PDCA Protocol to ensure a Data-Driven Approach for Problem-Solving

Problem-solving based, as much as possible, on real data, expert knowledge, and on-field observation are quite desired objectives. However, it creates several difficulties on deployment in real situations. In this work, a data-driven version of the well-known PDCA cycle is proposed for continuous improvement within a general class of problems represented by key performance indicators (KPI). Such class is wide enough to accommodate several real problems but still has a controlled level of complexity that allows defining a general data-driven protocol that covers all the (sub)steps of the cycle. New approaches and alternatives in the literature are discussed. A brief example of one of the steps of the protocol is given with real data from a company that adopts many of the new Industry 4.0 technologies.publishe

Análise e melhoria do processo de abastecimento de Angola numa empresa de construção civil

Nos dias que correm, a palavra “melhoria” já se encontra bem enraizada no vocabulário das empresas. Progressivamente, tem aumentado uma perceção generalizada de que o ponto ótimo de performance dos processos das empresas ainda não foi atingido, e há uma necessidade permanente em rever as metodologias utilizadas. À luz desta premissa, surge o presente relatório que apresenta um estudo e análise dos processos inerentes à cadeia de abastecimento do mercado de Angola, na empresa portuguesa de construção Mota-Engil. O mercado de Angola, que revela maior expressividade no volume de negócios da empresa, é apoiado pela Supply Hub Portugal quando os seus fornecedores locais não conseguem atender determinadas necessidades de produtos. Desta forma, o alvo de estudo neste estágio foi o processo de abastecimento do mercado angolano, especificamente quando apoiado por esta Supply Hub, através do processo de compra internacional. Assim, os objetivos primários neste estágio foram o cálculo do Lead Time entre as várias etapas deste processo de abastecimento, bem como do Lead Time global da cadeia. Este cálculo permitiu identificar as etapas críticas do processo, bem como delinear aquelas que seriam passíveis de mudança. Por conseguinte, o objetivo final foi a redução do Lead Time global de abastecimento. A metodologia utilizada para execução deste estágio passou, inicialmente, por uma aquisição progressiva de conhecimentos relativamente ao problema e toda a sua envolvente. Para auxiliar este conhecimento, procedeu-se ainda a uma consulta da bibliografia disponível relativa ao tema. Seguidamente, fez-se uma identificação e definição clara do problema a estudar, bem como dos objetivos a atingir. Posteriormente foi desenvolvido um plano de ação para a resolução do problema, seguido de mensuração do impacto que a implementação deste plano poderá produzir, através de uma estimativa dos dados históricos de 2018 e 2019. Para a empresa, a implementação correta deste plano de ação permitirá aumentar a eficiência no abastecimento de Angola, em aproximadamente 15%. Isto é, o Lead Time global de abastecimento é reduzido em 20 dias, o que produzirá uma melhor resposta às obras deste mercado, permitindo ainda uma redução nos custos referentes ao atraso na entrega dos produtos às obras.Nowadays, the word "improvement" is well rooted in the companies’ vocabulary. Progressively, it has increased a widespread perception that the optimum performance point of the companies’ processes has not yet been reached and so there is a permanent need to review the adopted methodologies. Considering this premise, the following report presents a study and analysis of the supply chain processes from the Angolan market, at the Portuguese construction company Mota-Engil. The Angolan market, which has greater expressiveness in the company's turnover, is supported by the Portuguese Supply Hub when its local suppliers are unable to meet certain product needs. This way, the target of study for this internship was the Angolan supply process, specifically when supported by this Supply Hub, through the international purchase process. The primary objectives in the internship were the calculation of the Lead Time between the various stages of this supply process, as well as the global Lead Time of the chain. This calculation allowed to identify the critical stages of the process, as well as to outline those that could be changed. Therefore, the ultimate goal was to reduce the global supply Lead Time. The methodology used to carry out this internship, initially, went through a progressive acquisition of knowledge regarding the problem and all its surroundings. To assist this knowledge, it was consulted the available bibliography. After this, the problem to be studied was clearly identified and defined, as well as the objectives to be achieved. Subsequently, an action plan was developed to solve the problem, followed by the measurement of the plan’s implementation impact, through an estimation of the historical data for 2018 and 2019. For the company, the correct implementation of this action plan will increase efficiency in Angola’s supply, by about 15%. That is, the global supply Lead Time is reduced in 20 days, which will lead to a better response to the construction works in this market, while also allowing a reduction in costs inherent to the delay in the delivery of products.Mestrado em Engenharia e Gestão Industria

