How to Visualize Data in Supply Chain Analytics

Demand & supply planning = insane number crunching + charts This document shows Excel and Power BI for demand and supply planners: Forecasting ↳ Excel: build and test logic; e.g. apply a 3-month moving average to a stable SKU ↳ Power BI: visualize forecast performance & automate exception alerts; e.g. flag a demand spike after a competitor exits and the forecast adjusts Real-time Insights ↳ Excel: ad hoc analysis or quick checks; e.g. download stock levels from ERP to run a quick pivot table ↳ Power BI: for live dashboards & exception monitoring; e.g. see low safety stock alerts triggered automatically when inventory drops below thresholds Visualization ↳ Excel: for quick visual tests or when working offline; e.g. build a bar chart to compare production vs. capacity by plant ↳ Power BI: to scale dashboards, track trends, and share insights; e.g. use a heatmap to spot which SKUs are overloading capacity next week Data Integration ↳ Excel: + Power Query to connect and shape data; e.g. download data from SAP & supplier files, then use Power Query to build a tracker ↳ Power BI: to centralize & automate integrated datasets for faster decisions; e.g. set up Power BI to pull fresh data daily from SAP Automation ↳ Excel: for light automation; e.g. use Power Query to clean incoming sales data and refresh reports with a single click each week ↳ Power BI: to scale reporting & alerts; e.g. dashboards refresh daily and send alerts (Power Automate) when inventory drops below safety stock Customization Capabilities ↳ Excel: for quick, flexible modeling; e.g. build a custom safety stock calculator tailored to SKU-level rules ↳ Power BI: to create scalable, reusable analytics and dashboards; e.g. build DAX measures to calculate dynamic KPIs and visuals to highlight service risk Scenario Analysis ↳ Excel: to build and validate logic; e.g. change lead times in a supply model to see how it affects inventory levels ↳ Power BI: to run multiple scenarios at scale; e.g. simulate a 10% demand surge and instantly sees the impact on service levels, capacity, and cost Any others to add?

Tables miss the big picture. Graphs unlock deeper insights. When your data is too complex, key insights stay hidden. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗯𝗿𝗶𝗻𝗴𝘀 𝗰𝗹𝗮𝗿𝗶𝘁𝘆—𝗳𝗮𝘀𝘁. That’s where tools like Neo4j Bloom come in. Visualization platforms transform connected data into an intuitive experience anyone can explore. No complex queries, just patterns and insights at your fingertips. It’s like a search engine for your graph data. Type a name, concept, or relationship and instantly see the connections. If you are using Neo4j and Bloom you can leverage: ✅ 𝗖𝘂𝘀𝘁𝗼𝗺 𝗩𝗶𝗲𝘄𝘀: Adjust node colors, sizes, and labels to match your focus. ✅ 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗙𝗼𝗿𝗺𝗮𝘁𝘁𝗶𝗻𝗴: Highlight patterns or anomalies with rule-based colors. ✅ 𝗩𝗲𝗿𝘀𝗮𝘁𝗶𝗹𝗲 𝗟𝗮𝘆𝗼𝘂𝘁𝘀: Switch between org charts, geographic maps, and more. These tools become even more powerful when paired with AI. LLM integration turns natural language questions into Cypher queries. For example, asking "Which customers are most likely to churn?" can return high-risk customers in the visualization. Graph visualization tools like Neo4j Bloom bridge the gap between data complexity and business insight. They transform raw data into relationships that drive decisions. Whether you’re conducting fraud investigations or mapping customer journeys, graph visualization gives you the clarity to act. 💬What is your favorite approach to visualizing connected data? Share it in the comments. 📢 Know someone struggling to understand complex data? Share this post to help them out! 🔔 Follow me, Daniel Bukowski, for practical insights about building with connected data. 

