K-means clustering combined with principal component analysis for material profiling in automotive supply chains

At a time where available data is rapidly increasing in both volume and variety, descrip- tive Data Mining (DM) can be an important tool to support meaningful decision-making processes in dynamic Supply Chain (SC) contexts. Up until now, however, scarce attention has been given to the application of DM techniques in the field of inventory management. Here, we take advantage of descriptive DM to detect and grasp important patterns among several features that coexist in a real-world automotive electronics SC. Concretely, Principal Component Analysis (PCA) is employed to analyze and understand the interrelations between ten quantitative and dependent variables in a multi-item/multi-supplier environment. Afterwards, the principal component scores are character- ized via a K-means clustering, allowing us to classify the samples into four clusters and to derive di↵erent profiles for the multiple inventory items. This work provides evidence that descriptive DM contributes to find interesting feature-patterns, resulting in the identification of important risk profiles that may e↵ectively leverage inventory management for superior performance.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The authors want to extend grateful thanks to the editors and reviewers, whose comments have greatly improved the quality of the paper

On the dynamics of a viral marketing model with optimal control using indirect and direct methods

The complexity of optimal control problems requires the use of numerical methods to compute control and optimal state trajectories for a dynamical system, aiming to optimize a particular performance index. Considering a real viral advertisement, this article compares the dynamics of a viral marketing epidemic model with optimal control under different cost scenarios and from two perspectives: using numerical methods based on the Pontryagin's Maximum Principle (indirect methods) and methods that treat the optimal control problem as a nonlinear constrained optimization problem (direct methods). Based on the trade-off between the maximization of information spreading and the minimization of the costs associated with it, an optimal control problem is formulated and studied. The existence and uniqueness of the solution are proved. Our results show not only that the cost of implementing control policies is a crucial parameter for the spreading of marketing messages, but also that low investment costs in control strategies fulfill the proposed trade-off without compromising the financial capacity of a company.Portuguese Foundation for Science and Technology (FCT – Fundação para a Ciência e a Tecnologia), through CIDMA – Center for Research and Development in Mathematics and Applications, within project UID/MAT/04106/2013; and through Algoritmi R&D Center, under COMPETE: POCI-01-0145-FEDER-007043 within the Project Scope: UID/CEC/00319/20

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

PreprintDemand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventory-related costs. This paper introduces a multivariate approach to predict manufacturer's demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component's life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component's life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages.INCT-EN - Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção(UIDB/00319/2020

Geometry Optimization for Miniaturized Thermoelectric Generators

Thermoelectric materials capable of converting heat into electrical energy are used in sustainable electric generators, whose efficiency has been normally increased with incorporation of new materials with high figure of merit (ZT) values. Because the performance of these thermoelectric generators (TEGs) also depends on device geometry, in this study we employ the finite element method to determine optimized geometries for highly efficient miniaturized TEGs. We investigated devices with similar fill factors but with different thermoelectric leg geometries (filled and hollow). Our results show that devices with legs of hollow geometry are more efficient than those with filled geometry for the same length and cross-sectional area of thermoelectric legs. This behavior was observed for thermoelectric leg lengths smaller than 0.1 mm, where the leg shape causes a significant difference in temperature distribution along the device. It was found that for reaching highly efficient miniaturized TEGs, one has to consider the leg geometry in addition to the thermal conductivity

Development of dioctadecyldimethylammonium bromide/monoolein liposomes for gene delivery

The artificial introduction of nucleic acids (NA) into mammalian cells (transfection) has become, in recent years, a well-established procedure in basic and applied research, which allowed the study of gene function and regulation. The advances in this area have made possible the use of these methods for gene-based medicines, which constitute alternative therapeutic approaches. One of the most prominent methods is lipofection that uses cationic liposome/NA complexes (a.k.a. lipoplexes) for the complexation, transport and release of therapeutic sequences into target cells. Although yielding lower transfection efficiencies compared with viral gene delivery, lipofection vectors are much safer for medical applications because no significant mutational or toxicological risk exist. Dioctadecyldimethylammonium Bromide (DODAB)/Monoolein (MO) liposomes have recently been described as a new promising alternative to common transfection reagents, due to the pioneering application of MO as helper lipid in lipoplex formulations. In this chapter, we will review the effect of MO on the physicochemical properties of DODAB/MO liposomes and pDNA/DODAB/MO lipoplexes. How lipoplex properties may affect the interaction with different extracellular components and their cell uptake and trafficking will be discussed. The importance of lipoplex biocompatibility towards efficient gene therapy will also be approached presenting pDNA/DODAB/MO system as a lipoplex model, supporting the use of MO as new helper lipid in lipofection.FCTCOMPETEThis work was supported by FCT research project PTDC/QUI/69795/2006, which is cofunded by the program COMPETE from QREN with co-participation from the European Community fund FEDER; CFUM [PEst-C/FIS/UI0607/2011]; CBMA [Pest C/BIA/UI4050/2011]; J.P.N. Silva holds a PhD Grant (SFRH/BD/46968/2008); A. C.N. Oliveira holds a PhD grant (SFRH/BD/68588/2010)

A hybrid bi-objective optimization approach for joint determination of safety stock and safety time buffers in multi-item single-stage industrial supply chains