A modular IoT-based architecture for logistics service performance assessment and real-time scheduling towards a synchromodal transport system

Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs.publishe

Performance Evaluation and Explainability of Last-Mile Delivery

The demand for last-mile delivery (LMD) services worldwide increased following online sales growth, so better methods to assess efficiency issues are paramount. This work explores a data-driven approach to evaluate LMD services and inform logistics service providers about possible improvement directions. It uses multi-directional efficiency analysis to benchmark LMD services based on process variables, such as delivery time and service cost. Then, by fitting machine learning models and using explainability algorithms with new metrics, characterizes factors that influence LMD performance. Early discussions with experts show that the approach produces understandable and integrable results that generate valuable insights, e.g., regarding the impact of each variable on service quality informing the direction for further improvement action.publishe

Workers benchmarking using multi-directional efficiency analysis in a manufacturing production system

The human factor plays a relevant role in all manual or partially automatic production systems, specially, the ones showing reliable and balanced dynamics. In the literature, parametric or survey-based models are quite common for performance evaluation of production workers. In this work, multi-directional efficiency analysis is used instead, for root cause analysis of product reworks and bottlenecks occurrence, according to four worker-related parameters: experience time, wage, delay time and response time. The approach allows to identify individual inefficiencies per tuple worker/working shift and to cluster them according to similar inefficiency parameters. In addition, this work opens a path to new applications of multi-directional efficiency analysis to problems in the manufacturing industry.publishe

Understanding and predicting process performance variations of a balanced manufacturing line at Bosch

Industry 4.0 takes advantage of data-driven approaches to improve manufacturing processes. Root cause analysis (RCA) techniques are naturally required to support the identification of reasons for (in)efficiency processes. However, RCA methods tend to be sensitive to data perturbations and outliers, compromising the confidence of the results and demanding the implementation of robust RCA approaches. Here, methods of graph theory (queue directed graphs), operational research (multi-directional efficiency analysis), machine learning (extreme gradient boosting), and game theory (Shapley analysis) are merged together, in order to obtain a robust approach that is able to benchmark the workers acting on a discrete manufacturing process, determine the relevance level of process variables regarding a worker belonging to the (in)efficient group, and predict the worker performance variation into its next working session. A use case at Bosch ThermoTechnology is analysed to show the methodology’s applicability.publishe

Benchmarking and Prediction of Entities Performance on Manufacturing Processes through MEA, Robust XGBoost and SHAP Analysis

Determining the reasons for process variability of manufacturing processes is generically quite demanding. In the era of big data and Industry 4.0, data-driven root cause analysis (RCA) techniques are required to support the identification of such reasons. However, an important issue with classical RCA methods is their sensibility to data perturbations. In fact, adversarial data perturbation is currently one of the hot topics in the literature. Such sensibility phenomena requires the implementation of robust RCA approaches. Here, methods of operational research (multi-directional efficiency analysis), machine learning (eXtreme Gradient Boosting), and game theory (Shapley values) are merged, to obtain a robust approach that can (1) benchmark entities acting on a manufacturing process, (2) determine the importance level of process variables regarding an entity belonging to the (in)efficient group, and (3) predict the performance of the entity’s future work sessions. A use case at Vista Alegre Atlantis S.A., a Portuguese leader company that manufactures porcelain tableware, high-quality glass and crystal, is analysed to show the methodology’s success.publishe