E1.ipynb

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    "## Exercise 1\n",
    "1. First ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity\n",
    "2. present a pain-point in that industry - something challenging that might be ripe for an Agentic solution\n",
    "3. call propose the Agentic AI solution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5b43573",
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   "outputs": [],
   "source": [
    "import sys, os\n",
    "sys.path.append(os.path.abspath(\"..\"))\n",
    "\n",
    "from OpenAISetup import Arun_OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2ccbb27",
   "metadata": {},
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   "source": [
    "question =[\"Pick a business area that might be worth exploring for an Agentic AI opportunity\",\n",
    "\"Present a pain-point in that industry - something challenging that might be ripe for an Agentic solution\",\"Propose the Agentic AI solution\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "903cbbce",
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   "outputs": [
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     "data": {
      "text/markdown": [
       "One promising business area for exploring an Agentic AI opportunity is **Supply Chain and Logistics Optimization**.\n",
       "\n",
       "### Why Supply Chain & Logistics?\n",
       "- **Complex Decision-Making:** Supply chains involve numerous interconnected decisions—demand forecasting, inventory management, route planning, supplier selection—that require dynamic optimization.\n",
       "- **Dynamic Environments:** Conditions frequently change due to delays, demand fluctuations, geopolitical shifts, and unforeseen disruptions (e.g., natural disasters).\n",
       "- **High Impact:** Improvements can lead to significant cost savings, faster deliveries, reduced waste, and better customer satisfaction.\n",
       "- **Data-Rich:** Ample data exists from sensors, transaction records, GPS, and market trends, enabling sophisticated AI models.\n",
       "\n",
       "### How Agentic AI Could Add Value\n",
       "- **Autonomous Decision Making:** Agentic AI systems can proactively identify and respond to disruptions without human intervention.\n",
       "- **Continuous Learning & Adaptation:** These agents can learn from past events and continuously optimize decisions.\n",
       "- **Coordination Across Multiple Entities:** They can negotiate and coordinate among suppliers, manufacturers, and carriers to optimize the whole network.\n",
       "- **Scenario Simulation & Planning:** Agentic AI can simulate alternatives and plan contingencies faster and more comprehensively than humans.\n",
       "\n",
       "### Potential Applications\n",
       "- Real-time dynamic routing for delivery fleets.\n",
       "- Autonomous inventory replenishment decisions.\n",
       "- Predictive maintenance scheduling for logistics equipment.\n",
       "- Supplier risk assessment and contract negotiation.\n",
       "\n",
       "In summary, Supply Chain and Logistics represent a fertile ground for agentic AI to create substantial operational efficiencies and competitive advantages."
      ],
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       "### Pain Point: **Real-Time Disruption Management and Recovery**\n",
       "\n",
       "Supply chains are increasingly vulnerable to sudden disruptions—such as traffic jams, port congestions, supplier delays, natural disasters, or geopolitical events—that can cascade through the entire supply network. Managing these disruptions in real time is a critical and complex challenge that often relies heavily on human operators who must rapidly collect data, assess situations, communicate with multiple parties, and make decisions under uncertainty.\n",
       "\n",
       "#### Why This Pain Point is Ripe for an Agentic AI Solution:\n",
       "- **Speed and Scale of Response:** Human decision-making is too slow and siloed to react optimally to fast-evolving disruptions.\n",
       "- **Complex Coordination:** Multiple stakeholders with competing priorities must be coordinated instantly (e.g., rerouting shipments, reassigning inventory, locating alternative suppliers).\n",
       "- **Dynamic Constraints:** Real-time data changes constantly—traffic conditions, weather updates, equipment status, and order urgencies—which require continuous plan updates.\n",
       "- **High Stakes:** Mistakes or delays cause costly delivery failures, lost sales, increased inventory holding costs, and damaged customer relationships.\n",
       "\n",
       "#### How Agentic AI Could Address This:\n",
       "- Autonomously monitor a wide range of live data streams (sensor feeds, news, market signals).\n",
       "- Detect emerging disruptions and immediately evaluate impact across the supply chain.\n",
       "- Proactively generate alternative recovery plans, simulate their outcomes, and select optimal actions.\n",
       "- Coordinate communications and negotiations among suppliers, carriers, and warehouses without manual input.\n",
       "- Continuously learn from past disruption responses to improve future performance.\n",
       "\n",
       "Agentic AI designed to autonomously handle real-time disruption management could drastically reduce downtime, increase resilience, and maintain seamless supply chain operations in the face of uncertainty."
