result in substantial performance improvements of up to 58% compared with traditional approaches. Additional numerical analyses shed light on the behavior and performance drivers of the various approaches and demonstrate that in our case, the prescriptive approaches are much more robust to variations of exogenous cost parameters than traditional approaches. This paper was accepted by J. George Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics.

Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, sports, etc. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface energy resources in domains such as conventional and unconventional oil and gas, geologic carbon sequestration, and geothermal energy. It is useful to start with some definitions to establish a common vocabulary. Data analytics (DA)—Sophisticated data collection and analysis to understand and model hidden patterns and relationships in complex, multivariate data sets Machine learning (ML)—Building a model between predictors and response, where an algorithm (often a black box) is used to infer the underlying input/output relationship from the data Artificial intelligence (AI)—Applying a predictive model with new data to make decisions without human intervention (and with the possibility of feedback for model updating) Thus, DA can be thought of as a broad framework that helps determine what happened (descriptive analytics), why it happened (diagnostic analytics), what will happen (predictive analytics), or how can we make something happen (prescriptive analytics) (Sankaran et al. 2019). Although DA is built upon a foundation of classical statistics and optimization, it has increasingly come to rely upon ML, especially for predictive and prescriptive analytics (Donoho 2017). While the terms DA, ML, and AI are often used interchangeably, it is important to recognize that ML is basically a subset of DA and a core enabling element of the broader application for the decision-making construct that is AI. In recent years, there has been a proliferation in studies using ML for predictive analytics in the context of subsurface energy resources. Consider how the number of papers on ML in the OnePetro database has been increasing exponentially since 1990 (Fig. 1). These trends are also reflected in the number of technical sessions devoted to ML/AI topics in conferences organized by SPE, AAPG, and SEG among others; as wells as books targeted to practitioners in these professions (Holdaway 2014; Mishra and Datta-Gupta 2017; Mohaghegh 2017; Misra et al. 2019). Given these high levels of activity, our goal is to provide some observations and recommendations on the practice of data-driven model building using ML techniques. The observations are motivated by our belief that some geoscientists and petroleum engineers may be jumping the gun by applying these techniques in an ad hoc manner without any foundational understanding, whereas others may be holding off on using these methods because they do not have any formal ML training and could benefit from some concrete advice on the subject. The recommendations are conditioned by our experience in applying both conventional statistical modeling and data analytics approaches to practical problems.

Data analytics is related to extracting information from observations, measurements, or experiments about a phenomenon or subject of interest for different purposes, such as interpretation of the data, decision making, diagnosis, and prediction [1]. A classification of the entire field of big data analytics is difficult to make and can be based on a variety of criteria. One classification related to the depth of the analysis is presented by Blackett [2], identifying three levels: descriptive, predictive, and prescriptive analytics. Descriptive analytics uses past data to analyze what has occurred, while predictive analytics focuses on forecasting future trends and determining probabilities of occurrence mainly through the application of different statistical techniques or data mining algorithms for pattern extraction. Prescriptive analytics facilitates decision making and optimization of complex systems.

This paper proposed a pickup and delivery (P&D) task provisioning and vehicle routing framework for carrier robots in a factory with edge servers assisting as the intermediary between robots and working stations, where the task requests come. The carrier robots must pick up and deliver each assigned load from and to designated locations with minimal traveled distance and without violating designated constraints. Edge servers are utilized to facilitate communication between the main server and delivery robots and assist the main server in deciding the best robot for each incoming task request and the updated route to facilitate execution of tasks by each carrier robot. The problem of pickup and delivery for each robot is modeled based on Dial-a-Ride Problem, and a discrete bio-inspired algorithm is proposed to solve this problem. The tasks are distributed to edge servers and carrier robots by taking their service loads into account. The simulation results show that the proposed framework can provide an effective solution towards optimizing the pickup and delivery process in a smart factory.

Application of artificial intelligence technology in the manufacturing process and purchasing and supply management

Big data analytics assist the organizations and business managers to make decision more efficient by analyzing given data and applying some algorithms upon it. Data analytics is the process in which, we find patterns and draw conclusions about the information with the help of raw data. Mainly, three kinds of analytical approaches are existent for analysis of dataset, including descriptive analytics, predictive analytics and prescriptive analytics. In this paper, authors' focused on comparative analysis of descriptive analytical algorithms, along with their different approaches and applications in different areas. In addition to this, various types of data analytics have also been elaborated, in this paper.

