How can data visualization techniques improve productivity in IE?

Industrial engineers can use data to improve processes in several ways. For instance, engineers can collect data on production times, materials used, and costs associated with a particular process and use that data to identify areas of inefficiency or potential improvement. Additionally, data can be used to develop predictive models that can help identify potential problems before they occur, allowing industrial engineers to take corrective action. Finally, data can help industrial engineers monitor and adjust processes in real time to optimize performance.

It is right. "One of the main goals of IEs is to optimize the design and operation of systems, processes, and products." Systems include facilities of production of goods or services. In industrial engineering improvement, the first task is to understand the working of the design or the actual system in work. Data has to be gathered and analyzed. Data collecting and analysis are age old practices in industrial engineering. But the modern techniques and tools of data visualization can be help industrial engineers to notice the problems from real time accumulation of data.

O uso de ferramentas de melhoria contínua contribui com a construção das soluções, facilita a interação da equipes e agilidade.

In my opinion visualization of data enhances collaboration in several ways, making it a crucial point. 1. Clarity and understanding regarding cross-functional teams 2. Efficiency and waste identification 3. Root-cause-analysis 4. Data-driven discussions to support transparency ... just to name the main aspects.

I strongly believe that engineers must discern between data and information. An efficacious approach to transmute data into information involves selecting appropriate graphics and arranging visual elements thoughtfully. Nevertheless, the decisions made may be influenced by the available information. Therefore, it is imperative for engineers to initially contemplate the type of information required to make informed decisions and enhance processes and systems accordingly.

Data driven decision making is a cornerstone of industrial engineering. Industrial engineers collect and analyze data to make informed decisions regarding process improvements, resource allocation, capacity planning, and quality management. Industrial engineers rely on data to identify bottlenecks, optimize workflows, and enhance overall system performance. This emphasis on data and decision making aligns perfectly with the principles of data science, where data analysis is at the core of deriving insights and making informed decisions.

Industrial Engineering, especially on the regional level, is a business strategy function. Hence in itself, it’s sole focus is to prepare for and influence decisions. Pictures speak louder than words, so ability to visualize the story should be non-negotiable for a self respecting IE.

Using data visualization in Industrial Engineering, engineers are able to not only see and understand numbers, but also understand their stories, providing a dynamic way to interpret and interact with information that transcends traditional data analysis. By utilizing this visual approach to decision-making, patterns and trends become evident immediately, allowing faster and more effective strategic decisions to be made.

Data visualization can help us make decisions quickly. Conventional methods usually rely on reactive approach with delayed decisions, resulting in low productivity and profitability.

La IE es integradora de diversas ramas, de tal suerte que la data es muy util para la toma de decisiones en tiempo real (lead indicators). Por ejemplo, si tenemos a la vista la cantidad de veces que un Machuelo refresca las cuerdas de una pieza y se mide au desgaste, entonces podemos no solo predecir cuando se cambia el herramental, sino que probablemente se requiera cambiar antes del tiempo planeado si es que dicha herramienta se deterioro mas de lo normal. Un ejemplo mas comun es el uso de los datos estadisticos SPC, que nos ayudan a decidir en tiempo real sin tener que esperar a que se concluya un mes o una semana.

You cannot improve what you cannot monitor. I've had several personal experiences where simply monitoring activities led to improvements in the KPIs of team members, particularly those related to the SLAs.

Visualization of Problems- Jidoka - Kaizen to eliminate the root cause and avoid the problem reoccurrence. In a factory Productivity is KPI - Monitoring of Hourly Production results by line leaders and production in charges to check any abnormalities to check the loss and take corrective action. traditionally one uses Production board to update the details to Visualize, in present days rapid move to digitalization one can Visualize on the Dashboards of the MES - Further easy to Record, Analyse and maintain the date by using IOT ,AI & machine learning to move things faster.

Um elemento essencial no trabalho das Engenharias Industriais (IEs) é monitorar e medir os resultados de projetos e iniciativas. A visualização de dados desempenha um papel crucial ao permitir o acompanhamento em tempo real e a apresentação interativa do progresso e impacto das ações. Técnicas como medidores, termômetros e scorecards são empregadas para exibir status e tendências dos indicadores-chave de desempenho (KPIs). Ao visualizar os dados de desempenho, é possível identificar lacunas, problemas e riscos, direcionando a atenção e intervenção necessárias.

