What are the most effective tools and methods for supply chain data analysis and visualization?

Estou convencido de que a ferramenta mais eficaz para análise e visualiza??o de dados da cadeia de suprimentos é o quadro de gest?o visual e a rotina de gest?o que deriva do quadro através do Gerenciamento Diário. é uma vantagem contar com sistemas de informa??o digital e usar das fontes de dados a favor do processo, mas é uma ilus?o acreditar que apenas dispor ou ter os dados será suficiente para que sejam bem usados na tomada de decis?o no gemba e no momento certo. Intervir na medida certa é a condi??o para a melhoria contínua e os quadros de gest?o visual s?o preciosos para isso.

While Excel remains foundational, especially for basic tasks and smaller datasets, the integration of Python and AI in supply chain data analysis is needed. This combination not only enhances the accuracy but also empowers organizations to make more informed decisions smoothly in today's dynamic business environment.

Effective tools for supply chain data analysis and visualization include BI platforms (Tableau, Power BI), ERP systems (SAP, Oracle), SCM software (JDA, Kinaxis), advanced analytics platforms (SAS, IBM SPSS), data visualization tools (D3.js, Plotly), and cloud-based platforms (AWS, Google Cloud). Key methods involve descriptive analytics for past data, predictive analytics for forecasting, prescriptive analytics for recommendations, real-time analysis for immediate decisions, what-if analysis for scenario exploration, and geospatial analysis for optimizing logistics. Combining these tools and methods enhances decision-making and operational efficiency.

Start with the KPIs the business is measuring for a particular metric. The KPI alone may not be sufficient to provide any insight, so there always needs to be drill downs into deeper levels on what the KPI numbers mean. For example, a metric for stock outs per category may be available for the current week and a previous week. The category manager may want to analyze why there was an increase in the metric in the current week. He would want to drill down to see which product in the category caused the increase. Then he may want to see in which region or store the impact occurred. Ultimately, the root cause of the issue needs to be understood and if not, a dashboard may only be partially useful.

Numerous methods exist to analyse the data. It always helps to start with user requirements. Understand who is the user / audience for this data, define the metrics that each user needs to monitor to perform their job better and choose the analysis method accordingly. E.g. 1. Warehouse manager will need to see the key performance indicators to monitor the health of operations 2. Demand planner will need to see the historical trends and planned vs actual demand trend etc.

O método de análise dos dados é um meio para melhoria que deriva do tipo de problema que se quer resolver. Sua escolha depende do propósito (finalidade) e segue uma abordagem situacional, a depender também das especificidades do processo, do nível de capacita??o das pessoas e de características culturais. Fato é que: para lidar com situa??es complexas na gest?o da cadeia de suprimentos, deve-se recorrer a métodos simples e eficazes. Mais que a escolha de um método robusto, é importante fechar o ciclo de análise e corre??o do problema através do PDCA.

Before diving into data analysis and visualization, clearly define the type of analysis required to fit your purpose. Whether you need descriptive, diagnostic, predictive, or prescriptive insights, selecting the appropriate method ahead of time ensures that the analysis effectively addresses your questions and supports your decision-making.

Supply chain data analysis methods depend on the goals of the organization and the level of maturity in using the data. 1. It is always beneficial to start with basic descriptive statistics to identify average lead times, standard deviation of demand etc. 2. To go to the next level, we can utilize these descriptive statistics to identify seasonality, trends, purchasing patterns etc. and make predictions. 3. Predictions made will need validation, which can be done by enhancements in continuous data monitoring and real time data capturing to improve forecasting methods.

Tableau:It integrates with various data sources, enabling real-time analytics and visualization of key performance indicators (KPIs) such as inventory levels, demand patterns, and logistics efficiency. Power BI (Business Intelligence): Developed by Microsoft, Power BI is widely used for creating visually compelling reports and dashboards. It connects seamlessly with multiple data sources, including Excel, SQL databases, and cloud services, to analyze supply chain data. Qlik Sense: Qlik Sense provides intuitive data visualization and exploration capabilities. Qlik Sense supports data integration from various sources and facilitates collaborative decision-making through interactive visualizations.

