Restaurant & Menu Engineering — Analysis
Most dashboards answer questions. Very few tell you what to do on Monday. I rebuilt a restaurant analytics report this week and spent as much time on the narrative as the numbers. What that meant in practice: Every page ends with a written read-out panel. Not a KPI. An actual sentence explaining what the chart means and what it implies. "Labour averages 23.2% of sales, but The Bistro Table runs 60.4%, and it is a scheduling problem, not a menu problem." Colour carries meaning, not decoration. Stars are teal, Plow Horses blue, Puzzles amber, Dogs coral, and those four colours mean the same thing on every page. The menu engineering matrix uses reference lines at the actual portfolio benchmarks, so the quadrants are real thresholds rather than eyeballed corners. The icon set is hand-drawn SVG rather than stock clipart, rendered at 4x so it stays crisp on any display. Shipped in both a dark and a light theme. And a final page that does nothing but list the three model issues I found and the corrected DAX to fix them. If the numbers are wrong, the design is decoration. Six pages. One dataset. 17 venues, 167 menu items. Happy to share the DAX fix pack if it is useful to anyone.Boost Supply Chain Performance Through Data-Driven Dashboards
Why You Need This Supply Chain Management Dashboard In today’s fast-paced and complex supply chains, businesses struggle with stockouts, excess inventory, unreliable suppliers, rising logistics costs, and poor visibility across operations. These challenges lead to lost sales, wasted capital, and inefficiencies that hurt profitability. This Power BI dashboard brings all your critical supply chain data into one place, turning disconnected metrics into actionable insights. It helps you make faster, smarter decisions to reduce costs, improve service levels, and drive operational excellence across inventory, procurement, logistics, manufacturing, and sales. 1. Smart Inventory Insights & Optimization Monitor stock levels, turnover rates, and supplier lead times to balance inventory. Align procurement with demand patterns to reduce stockouts and excess stock. 2. Supplier Evaluation & Cost Efficiency Track lead time, cost, quality, and delivery volume across suppliers. Identify top-performing suppliers to control costs and ensure consistency. 3. Logistics Costing & Delivery Optimization Analyze shipping cost per unit, region, and transportation mode. Identify best routes and carriers by evaluating delivery performance and return rates. 4. End-to-End KPI Dashboard Monitor revenue, orders, shipping costs, and production quality in one view. Use demographic and product insights to forecast demand and guide strategy. 5. Production Performance & Quality Tracking Compare production targets vs. actuals to manage capacity. Reduce defects through analysis of supplier and product-specific quality issues. 6. Customer Profitability & Sales Analysis Discover high-value customers and revenue-driving demographics. Analyze sales, costs, and margins across regions and product categories. eyJrIjoiYTczMjdlNTQtZjM0OS00Y2M3LWJmYzMtMTYxNWQ3MWRiMmMxIiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9Optimising Sidebar Navigation (expandable) for Power BI Reports: Tips and tricks
I've recently developed and tested this functionality. It requires quite a bit of bookmarking but is manageable without any performance issues. To configure this efficiently, I recommend using Visual Studio Code to edit the Power BI report (PBIP) directly in the sidebar navigation. This method allows for quicker configuration compared to manual adjustments within the PBIX file. This feature is particularly useful when working with a large number of pages in Power BI visualisations. eyJrIjoiNTNjMjVhMzctNTgwMC00MzViLWEzODMtY2RiYWNiY2ExZGM2IiwidCI6IjBmNDBhZjEwLTI5MTgtNGVlMC1iMjY1LWQ2ZjAwNjc4ZjU4NSIsImMiOjh9Daylight Check: Window to Floor Area Ratio on a BIM Model
This report checks the window to floor area ratio of a residential building level by level, directly on its BIM model. Most building codes require the glazed area to be at least one eighth of the floor area: here every level gets its ratio, compared against that threshold, and the levels that fall short turn red on the 3D model. The model is an IFC file converted into tabular data with Vcad, so glazed areas, floor areas and levels come straight from the model, with no manual schedules. How to use it: - Click a level in the bar chart or in the table to isolate it. Cards, donut, table and 3D model filter together. - Use the Level slicer to jump to a specific floor. - Switch between As designed and What-if. The what-if scenario reduces the glazing on levels 2, 8 and 14 to 35% and shows which levels stop complying. - The donut shows where the glass is, grouped by band of levels. Colors on the model: blue for glazing, yellow for levels that pass, red for levels below 1/8. The ratio is computed on the gross floor area of each level, which makes it a conservative early check rather than a room by room verification.