      ],
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      "text/markdown": [
       "### Agentic AI Solution for Real-Time Disruption Management and Recovery in Supply Chains\n",
       "\n",
       "#### Solution Name: **SupplyPulse AI**\n",
       "\n",
       "---\n",
       "\n",
       "### 1. **Core Capabilities**\n",
       "\n",
       "**A. Comprehensive Real-Time Monitoring**  \n",
       "- Integrates and continuously ingests data from diverse sources including IoT sensors (GPS trackers, warehouse equipment, vehicle telematics), traffic and weather APIs, port authority feeds, news outlets, social media, geopolitical risk monitors, and internal ERP/WMS systems.  \n",
       "- Uses advanced event detection algorithms and anomaly detection to identify signals of potential disruptions as early as possible.\n",
       "\n",
       "**B. Autonomous Impact Analysis & Prioritization**  \n",
       "- Employs causal inference and graph-based supply chain models to dynamically assess disruption impact across tiers of suppliers, transport routes, warehouses, and customers.  \n",
       "- Prioritizes disruptions by impact severity, urgency, and cost implications, enabling focused resource allocation.\n",
       "\n",
       "**C. Proactive Alternative Planning & Simulation**  \n",
       "- Generates multiple alternative response plans autonomously such as rerouting shipments, reallocating inventory, activating backup suppliers, or adjusting delivery commitments.  \n",
       "- Uses scenario simulation (digital twin models) to forecast outcomes of each recovery plan under current and evolving constraints (traffic, weather, demand changes).  \n",
       "- Optimizes decisions by multi-objective criteria: minimize delays, costs, and carbon footprint while maximizing service levels.\n",
       "\n",
       "**D. Dynamic Coordination & Negotiation Engine**  \n",
       "- Automatically communicates with stakeholders (carriers, suppliers, warehouse managers) through integrated digital communication protocols (APIs, emails, messaging platforms).  \n",
       "- Negotiates schedules, capacities, and priorities using AI-based contract and SLA reasoning to secure resources or adjust expectations without human intervention.\n",
       "\n",
       "**E. Continuous Learning & Feedback**  \n",
       "- Tracks the effectiveness of past disruption responses and outcome deviations using reinforcement learning and causal feedback loops.  \n",
       "- Updates models and decision policies to improve response speed and quality over time.\n",
       "\n",
       "---\n",
       "\n",
       "### 2. **System Architecture**\n",
       "\n",
       "```\n",
       "                               +----------------------+\n",
       "                               | External Data Sources |\n",
       "                               | (Traffic, Weather,    |\n",
       "                               | News, Market Signals, |\n",
       "                               | IoT Sensor Feeds)     |\n",
       "                               +----------+-----------+\n",
       "                                          |\n",
       "                                          v\n",
       "                            +----------------------------+\n",
       "                            |   Data Ingestion & Fusion  |\n",
       "                            | - Real-time ETL            |\n",
       "                            | - Anomaly/Event Detection  |\n",
       "                            +-------------+--------------+\n",
       "                                          |\n",
       "                                          v\n",
       "                           +-----------------------------+\n",
       "                           | Disruption Detection &       |\n",
       "                           | Impact Analysis Module       |\n",
       "                           | - Supply Chain Graph Model   |\n",
       "                           | - Impact Prioritization      |\n",
       "                           +--------------+--------------+\n",
       "                                          |\n",
       "                                          v\n",
       "                   +--------------------------------------------+\n",
       "                   | Alternative Planning & Simulation Engine   |\n",