?? Boost Your Supply Chain Optimization with These Top Data Visualization Tools and Methods! ?? Effective supply chain optimization requires clear, actionable insights. Here are the most impactful tools: Tableau: Intuitive dashboards and real-time analytics. Power BI: Seamless integration with Microsoft products. QlikView: Associative data indexing for deeper insights. D3.js: Customizable and powerful visualizations. Heatmaps and Geospatial Analysis: Visualize logistics and warehouse efficiency. Real-Time Dashboards: Monitor KPIs and make data-driven decisions. What tools do you use to optimize your supply chain? Share your thoughts below! ?? #SupplyChain #DataVisualization #Optimization #Tableau #PowerBI #Logistics #Analytics

Os quadros da rotina de Gerenciamento Diário s?o indutores da jornada de melhoria contínua na cadeia de suprimento porque além de servir como ferramenta de visualiza??o de dados, contém a conex?o com o propósito, variáveis de controle em escala suficiente para permitir acompanhar a performance e intervir na medida certa, e o ciclo de solu??o de problemas. Deve-se questionar dashboards ou instrumentos de visualiza??o de dados que n?o se conectam com o propósito de valor ou ainda n?o estimula o ciclo de análise e solu??o de problemas.

Presenting supply chain data effectively is crucial for making informed decisions. Dashboards offer a dynamic way to keep an eye on key performance indicators, ensuring that we stay aligned with our goals. Maps help us understand the geographical spread of our operations, making it easier to optimize logistics. Infographics, on the other hand, are perfect for storytelling, helping us communicate complex data in a way that's engaging and easy to understand for all stakeholders.

Streamlining drayage with tech and data is a game-changer these days. A great example is using IoT devices and GPS tracking. By equipping trucks with IoT sensors and GPS, you can monitor their location, speed, and even the condition of the cargo in real-time. Pair this with AI-powered route optimization tools, and you can figure out the best routes to avoid traffic jams and minimize fuel consumption. Think of it as Google Maps on steroids, specifically for your drayage operations.

Effective tools for supply chain data analysis and visualization include Tableau, Power BI, and QlikView for BI; Python with Pandas and Matplotlib, and R with ggplot2 for custom analysis; SAP SCM and Oracle SCM Cloud or any 3rd party tools for management; TensorFlow and PyTorch for ML; and ArcGIS and Google Maps API for geospatial analysis. Methods include descriptive, predictive, and prescriptive analytics, and geospatial analysis. Visualization techniques involve dashboards, heat maps, and network diagrams. Generative AI (Gen AI) enhances these by automating insights, augmenting data, processing unstructured data, simulating scenarios, enabling interactive queries, and detecting anomalies, optimizing decision-making and efficiency.

Most companies have a natural tendency to upper estimate the quality of the data that they possess but when it comes to the real analysis and application of that data, it's only then they realize that the quality of data isn't as good as they thought it to be at the start of such project. There is also a bias to act upon anything with a higher level of trust that is coming out of a system/software/analysis and that eventually results in a disaster because we often overlook the quality of data that we feed to the system. Avoid that!

Implement Electronic Data Interchange (EDI) systems to streamline communication & collaboration with suppliers. In our automotive parts manufacturing business, we adopted an EDI system to automate order processing and invoicing with our suppliers. This reduced manual errors by 90% & improved order fulfilment speed. Documenting the EDI integration process and its benefits ensured seamless communication and efficient supply chain operations. Use cloud-based platforms to enhance supply chain visibility & scalability. In our global supply chain, we adopted a cloud-based supply chain management platform that provided real-time visibility into inventory levels, shipment statuses, and supplier performance across multiple regions.

A prática do pensamento lean para a gest?o da cadeia de suprimentos deve ser considerada, junto de suas ferramentas, para uma eficaz e eficiente gest?o dos dados para promo??o da melhoria. Ainda, devemos sempre lembrar a máxima de Taiichi Ohno: "os dados s?o importantes, porém coloco mais ênfase nos fatos".