Choosing the right chart is half the battle in data storytelling. This one visual helped me go from “𝐖𝐡𝐢𝐜𝐡 𝐜𝐡𝐚𝐫𝐭 𝐝𝐨 𝐈 𝐮𝐬𝐞?” → “𝐆𝐨𝐭 𝐢𝐭 𝐢𝐧 10 𝐬𝐞𝐜𝐨𝐧𝐝𝐬.”👇 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐡𝐨𝐰 𝐭𝐨 𝐜𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐜𝐡𝐚𝐫𝐭 𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚: 🔹 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧? • Few categories → Bar Chart • Over time → Line Chart • Multivariate → Spider Chart • Non-cyclical → Vertical Bar Chart 🔹 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩? • 2 variables → Scatterplot • 3+ variables → Bubble Chart 🔹 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧? • Single variable → Histogram • Many points → Line Histogram • 2 variables → Violin Plot 🔹 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧? • Show part of a total → Pie Chart / Tree Map • Over time → Stacked Bar / Area Chart • Add/Subtract → Waterfall Chart 𝐐𝐮𝐢𝐜𝐤 𝐓𝐢𝐩𝐬: • Don’t overload charts; less is more. • Always label axes clearly. • Use color intentionally, not decoratively. • 𝐀𝐬𝐤: What insight should this chart unlock in 5 seconds or less? 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫: • Charts don’t just show data, they tell a story • In storytelling, clarity beats complexity • Don’t aim to impress with fancy visuals, aim to express the insight simply, that’s where the real impact is 💡 ♻️ Save it for later or share it with someone who might find it helpful! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 14,000+ readers here → https://lnkd.in/dUfe4Ac6

One of the biggest challenges in data visualization is deciding 𝘸𝘩𝘪𝘤𝘩 chart to use for your data. Here’s a breakdown to guide you through choosing the perfect chart to fit your data’s story: 🟦 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 If you’re comparing different categories, consider these options: - Embedded Charts – Ideal for comparing across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴, giving you a comprehensive view of your data. - Bar Charts – Best for fewer categories where you want a clear, side-by-side comparison. - Spider Charts – Great for showing multivariate data across a few categories; perfect for visualizing strengths and weaknesses in radar-style. 📈 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲 When tracking changes or trends over time, pick these charts based on your data structure: - Line Charts – Effective for showing trends across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴 over time. Line charts give a sense of continuity. - Vertical Bar Charts – Useful for tracking data over fewer categories, especially when visualizing individual data points within a time frame.    🟩 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗖𝗵𝗮𝗿𝘁𝘀 To reveal correlations or relationships between variables: - Scatterplot – Best for displaying the relationship between 𝘵𝘸𝘰 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦𝘴. Perfect for exploring potential patterns and correlations. - Bubble Chart – A go-to choice for three or more variables, giving you an extra dimension for analysis. 🟨 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Understanding data distribution is essential for statistical analysis. Use these to visualize distribution effectively: - Histogram – Best for a 𝘴𝘪𝘯𝘨𝘭𝘦 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦 with a few data points, ideal for showing the frequency distribution within a dataset. - Line Histogram – Works well when there are many data points to assess distribution over a range. - Scatterplot – Can also illustrate distribution across two variables, especially for seeing clusters or outliers. 🟪 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Show parts of a whole and breakdowns with these: - Tree Map – Ideal for illustrating hierarchical structures or showing the composition of categories as part of a total. - Waterfall Chart – Perfect for showing how individual elements contribute to a cumulative total, with additions and subtractions clearly represented. - Pie Chart – Suitable when you need to show a single share of the total; use sparingly for clarity. - Stacked Bar Chart & Area Chart – Both work well for visualizing composition over time, whether you’re tracking a few or many periods. 💡 Key Takeaways - Comparing across categories? Go for bar charts, embedded charts, or spider charts. - Tracking trends over time? Line or bar charts help capture time-based patterns. - Revealing relationships? Scatter and bubble charts make variable correlations clear. - Exploring distribution? Histograms or scatter plots can showcase data spread. - Showing composition? Use tree maps, waterfall charts, or pie charts for parts of a whole.