       "                   | - Digital Twin Models                       |\n",
       "                   | - Multi-objective Optimization              |\n",
       "                   +----------------+--------------------------+\n",
       "                                    |\n",
       "                                    v\n",
       "                 +-----------------------------+    +--------------------------+\n",
       "                 | Coordination & Negotiation  |--> | Communication Systems     |\n",
       "                 | Engine                     |    | (APIs, Messaging, Emails)  |\n",
       "                 +-------------+--------------+    +--------------------------+\n",
       "                               |\n",
       "                               v\n",
       "                     +-------------------+\n",
       "                     | Continuous Learning |\n",
       "                     | & Feedback Loop     |\n",
       "                     +-------------------+\n",
       "```\n",
       "\n",
       "---\n",
       "\n",
       "### 3. **User Interaction & Human-in-the-Loop**\n",
       "\n",
       "- **AI Dashboard:** Provides real-time disruption alerts, impact assessments, and recommended recovery plans with predicted outcomes.  \n",
       "- **Override Controls:** Human operators can adjust preferences, manually select or tweak AI-generated plans, and provide feedback for continuous improvement.  \n",
       "- **Collaboration Hub:** Enables seamless multi-stakeholder visibility and manual intervention if required.\n",
       "\n",
       "---\n",
       "\n",
       "### 4. **Implementation Roadmap**\n",
       "\n",
       "- **Phase 1: Data Integration & Baseline Monitoring**  \n",
       "  Integrate internal supply chain data with key external feeds; implement anomaly detection to establish baseline situational awareness.\n",
       "\n",
       "- **Phase 2: Disruption Impact Modeling & Prioritization**  \n",
       "  Develop supply chain digital twin, graph models, and causal impact analysis modules.\n",
       "\n",
       "- **Phase 3: Automated Planning & Simulation**  \n",
       "  Build scenario generation, outcome simulation, and multi-objective decision-making components.\n",
       "\n",
       "- **Phase 4: Coordination Engine & Communication Automation**  \n",
       "  Deploy AI negotiation agents with integrated communication protocols to engage stakeholders autonomously.\n",
       "\n",
       "- **Phase 5: Continuous Learning & Optimization**  \n",
       "  Incorporate reinforcement learning loops using historical disruption data to refine response effectiveness.\n",
       "\n",
       "---\n",
       "\n",
       "### 5. **Benefits**\n",
       "\n",
       "- **Speed:** Near-instant detection and response to evolving disruptions.  \n",
       "- **Resilience:** Multiple recovery options and rapid coordination reduce cascading failures.  \n",
       "- **Cost Efficiency:** Optimized decisions lower penalty costs, holding costs, and expedite deliveries.  \n",
       "- **Scalability:** Can handle expanding supply chain complexity and global operations seamlessly.  \n",
       "- **Adaptability:** Continuously learns from new data and improves over time.\n",
       "\n",
       "---\n",
       "\n",
       "### Summary\n",
       "\n",
       "SupplyPulse AI represents an agentic AI solution that acts as an autonomous crisis manager for supply chains—monitoring, diagnosing, planning, and coordinating recovery efforts instantaneously and continuously, thus transforming how enterprises handle real-time disruptions for superior resilience and operational excellence."
      ],
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   ],
   "source": [
    "from IPython.display import Markdown\n",
    "\n",
    "\n",
    "answer = \"\"\n",
    "for q in question:\n",
    "    question_prompt = answer + \"\\n\\n\" + q\n",
    "    messages = [{\"role\": \"user\", \"content\": question_prompt}]\n",
    "    response = Arun_OpenAI.chat.completions.create(\n",
    "        model=\"gpt-4.1-mini\",\n",
    "        messages=messages\n",
    "    )\n",
    "    answer = response.choices[0].message.content\n",
    "    display(Markdown(answer))"
   ]
  }
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