Deep Dive

Logistics risk scanning in supply chains involves the continuous and proactive identification, assessment, and mitigation of potential disruptions and vulnerabilities that could impact the safe and timely delivery of goods.

This encompasses monitoring a wide array of factors, including geopolitical events, natural disasters, supplier solvency, transportation network stability, and regulatory changes, all of which can introduce risks to product integrity, supply continuity, and compliance.

By leveraging advanced analytics and real-time data, these systems aim to provide early warnings and actionable insights, enabling pharmaceutical companies to anticipate and respond effectively to emerging threats.

Without a robust system for logistics risk scanning, supply chains may be highly susceptible to unforeseen disruptions, leading to severe consequences. A lack of foresight can result in product shortages. In biopharma, this may critically impact patient's ability to access to essential medications and compromise public health. In food supply, the consequence may be empty supermarket shelves. Across all industries, financial repercussions can also be substantial, including increased operational costs due to emergency logistics, inventory write-offs from spoilage or loss, and significant revenue loss from missed market opportunities.

The inability to ensure consistent supply may furthermore damage a company's reputation, and erode trust among consumers. In industries like biopharma this trust may be compromised among both patients and healthcare providers, with stockouts leading to regulatory scrutiny and penalties for failing to meet supply obligations.

Comprehensive data integration

Effective implementation of logistics risk scanning necessitates comprehensive data integration from diverse internal and external sources.

Internally, this includes data from:

• Enterprise Resource Planning (ERP) systems for inventory and order management
• Transportation Management Systems (TMS) for logistics and delivery statuses.
• Quality Management Systems (QMS) for product integrity records.

Externally, it requires integrating real-time feeds from one or more of:

• Global news agencies,
• Weather forecasting services,
• Geopolitical risk databases,
• Shipping and customs data,
• Social media sentiment analysis to capture a broad spectrum of potential risk indicators.

This integrated data ecosystem is crucial for a holistic and predictive view of the supply chain's vulnerabilities.

AI-enabled Logistics Risk-Scanning

The benefits of AI-enabled logistics risk scanning can be transformative, moving from reactive crisis management into proactive resilience building.

Proactive, not reactive

AI algorithms can process and analyze vast, disparate datasets at speeds and scales impossible for human analysts, identifying subtle correlations and weak signals that indicate emerging risks. This predictive power allows companies to anticipate disruptions, such as port closures or sudden demand surges and model the potential impact of various scenarios.

Prescriptive response

AI can support novel exploration of potential responses to unforeseen events, and help map incidents and occurrences to suggested courses of action that may already be covered by existing business continuity plans.

Implementation & Enhancement

Selecting A Solution

When building out and utilizing logistics risk management solutions, it is important to consider:

• Data quality & latency: real-time integration, master-data governance, streaming ETL – requirements to ensuring data is up-to-date.
• Change management: upskilling shipping coordinators in their usage – start by rolling out Logistics Risk Scanning as copilots to humans before relying on them in any autonomous capacity.
• Cybersecurity: ensure the system supports granular permissions and access controls consistent with your business requirements, and any partner access (e.g., via portals) is similarly secure.
• Ongoing maintenance costs: does the solution run in the cloud, or must it be hosted internally/on-premises and maintained manually?
• Open-source: is the Supplier Sourcing Insights solution open-source, or is there risk of platform/vendor lock-in?
• Incremental adoption: can the solution be used to target specific use-cases initially to prove its value and deliver quick wins (e.g.,high risk routes, high value products) before broader roll-out?

Roadmap To Value

A typical logistics risk scanning and response solution built on HASH looks like:

1. Baseline & cleanse data: connect key ERPs, TMS, QMS and specify any unstructured data sources of interest.
2. Launch visibility layer: real-time event dashboards, basic ML alerts.
3. Add predictive & prescriptive apps: Recommending optimal risk mitigation strategies, performing potential risk impact modeling, issuing predicted disruption alerts, etc.
4. Continuously learn: reinforce models with new data; driving a culture of decision-intelligence through an "OODA loop".