Deep Dive

Pre-AI Competitor Shortage Monitoring

Conventional approaches to competitor shortage monitoring rely on manual review and basic analytics, looking at a mix of structured market data, industry reports, and (in more sophisticated set-ups) occasional web scraping.

Once identified, knowledge about suspected or actual idenitfied competitor shortages typically need to pass manually from the part of the organization where the information originated (usually the sales or commercial team) to those who need to take action on those insights (typically in the supply chain).

Key challenges involved with this approach include:

• Slow detection and delayed response times, thus reducing the ability of the supply chain organization to react.
• Manual prioritization of alerts, with inconsistent accuracy, leading to missed opportunities to capitalize on competitor vulnerabilities.
• Inability to dynamically adjust forecasting or supply plans based on competitor stock fluctuations.

While regulator-run moniyoting systems like the European Shortages Monitoring Platform (ESMP) are in place, notices provided under these may lag behind other available indicators, which would provide insight further in advance.

AI-Enabled Competitor Shortage Monitoring

By integrating generative AI, including large language models (LLMs), alongside predictive machine learning models, firms can significant enhances their ability to detect and forecast competitor product shortages ahead of time, by continuously monitoring and interpreting a range of diverse, real-time data sources.

Comprehensive Data Integration

Structured data sources include:

• Market intelligence
• Sales data
• Inventory reports (typically from Enterprise Resource Management (ERP) systems)
• Regulatory filings

These are combined with unstructured sources from:

• News reports
• Social media posts
• Patient or industry forums

Utilizing these data sources in combination, AI-driven shortage monitoring tools build comprehensive, highly-accurate predictive models. Platforms specializing in unstructured data synthesis and AI-driven analytics, such as HASH, leverage knowledge graphs and Natural Language Processing (NLP) techniques to consolidate structured and unstructured data into high-confidence, actionable insights.

Predictive & Prescriptive Analytics

Advanced machine-learning algorithms evaluate competitor-related signals to forecast the probability and anticipated duration of product shortages. Predictive capabilities include:

• Dynamic scoring of signals based on source credibility and historical accuracy.
• Shortage risk forecasting based on geopolitical events, raw material shortages, regulatory actions, supply chain disruptions, and consumer sentiment shifts.

In addition, a recommendation engine can assess response options and integrate with Agentic AI for autonomous actions when time is of the essence:

• Immediate inventory increases for high-demand products at risk of competitor shortage.
• Enhanced coordination with suppliers and logistics providers to preemptively mitigate impacts.

AI Copilot For Competitor Analysis

Generative AI co-pilots allow users to query the system intuitively, for example using a prompt such as “Identify competitor products at risk in the next quarter and suggest supply adjustments.” GenAI also enables automatic drafting of reports summarizing competitor shortage impacts, mitigation actions and resulting ROI.

Implementation & Enhancement

Establishing competitor shortage monitoring as a foundational capability unlocks additional use cases through utilization of GenAI and Agentic AI, offering value across commercial and supply chain management teams.

Selecting A Solution

When selecting and implementing an AI-driven supply network optimization tool, considerations include:

• Data Quality & Timeliness: Ensuring data streams are continuously updated and validated.
• Security & Compliance: Maintaining data privacy and regulatory adherence while monitoring external sources.
• Scalability: Cloud-based versus on-premises deployment for optimal maintenance and cost-efficiency.
• User Adoption: Training users across the organisation to effectively leverage predictive insights and recommended actions.

HASH is an open-source platform capable of integrating information from any source, both structured and unstructured. This allows it to be used as a standalone solution for competitor shortage monitoring, 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 competitor shortage monitoring tool built on HASH looks like:

1. Initial integration and validation of data sources.
2. Deployment of real-time monitoring dashboards and basic predictive alerts.
3. Enhancement with advanced predictive analytics and prescriptive recommendations.
4. Integration of autonomous decision-making tools for proactive adjustments.
5. Continuous refinement through model training and feedback loops.