Golden Wok Delivery Network Intelligence
From network pressure to service reliability and corridor-level action Golden Wok’s delivery network presents a connected operational challenge: understanding where delivery pressure originates, when service reliability begins to deteriorate, and which corridors require attention. The report follows a three-stage analytical journey: Network Performance → Service Reliability → Network Optimization 01 | Network Performance The first page explores where delivery pressure originates across the network. Delivery radius, traffic friction, weather conditions and zone tiers provide the context for understanding where operational pressure is concentrated and how those conditions affect delivery performance. Outcome: Identify where network conditions create the greatest delivery pressure. 02 | Service Reliability The second page focuses on what happens as delivery performance deteriorates. One of the clearest findings was the relationship between delivery duration and SLA risk. Breach rates increased sharply across longer delivery-time bands, while customer ratings remained comparatively stable. This made service reliability a stronger operational signal than the customer-rating response in the supplied data. Outcome: Understand when delivery performance begins to translate into significant SLA risk. 03 | Network Optimization The final page moves from diagnosis to action. I combined Profit Efficiency and SLA Risk to classify delivery corridors into Priority Intervention, Service Intervention, Economic Optimization, and Protect & Maintain. ABC classification then adds economic importance, helping distinguish valuable corridors that should be protected or repaired from weaker, higher-risk corridors that may warrant deeper review. Interactive tooltips provide additional corridor and intervention context without overcrowding the main report. Outcome: Prioritize corridor-level action based on economic value and service reliability. Validation Before Visualization The brief served as a checklist for testing key assumptions against the supplied data. Where discrepancies appeared, values were adjusted only when defensible or excluded when they could not be reliably interpreted. This kept the analysis grounded in the brief while allowing validated evidence to determine the final story. Final Takeaway Golden Wok became a simple decision journey: Diagnose → Understand → Prioritize The project reinforced the importance of validating assumptions before visualization and keeping each report page focused on a clear business question. A rich operational dataset does not need a crowded report. Every visual should help move the analysis closer to a decision. Data Source: September 2026 DataDNA – Golden Wok Food Delivery Analytics ChallengeF&B Sales Performance Dashboard
F&B Sales Intelligence Dashboard An interactive dashboard developed as part of my data analytics portfolio to explore sales performance across three F&B outlets in Indonesia. It uses the Anonymous Transactional Dataset from Mendeley Data, containing historical POS transactions and product metadata for January–September 2025. The dashboard covers sales KPIs, outlet performance, weekly trends, transaction intensity by day and operating hour, and product rankings. It also incorporates RFM-based transaction segmentation using K-Means, replicating a published methodology with the full dataset. Four transaction segments are summarized by volume, average spending, and total transaction value. The analysis covers 53,820 transactions with a total transaction value of approximately IDR 1.32 billion. Key findings include SHOP001 contributing nearly 49% of total transaction value and high-value transactions contributing a substantial share despite their smaller volume. Tools: Power BI and Google Colab (Python) Skills: Data modeling, DAX, KPI development, interactive visualization, RFM analysis, K-Means clustering, and insight communication. Dataset: https://data.mendeley.com/datasets/kcgf45y24m/2 Feedback on the dashboard’s design, analysis, and usability is welcome.Makeover Monday week 36 - Americans favourite season
At the start of the week I saw that at dm-p had developed and released a chart xkcd custom visual for Power BI so I thought I'd use it for this weeks Makeover Monday data on Americans favourite season. It's a very small dataset and not all of it ideal for the visuals but was a good opportunity to try it out, I even bought myself to create some pie and Doughnut charts. eyJrIjoiZjc4MTY4YjItMzNmMi00YzBiLTgyMGYtNTM3MDgxMTBmMGY1IiwidCI6IjgzNzBjZjE0LTE2ZjMtNGMxNi1iODNjLTcyNDA3MTY1NDM1NiIsImMiOjh9E-commerce Profitability Audit: Protect, Monitor or Act
Built with the dataset from the ZoomCharts 4U Report Challenge (European e-commerce, 2024–2025). The question: Sales grew 69% and contribution margin more than doubled, but which categories actually stayed profitable? Page 1 – Audit: each category gets a verdict (Protect, Monitor, Act) based on three rules: CM %, product cost % of sales, and other operating cost %. The rules are shown on the page, so the verdict is transparent. Hover over any category to see a tooltip comparing its cost lines with the healthy categories. Page 2 – Detail: a P&L variance table (current vs prior year) and a margin bridge from prior-year CM to current-year CM. Every subtotal reconciles. Key findings: Beauty, Fashion and Sports & Outdoors deliver about 79% of margin. Electronics runs at 3.4% CM, the only category in Act. Product cost rose about $97K, but most costs grew slower than sales, so CM % improved by 3.3 pp. Feedback welcome.E-commerce Profitability Audit: Protect, Monitor or Act
Built with the dataset from the ZoomCharts 4U Report Challenge (European e-commerce, 2024–2025). The question: Sales grew 69% and contribution margin more than doubled, but which categories actually stayed profitable? Page 1 – Audit: each category gets a verdict (Protect, Monitor, Act) based on three rules: CM %, product cost % of sales, and other operating cost %. The rules are shown on the page, so the verdict is transparent. Hover over any category to see a tooltip comparing its cost lines with the healthy categories. Page 2 – Detail: a P&L variance table (current vs prior year) and a margin bridge from prior-year CM to current-year CM. Every subtotal reconciles. Key findings: Beauty, Fashion and Sports & Outdoors deliver about 79% of margin. Electronics runs at 3.4% CM, the only category in Act. Product cost rose about $97K, but most costs grew slower than sales, so CM % improved by 3.3 pp. Feedback welcome.African Gig Economy & Digital Wallet Risk Analysis
For the August #DataDNA challenge, I developed a Power BI report focused on understanding fraud exposure, fraud drivers, and operational risk across a digital wallet ecosystem serving the African gig economy. The objective was to move beyond reporting metrics and answer three business questions: What is the current risk exposure? What factors are driving fraud risk? Where should controls and monitoring be strengthened? To support this analysis, the report was structured into three sections: Executive Risk Overview Assess transaction performance, fraud exposure, disputes, and reversals. Fraud Driver Analysis Identify the customer, behavioural, and transaction characteristics associated with elevated fraud risk. Operational Risk & Controls Evaluate dispute patterns, reversal behaviour, transaction timing, and market risk indicators to identify opportunities for intervention. Key Observations Fraud exposure was more closely associated with customer behaviour, transaction type, and channel usage than demographics alone. USSD channels and Cash-In transactions consistently recorded elevated fraud rates. Month-end periods showed increased cash-out activity and reversal risk. Observed fraud exposure did not always align with expected market risk levels. The report also incorporates recent Power BI enhancements, including Donut Chart Center Value and Drop-down Slicer features introduced in the August Power BI update. Source: https://datadna.onyxdata.co.uk/challenges/august-2026-datadna-african-gig-economy-and-digital-wallet-analytics-challenge/
