Investment strategies in Industry 4.0 for enhanced supply chain resilience: an empirical analysis

AbstractModern economies grapple with unprecedented challenges that yielded traditional supply chain resilience (SCR) ineffective, creating a race towards digital supply chain resilience (DSCR) through adopting Industry 4.0 (I4.0) strategies and technologies, with the primary goal to fortifying organizations’ capabilities in promptly and efficiently identifying, mitigating, and rebounding from disruptions. This shift highlights the critical differences between traditional SCR and the emerging DSCR paradigm. Nevertheless, the literature on DSCR, especially pertaining to precise investment strategies, remains notably limited. This research seeks to address this critical gap through an empirical investigation leveraging insights from seasoned supply chain experts in academia and industry. Distinguishing itself, the study meticulously navigates investment decisions, aiming for a striking delicate balance between avoiding over-investment risks jeopardizing profitability and steering clear of under-investment pitfalls exposing vulnerabilities. This research stands as a distinctive contribution to existing literature, offering actionable insights into the nuanced realm of DSCR, while highlighting the shifting dynamics between traditional SCR and emerging DSCR strategies. However, while insights from experienced experts offer valuable perspectives, the study is not immune to empirical challenges. Individual industry contexts may introduce variability in strategy applicability. Additionally, the dynamic landscape of technology and business practices implies findings may need periodic reassessment. Despite these limitations, the research’s implications are profound, serving as a roadmap for organizations navigating toward DSCR complexities, and for policymakers aiming towards providing efficient regulations and ecosystems that allow for harnessing I4.0 powers in enhancing an organization’s DSCR within financial constraints

Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

Smart manufacturing in the so-called Industry 4.0 age pushes the research and development of laboratory-scale proof of concepts before its deployment in pilots and real-size equipment. As such, we present a cyber-physical system (CPS) demonstration in the mining industry field engineered to autonomously manage the handling of solids flowing in a conveyor-belt that drops materials in containers, forming multiple stockpiles per belt. The CPS operates to control multiple stockpiles’ inventories using mixed-integer optimization that minimizes the square deviation of the measured inventory to their targets (heights). Within the sensing-optimizing-actuating (SOA) cycle, the CPS demonstration is performed as follows. First, the sensing (data measurement, data processing, and system evaluation) uses a deep neural network in real-time to assess the level of materials stored in transparent containers. Second, the optimizing (mathematical programming, optimization techniques, and decision-making capabilities) is performed using a flowsheet network formulation called unit-operation-port-state superstructure (UOPSS) that permits a fast solution for the position-idle-time-varying discrete manipulated variables as operational schedules. Third, the actuating (cyber-physical integration) implements a physical actuation solution through an integrated CPS environment. According to the findings of our experimentation, stockpiling process control in a smart manufacturing context has enormous potentials to control multiple stockpiles’ inventory autonomously

Educational System Resilience during the COVID-19 Pandemic—Review and Perspective

The COVID-19 pandemic has disrupted the educational system worldwide because of the restrictions imposed in response to the pandemic for the purpose of impeding the fast spread of the virus. Educational institutions and people around the world worked intensively to create contingency plans that ensured the quality and continuity of the educational system. The ability to cope with the new changes in the educational environment varied since it depended on the available technologies and level of social restrictions, among other factors. This paper aims to investigate the literature on the educational system during the pandemic, with a particular emphasis on (1) the challenges faced by students and educators during the learning process, (2) the strategies used to overcome such challenges, and (3) the roles of educational institutions and parents. Two databases were selected in this review: Scopus and Web of Science. There were five stages in the identification, screening, and assessment of the eligibility of papers, whereby 87papers were selected to be analyzed. Opportunities to ensure the continuity and quality of learning are highlighted, and a framework is derived from the literature to achieve enhanced and more resilient online educational systems. By including multiple educational levels, potential research gaps can be identified, highlighting the need for further investigation and exploration in specific educational domains—e.g., addressing behavioral, procedural, and technological challenges requires a thorough examination to achieve optimal solutions and implement reforms

