Deep Dive

Conventional Cold Chain Management

Ordinary cold chain logistics rely heavily upon passive monitoring and reactive intervention. Systems typically track temperature and humidity through manual inspections or basic sensor logs, leading to delayed visibility and slow corrective responses to excursions. This activity may occur in environments with low or no internet connectivity (e.g. during air/sea transit) and traditional approaches are highly dependent on standardized packaging, without precise dynamic risk assessment for varied routes and weather conditions. Challenges to traditional approaches include:

• Limited real-time insights
• Manual data analysis
• Reliance on predefined static shipment routes

The above challenges result in increased risk of product wastage and regulatory non-compliance. Estimates of annual losses due to cold chain failures vary, with losses of pharmaceutical goods estimated to be in the region of $35bn annually, and food industry losses exceeding $750bn per year.

AI-enabled Predictive Cold Chain Management

By shifting towards more predictive cold chain management, losses can be reduced. New AI models, analytics tools, real-time IoT monitoring and newer satellite-based or other "novel" internet connectivity solutions in combination help enhance visibilty, enable forecasting, and facilitate in-time dynamic response.

Comprehensive data integration

Platforms like HASH are able to continuously ingest structured data from:

• Internet of Things (IoT) sensors measuring temperature, humidity, and location (e.g. via GPS, or other radio/communication signals)
• Enterprise Resource Planning (ERP) systems
• Transportation Management Systems (TMS)

External data sources such as weather forecasts and traffic reports can also be incorporated. HASH converts all of this diverse data into actionable insights using highly-trustable knowledge graphs, providing a robust, continuously updated representation of products moving through the logistics network. This real-time data fusion enables precise monitoring and predictive analytics, ensuring continuous visibility and proactive management.

Predictive & Prescriptive Analytics

AI models analyze historical shipment data, ambient conditions, route profiles, and packaging effectiveness to predict the likelihood of product spoilage or temperature excursions. Leveraging predictive capabilities:

• AI agents proactively alert logistics teams to potential risks, allowing intervention before excursions occur.
• Dynamic routing capabilities integrate seamlessly, automatically suggesting alternative routes or corrective measures in real-time to ensure product integrity.

For example:

• Pharmaceutical companies using shipment history, live and historical temperature readings, and GPS data into AI-driven systems to predict and prevent excursions.
• Food industry logistics providers utilizing real-time ambient condition monitoring and AI-driven routing to dynamically reroute perishable products around adverse conditions, preserving freshness and reducing waste.

Generative AI Copilots For Manufacturing Teams

GenAI-powered copilots embedded within cold chain management platforms allow logistics operators to query risk assessments directly using natural language (“Identify shipments at highest risk for temperature excursions today and recommend alternative routes”). This accelerates decision-making, enabling teams to rapidly mitigate risks.

Additional GenAI features include automated generation of excursion reports, and actionable alerts, further accelerating response times and reducing manual workload.

Autonomous Cold Chain Management

Consolidating diverse logistics and sensor data empowers AI agents, enabling automated, intelligent decision-making for cold chain operations. AI agents can autonomously initiate corrective actions, such as adjusting routes, altering transit speeds, or triggering rapid response measures to maintain product integrity.

Use In Digital Twins

More sophisticated cold chain tracking technology can act as an enabling technology, providing centralized access to trusted real-time data on shipments and environmental conditions. When connected to an organization's digital twin this can enable virtual modeling of cold chain operations — and scenario-based simulations:

• "What if ambient temperatures rise above forecast levels?"
• "What if transit times extend due to congestion?" Instant simulation results inform risk mitigation strategies proactively.

Implementation & Enhancement

Selecting A Solution

When looking to address cold chain visibility and planning/optimization, consider:

• Data accuracy & latency: High-quality, real-time sensor data integration.
• Data availability: Connectivity solutions must enable data to be transmitted and received.
• Change management: Training teams to leverage AI-powered predictive insights, gradually shifting from manual to automated interventions.
• Cybersecurity: Robust data protection protocols for commercially-sensitive shipment and sensor data.
• Scalability & Cost-effectiveness: Cloud-hosted versus on-premises solutions, balancing maintenance and operational costs.
• Vendor flexibility: Evaluating open-source versus proprietary software to avoid vendor lock-in.

HASH is an open-source platform capable of integrating information from any source, both structured and unstructured. This allows it to be used standalone for predictive cold chain management, or integrated with execution systems in existing tools to automate response actions given tight deadlines. HASH has been built from the ground-up to utilize AI, deeply supporting both the integration of traditional machine learning and new generative AI. To find out more about our platform, visit hash.ai or contact us to learn more about how our technology and services can support your supply chain.

Roadmap To Value

A typical cold chain management model built on HASH includes:

1. Baseline & integrate data: Connect temperature, humidity, GPS sensors, packaging monitors, and external data sources.
2. Launch predictive visibility: Real-time predictive dashboards and alerts.
3. Implement prescriptive actions: AI-driven route optimization, dynamic packaging recommendations.
4. Deploy autonomous response agents: Automated intervention for excursions, dynamic routing.
5. Expand analytics capabilities: Incorporate broader ESG and regulatory reporting.
6. Continuous learning: Reinforce models using ongoing data feedback loops, continually enhancing predictive accuracy and response capabilities.