In material requirements planning (MRP) systems, safety stock and safety time are two well-known inventory buffering strategies to protect against supply and demand uncertainties. While the role of safety stocks in coping with uncertainty is well studied, safety time has received only scarce attention in the supply chain management literature. Particularly, most previous operations research models have typically considered the use of such inventory buffers in a separate fashion, but not together. Here, we propose a decision support system (DSS) to address the problem of integrating optimal safety stock and safety time decisions at the component level, in multi-supplier multi-item single-stage industrial supply chains under dynamic demands and stochastic lead times. The DSS is based on a hybrid bi-objective optimization approach that simultaneously optimizes upstream inventory holding costs and β-service levels, suggesting multiple non-dominated Pareto-optimal solutions to decision-makers. We further explore a weighted closed-form analytical expression to select a single Pareto-optimal point from a set of non-dominated solutions, thereby enhancing the practical application of the proposed DSS. We describe the implementation of our approach in a major automotive electronics company operating under a myriad of components with dynamic demand, uncertain supply and requirements plans with different degrees of sparsity. We show the potential of our approach to improve β-service levels while minimizing inventory-related costs. The results suggest that, in certain cases, it appears to be more cost-effective to combine safety stock with safety time compared to considering each inventory buffer independently.This work has been supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Pro-gram (COMPETE 2020) [Project No. 39479, Funding reference: POCI-01–0247-FEDER-39479]

Conceiving a Digital Twin for a Flexible Manufacturing System

Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model.Thanks are due to Elkartek 2022 project LANVERSO, and in some sections (simulations) to Basque government university group IT 1573-22

Reaction of carboxylic dyes with wool and polyamide. Part III: Effect of the activating agent

Dyes containing a carboxylic acid group had been shown to react with wool and polyamide fibres when activated with ethyl chloroformate (Parts I and II). One of the dyes, 3-aminobenzoic acid →N,N-dimethylaniline, was, in this work, activated with other chlorofirmates, so as to improve the dyeing conditions. Benzyl chloroformate was found to be a good substitute since it is not as volatile as ethyl chloroformate, which suggests that it will be easier to apply in practical dyeing conditions. The yield of the reaction with cyclohexylamine is similar to the one obtained with ethyl chloroformate, suggesting that the fixation of the dye on wool or polyamide will be much the same. The fastness results are also equivalent.FCT-Fundação para a Ciência e Tecnologi

Advancing logistics 4.0 with the implementation of a big data warehouse: a demonstration case for the automotive industry

The constant advancements in Information Technology have been the main driver of the Big Data concept’s success. With it, new concepts such as Industry 4.0 and Logistics 4.0 are arising. Due to the increase in data volume, velocity, and variety, organizations are now looking to their data analytics infrastructures and searching for approaches to improve their decision-making capabilities, in order to enhance their results using new approaches such as Big Data and Machine Learning. The implementation of a Big Data Warehouse can be the first step to improve the organizations’ data analysis infrastructure and start retrieving value from the usage of Big Data technologies. Moving to Big Data technologies can provide several opportunities for organizations, such as the capability of analyzing an enormous quantity of data from different data sources in an efficient way. However, at the same time, different challenges can arise, including data quality, data management, and lack of knowledge within the organization, among others. In this work, we propose an approach that can be adopted in the logistics department of any organization in order to promote the Logistics 4.0 movement, while highlighting the main challenges and opportunities associated with the development and implementation of a Big Data Warehouse in a real demonstration case at a multinational automotive organization.This work was supported by FCT–Fundação para a Ciência e Tecnologia—within the R&D Units Project Scope: UIDB/00319/2020 and doctoral scholarship grants: PD/BDE/142895/2018 and PD/BDE/142900/2018

Radiographic interpretation using high-resolution Cbct to diagnose degenerative temporomandibular joint disease

The objective of this study was to use high-resolution cone-beam computed images (hr-CBCT) to diagnose degenerative joint disease in asymptomatic and symptomatic subjects using the Diagnostic Criteria for Temporomandibular Disorders DC/TMD imaging criteria. This observational study comprised of 92 subjects age-sex matched and divided into two groups: clinical degenerative joint disease (c-DJD, n = 46) and asymptomatic control group (n = 46). Clinical assessment of the DJD and high-resolution CBCT images (isotropic voxel size of 0.08mm) of the temporomandibular joints were performed for each participant. An American Board of Oral and Maxillofacial Radiology certified radiologist and a maxillofacial radiologist used the DC/TMD imaging criteria to evaluate the radiographic findings, followed by a consensus of the radiographic evaluation. The two radiologists presented a high agreement (Cohen’s Kappa ranging from 0.80 to 0.87) for all radiographic findings (osteophyte, erosion, cysts, flattening, and sclerosis). Five patients from the c- DJD group did not present radiographic findings, being then classified as arthralgia. In the asymptomatic control group, 82.6% of the patients presented radiographic findings determinant of DJD and were then classified as osteoarthrosis or overdiagnosis. In conclusion, our results showed a high number of radiographic findings in the asymptomatic control group, and for this reason, we suggest that there is a need for additional imaging criteria to classify DJD properly in hr-CBCT images