Supply Chain Resilience, Industry 4.0, and Investment Interplays: A Review

ABSTRACTToday’s global supply chain (SC) is grappling with unprecedented challenges, which rendered conventional SC resilience (SCR) inadequate. In the wake of the Industry 4.0 (I4.0) age, investment in digital SCR (DSCR) relying on I4.0 technologies can potentially enhance organizations abilities to detect, avoid, respond to, and recover from disruptions promptly and efficiently. Literature most focuses on traditional SCR, while DSCR remains incipient. From this, this paper conducts a comprehensive literature review on the SCR, I4.0, and investment (INV) interplays to identify potential research gaps and avenues. It is revealed the integration of SCR-I4.0-INV as a complex and multifaceted process that requires holistic approaches. Some research gaps include the need for empirical studies on DSCR impacts, the role of organizational culture in supporting digital transformation, and investment and resilience trade-offs. This research provides insights for decision-makers and policymakers seeking to develop strategies for promoting resilient SCs in the digital transformation era

Investment strategies in Industry 4.0 for enhanced supply chain resilience: an empirical analysis

Modern economies grapple with unprecedented challenges that yielded traditional supply chain resilience (SCR) ineffective, creating a race towards digital supply chain resilience (DSCR) through adopting Industry 4.0 (I4.0) strategies and technologies, with the primary goal to fortifying organizations’ capabilities in promptly and efficiently identifying, mitigating, and rebounding from disruptions. This shift highlights the critical differences between traditional SCR and the emerging DSCR paradigm. Nevertheless, the literature on DSCR, especially pertaining to precise investment strategies, remains notably limited. This research seeks to address this critical gap through an empirical investigation leveraging insights from seasoned supply chain experts in academia and industry. Distinguishing itself, the study meticulously navigates investment decisions, aiming for a striking delicate balance between avoiding over-investment risks jeopardizing profitability and steering clear of under-investment pitfalls exposing vulnerabilities. This research stands as a distinctive contribution to existing literature, offering actionable insights into the nuanced realm of DSCR, while highlighting the shifting dynamics between traditional SCR and emerging DSCR strategies. However, while insights from experienced experts offer valuable perspectives, the study is not immune to empirical challenges. Individual industry contexts may introduce variability in strategy applicability. Additionally, the dynamic landscape of technology and business practices implies findings may need periodic reassessment. Despite these limitations, the research’s implications are profound, serving as a roadmap for organizations navigating toward DSCR complexities, and for policymakers aiming towards providing efficient regulations and ecosystems that allow for harnessing I4.0 powers in enhancing an organization’s DSCR within financial constraints.</p

Improved Swing-Cut Modeling for Planning and Scheduling of Oil-Refinery Distillation Units

Nonlinear planning and scheduling models for crude-oil atmospheric and vacuum distillation units are essential to manage increased complexities and narrow margins present in the petroleum industry. Traditionally, conventional swing-cut modeling is based on fixed yields with fixed properties for the hypothetical cuts that swing between adjacent light and heavy distillates, which can subsequently lead to inaccuracies in the predictions of both its quantity and quality. A new extension is proposed to better predict quantities and qualities for the distilled products by taking into consideration that we require corresponding light and heavy swing-cuts with appropriately varying qualities. By computing interpolated qualities relative to its light and heavy swing-cut quantities, we can show an improvement in the accuracy of the blended or pooled quality predictions. Additional nonlinear variables and constraints are necessary in the model, but it is shown that these are relatively easy to deal with in the nonlinear optimization</p

Successive LP Approximation for Nonconvex Blending in MILP Scheduling Optimization Using Factors for Qualities in the Process Industry

We develop a linear programming (LP) approach for nonlinear (NLP) blending of streams to approximate nonconvex quality constraints by considering property variables as constants, parameters, or coefficients of qualities that we call <i>factors</i>. In a blend shop, these intensive properties of streams can be extended by multiplying the material flow carrying out these amounts of qualities. Our proposition augments equality balance constraints as essentially cuts of quality material flow for each property specification in a mixing point between feed sources and product sinks. In the LP factor formulation, the product blend quality is replaced by its property specification and variables of slacks and/or surpluses are included to close the balance; these are called <i>factor flows</i> and are well known in industry as product giveaways. Examples highlight the usefulness of factors in successive substitution by correcting nonlinear blending deltas in mixed-integer linear models (MILP) and to control product quality giveaways or premium specifications in blend shops

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle