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This repository explains Network Optimization in Supply chain management, visually represented using AI-generated illustrations powered by automated reasoning, structured decomposition, and text2image prompt engineering

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🦓 Prompt-library-for-AI-to-visualise-Network-optimisation-process-using-ART-technique

This repository explains Network Optimization in Supply chain management, visually represented using AI-generated illustrations powered by automated reasoning, structured decomposition, and text2image prompt engineering


🐢 Automatic Reasoning and Tool-use (ART)

Like the self-consistency and ToT prompt techniques, ART is a prompting technique that builds on a CoT process. It is specifically used for multi-step reasoning tasks. This technique essentially deconstructs complex tasks by having the model select demonstrations of multiple, or few-shot, examples from the task library. At the same time, the model is using this few shot breakdown, it uses predefined external tools such as search and code gen to carry out the task. Research shows that ART performs substantially better than few-shot prompting and automatic CoT for unseen tasks and matches the performance of handcrafted CoT prompts on a majority of tasks. ART also makes it more efficient for humans to update information in the task libraries, which can correct errors ultimately improve performance


🦑 Key phases of Network Optimisation

  1. Supply Chain Network Modeling
  2. Demand data & forecast integration
  3. Facility location analysis
  4. Route optimization
  5. Capacity & flow allocation
  6. Scenario & Sensitivity analysis
  7. KPI visualization & Decision support

1. Supply Chain Network Modeling

Dall-E 2: Schematic diagram of a supply chain network with factories, warehouses, retailers, connected by transportation lines. Icons for each node, global map backdrop

Imagen: Clean vector map showing supply chain network with multiple nodes and directional arrows. Icons of trucks, warehouses and plants

Stable Diffusion: Infographic showing factories, distribution centers, and customers connected by transport routes, clean layout, labeled nodes

Midjourney: Supply Chain network map, icons of factories, trucks, distribution hubs, minimalistic infographic style -v 5 -ar 16:9 -style line-drawing blueprint

2. Demand data and forecast integration

Dall-E 2: Digital dashboard displaying demand forecast graphs, sales data trends, and predictive analytics charts in a logistics control room

Imagen: Business intelligence interface showing future demand forecasts for regions, predictive analytics dashboard in modern office

Stable Diffusion: Forecasting panel with line graphs, seasonal sales patterns, demand heatmaps over regions, futuristic analytics room

Midjourney: Demand forecasting dashboard, predictive charts, heatmaps, business control room scene -v 5 -ar 16:9 -style photorealistic UX

3. Facility location analysis

Dall-E 2: Map with highlighted optimal locations for new warehouses, cost overlays, transportation time zones, and pushpin markers

Imagen: Geospatial analysis map showing potential warehouse sites, circles indicating distance from customer zones

Stable Diffusion: Analytical map with clustered optimal facility zones, logistics routes, cost evaluation layers visible

Midjourney: Warehouse location optimisation, data layers on regional map, pushpins and zone shading -v 5 -ar 16:9 -style geographic analysis

4. Route optimization

Dall-E 2: Logistics route planning interface showing optimized truck routes, minimized travel paths, time-saving algorithms on screen

Imagen: Fleet management software interface with optimal routes drawn between warehouses and retail outlets

Stable Diffusion: High-detail routing dashboard with trucks, GPS-based route planning, optimized delivery flow paths on map

Midjourney: Logistics route optimization interface, best route maps, trucks and nodes, urban delivery network -v 5 -ar 3:2 -style minimal tech

5. Capacity & Flow Allocation

Dall-E 2: Warehouse capacity planning dashboard showing flow charts, bottlenecks, and allocation indicators per distribution center

Imagen: Graphical interface showing capacity utilization by facility, flow percentages through network nodes

Stable Diffusion: Animated flow diagram of goods from factory to customer with capacity indicators, color-coded loads

Midjourney: Supply chain capacity dashboard, goods flow arrows, bottlenecks highlighted, technical interface -v 5 -ar 16:9 -style isometric logistic tech

6. Scenario & Sensitivity Analysis

Dall-E 2: Simulation dashboard comparing logistics scenarios, graphs showing cost impact from changing demand or route choices

Imagen: Sensitivity charts showing cost fluctuation by location change, scenario comparison heatmaps

Stable Diffusion: Interactive scenario analysis panel, multiple what-if models, cost variance and demand shifts

Midjourney: Logistics scenario comparison panel, graphs and sliders, KPI impact simulations -v 5 -ar 3:2 -style dashboard futuristic

7. KPI visualization & Decision support

Dall-E 2: Decision support center with big data dashboards showing supply chain KPIs: cost, service level, lead time, fill rate. Futuristic UI

Imagen: Wall-mounted display in logistics control room showing key supply chain metrics with charts and guages

Stable diffusion: Business dashboard with supply chain KPIs, performance metrics, heat maps, and timeline graphs

Midjourney: Logistics KPI dashboard, cost and delivery metrics, futuristic command center -v 5 -ar 16:9 -style data-driven cyberlogistics


🐣 Requirements

  • Prompt Engineering fundamentals
  • Network optimisation basics

"The key to Artificial Intelligence has always been the representation" -Jeff Hawkins

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This repository explains Network Optimization in Supply chain management, visually represented using AI-generated illustrations powered by automated reasoning, structured decomposition, and text2image prompt engineering

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