For example, I have used data visualization in the past to explain the variability of fill levels due to the leveling of a filler. It was a great visual for the team when we were doing the Lean Six Sigma project to reduce overfills.

Performance monitoring is important to understand where is actually business going and make decisions based on that as early as possible. Data viz helps with visualizing the exact KPI that is defined and monitoring the performance easily.

Visualizar os dados de forma dinâmica com suas correlações de causa e impacto com equipes multidisciplinares traz insights para construção do conhecimento, acompanhamento dos resultados e alinhamento da estratégia.

This might be the most important aspect. Setting up a decent set op KPI’s and monitoring them will help understand where optimisations can be applied. However if you’re not able to effectively convey your insights to the decision makers, nothing will be done. Data visualisation enables you to summarise complex analysis in to simple visuals. Which are easy to understand, even when the audience was not involved in the complete study/research. This will help decisionmakers to quickly understand the findings.

As Engenharias Industriais (IEs) necessitam da habilidade crucial de compartilhar conhecimento de maneira eficiente, e a visualização de dados desempenha um papel vital nesse processo. Ela possibilita a disseminação eficaz de insights e soluções, tornando-as mais acessíveis e envolventes. Utilizando técnicas como infográficos, mapas, diagramas e ícones, é possível resumir e ilustrar descobertas e recomendações. A visualização não apenas facilita a transferência de conhecimento, mas também aumenta a retenção e compreensão do público e stakeholders tornando-se uma habilidade técnica, criativa e estratégica essencial para o sucesso nas Engenharias Industriais.

El intercambio de conocimientos comienza con el aprendizaje social. Las personas aprenden mejor a través de experiencias prácticas, interactuando con compañeros y gerentes. Necesitan información en el punto de necesidad, no durante la incorporación o una vez que el curso esté disponible.La visualización de datos en este entendido es una estrategia importante para el intercambio de conocimientos.

Un ejemplo de esto es la conformación de tableros corporativos donde se visualizan los resultados de distintas plantas, el integrador de dicha información debe poseer la experiencia, habilidad e incluso instinto de poder quitar sesgos locales de las plantas y que sean medidas de manera estándar y bajo los mismos parámetros.

Although data visualization is widely acknowledged as an essential tool for Industry 4.0, many industrial businesses have been sluggish to adopt these tools, possibly due to a lack of expertise. Many find it difficult to obtain real-time process insight because manufacturing comprises complicated multivariate systems that generate large amounts of high-velocity data. As a result, there is a huge computational requirement to gather, evaluate, and acquire insight into this data.

In the realm of Industrial Engineering (IE), the strategic application of data visualization holds the potential to significantly boost productivity. By visually representing intricate processes, data visualization offers lucid insights, facilitating the identification of bottlenecks within operations. This, in turn, becomes instrumental in informed decision-making. The power of data visualization lies in its ability to streamline intricate datasets, expediting the analysis process and empowering organizations to develop more nuanced and effective strategies for optimizing their operational workflows.

Data visualization is a powerful tool for enhancing the productivity of not only Industrial Engineers (IE) but also for all the departments involved in process. As it provids clear insights into industrial data and processes. For Example: -Efficient data interpretation -Identify trends and patterns -Process Optimization -Comparative analysis -Real time monitoring -Resource Allocation -Rout Cause Analysis Conclusively, data visualization empowers Industrial Engineers to quickly understand, analyze, and act on industrial data. Whether it's monitoring real-time production, identifying inefficiencies, or making data-driven decisions, the visual representation of data is a cornerstone for enhancing the productivity of IE.

The amount of information that results from the operations results in the behavior of the continuous and discrete variables. The issue is that they must be considered if they generate information that is related to the value strategy that is intended to be achieved in the BSC.

From my experience, the use of presentation tools such as PowerBI, that provide the clearest vision of data and analytic results, is key for the following processes: - Determination of inputs variability and their impact on the process' output(s). - Problem's understanding and its root cause(s) determination. - Adequate base for further/deeper analysis. - And the last and more important, enabling decision making at top management level.