A Blockchain-Based Data-Driven Fault-Tolerant Control System for Smart Factories in Industry 4.0

As digitalization continuously gets established in manufacturing, increasing amounts of data are being generated. This change opens up various possibilities to utilize these data to improve production processes by supporting decision-making. Data analytics advances the acquisition of knowledge from data and, thus, improves decision-making in manufacturing and related processes such as maintenance. Identifying the current maturity of data analytics in the manufacturing environment reveals potential and builds the basis for future developments. This paper presents a theory-driven maturity model for the classification of data analytics use cases in the context of data analytics in manufacturing. Furthermore, the model aims to offer a subcategorization of the vast and complex topic of data analytics for manufacturing purposes. The model is applied to an example of Smart Services at TRUMPF GmbH + Co. KG. This case highlights the major potential of predictive data analytics and first ideas towards prescriptive data analytics are presented.

This study explores the integration of business analytics into government-led agricultural marketing campaigns, with a focus on optimizing resource allocation across multi-regional markets. As agricultural sectors face increasing pressure due to climate change, fluctuating market demands, and limited resources, data-driven strategies offer significant potential to improve efficiency and decision-making. The research highlights the critical role of predictive and prescriptive analytics in enhancing market access, reducing waste, and ensuring that resources are allocated where they are most needed. Key findings indicate that advanced data analytics, such as machine learning algorithms and optimization models, can forecast market trends, optimize supply chain management, and guide marketing strategies that align with regional agricultural dynamics. The study also emphasizes the need for a comprehensive data infrastructure that facilitates real-time data collection and analysis, as well as the importance of developing institutional capacity to support the adoption of business analytics. The study provides practical recommendations for policymakers and agricultural agencies, advocating for investments in data infrastructure, capacity-building, and public-private partnerships to scale analytics capabilities. Additionally, it discusses the challenges of overcoming institutional resistance, limited technological infrastructure in rural regions, and regulatory barriers to the widespread use of business analytics. Future research should explore the role of emerging technologies, such as blockchain and the Internet of Things (IoT), in enhancing the traceability and transparency of agricultural supply chains, and address gaps in data availability and institutional readiness. The integration of business analytics into agricultural marketing campaigns offers a pathway to more efficient, data-driven decision-making that improves market outcomes, supports food security, and drives sustainable agricultural development. Bridging the gap between analytics and agricultural policy is essential for addressing the complex challenges facing global agriculture today.

Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines and assembly lines generate large amounts of data and effective visual exploration techniques are needed for troubleshooting, process optimization, and decision making. However, developing effective visual analytics solutions for this application domain is a challenging task due to the sheer volume and the complexity of the data collected in the manufacturing processes. We report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of assembly line performance and historical data exploration to identify inefficiencies, locate anomalies, and form hypotheses about their causes and effects. The system is designed based on a set of requirements gathered through discussions with the managers and operators from manufacturing sites. It features interlinked views displaying data at different levels of detail. In particular, we apply and extend the Marey's graph by introducing a time-aware outlier-preserving visual aggregation technique to support effective troubleshooting in manufacturing processes. We also introduce two novel interaction techniques, namely the quantiles brush and samples brush, for the users to interactively steer the outlier detection algorithms. We evaluate the system with example use cases and an in-depth user interview, both conducted together with the managers and operators from manufacturing plants. The result demonstrates its effectiveness and reports a successful pilot application of visual analytics for manufacturing in smart factories.

Information and communication technology (ICT) has been the backbone of businesses for some time now. ICT adoption has rendered data availability in vertically connected organizations. In recent times, the rise of analytics has paved the way to rethink the structure and working of vertically connected firms within a supply chain. Big data analytics (BDA) for instance, has become a prominent tool to analyse unstructured data. This study explores the application and benefit of various types of analytics such as descriptive analytics, predictive analytics and prescriptive analytics in the process of supply chain management (SCM). Additionally, the study also looks at ways by which analytics could integrally be included within the SCM curriculum in higher education to prepare future supply chain analyst. Notably, the article is positioned for bridging the gap between the use of analytics in SCM, both from academia and the industry perspective.

Machine learning classification of surface fracture in ultra-precision diamond turning using CSI intensity map images