Deep Dive

Pre-AI Control Towers

Supply chain “control towers” predate the advent of new generative AI, and aggregate streaming data from ERPs, TMS/WMS, IoT sensors, telematics, and external feeds (weather, port congestion, macro-events) into a single, personalized dashboard so teams see what is happening now and what is likely to happen next. This provides the ability to understand, triage and address potential issues in real-time, while providing alerting and monitoring capabilities to practitioners.

Some of the benefits they provide include:

• 360° view eliminates blind spots between suppliers, manufacturers, third-party logistics (3PLs) and customers, unlocking real-time visibility into goods’ locations and estimated arrival times.
• Faster root-cause issue identification reduces firefighting time and improves on-time-in-full (OTIF): early-warning signals detected may enable hours or days of extra reaction time, allowing for adaptation prior to price increases or availability constraints precluding an effective response.
• Gaps in network visibility and resiliency are clearly highlighted, and “missing data” such as ESG-related carbon, circularity and social-compliance metrics can be more easily calculated.

AI-Enabled Control Towers

By combining control towers with new AI, including generative AI (large language models) and advanced machine learning, a range of new capabilities are unlocked.

Comprehensive Data Integration

In addition to ingesting the kinds of structured data outlined above, datapoints from “unstructured” information can also be gathered and integrated with the assistance of Large Language Models. For example, information shared by suppliers, distributors and other partners in private communications (emails/calls), or competitive and macro intelligence gathered from public sources such as radio transmissions, published news reports, and social media. Platforms like HASH specialize in integrating unstructured data alongside structured data into highly-trustable knowledge graphs.

By fusing unconventional signals relating to geopolitics, the climate or supplier-health, control towers can alert planners proactively to tier-n supply risks or issues like port congestion days earlier than they may always be aware.

Predictive & Prescriptive Analytics

Machine-learning models layer on top of collected data can be used to forecast demand swings, transit time variance, or equipment failure, and then prescribe the best mitigation. Next-generation control tower solutions provide not only a descriptive overview of current data, but actionable recommendations to adjust plans before disruptions occur”.

This can help contribute to service-level improvements even as variability rises, because control towers are able to propose the least-cost, highest-service options available. It can also enable AI-driven inventory and replenishment optimization, with:

• leading large retailers feeding store-level sales, lead-time, and shelf-availability data into AI models that make billions of predictions each week, to flag impending stockouts or misplaced items and trigger proactive orders;
• sophisticated suppliers and manufacturers utilizing emerging data to dynamically reroute, reprice, and resource goods and materials on a data-driven, day-to-day (or even more frequent) basis.

AI Copilots in Control Towers

GenAI-powered copilots inside of control towers allow planners to ask natural-language questions (“Show me suppliers at risk next quarter and suggest alternatives”), speeding up human expert analysis of supply chain risks and opportunities.

GenAI research agents may also be linked to control towers to proactively conduct new supplier research, assisting in adaptive sourcing efforts, and pre-screening or self-assessing the suitability of potential incident response options.

GenAI may also be integrated directly within Control Towers to auto-generate incident summaries, pre-draft supplier emails, and automate other manual processes and tasks.

An Enabling Technology

As well as providing supply chain practitioners with direct insights into the state of supply chains via accessible dashboards, supporting observability of key supply chain metrics by human leaders, control towers are in general an enabling technology. By integrating information from many different sources into a single control tower, they provide a trusted “single source of truth” for critical business data, similar to a data lake or data warehouse (albeit with a dedicated user interface or set of dashboards for supply chain practitioner end-users).

Pre-requisite For Digital Twins

This unified single representation of business data can further support and enable “digital twins”, which are in effective models of a business, its supply chain, and operations. This can enable “What if?” simulations to be run, allowing planners to ask questions like “What if Suez closes again?” or “What if I shift 15 % of volume to rail?” and instantly see service, cost and CO₂ impacts – helping bridge planning and execution.

Nerve Center For Agentic AI

With information from across an organization’s supply chain consolidated into a single accessible form, data may be made available not only to humans via dashboards, but to AI agents, as well, supporting probabilistic and generative AI-powered agents in their decision-making, necessary to inform autonomous execution of multistep workflows (e.g. re-routing, re-pricing, or re-sourcing of goods or materials). In an IBM-commissioned survey this year (2025), 62% of CSCOs said they already saw AI agents as a speed-to-action accelerator, with organizations investing heavily in them within a supply chain operations context reaping a 61% revenue-growth premium over lagging peers. A centralized source of trusted realtime supply chain data is required in order to enable such effective agentic decision making.

Implementation & Enhancement

While control towers themselves may integrate AI capabilities, because of their important enabling role, setting up a new control tower, or enhancing an existing one, is often a first-step on the way to delivering other AI applications in supply chain management.

Selecting A Solution

When building out and utilizing control towers, it is important to consider:

• Data quality & latency: real-time integration, master-data governance, streaming data pipelines – requirements to ensuring data is up-to-date.
• Change management: upskilling planners in their usage – start by rolling out control towers as copilots to humans before relying on them in any autonomous capacity.
• Cybersecurity and data governance: 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 control tower software/solution open-source, or is there risk of platform/vendor lock-in?
• Incremental adoption: can the control tower be used to target specific use-cases initially to prove its value and deliver quick wins (e.g. inventory, freight optimisation) before broader roll-out?

HASH is an open-source platform capable of integrating information from any source, both structured and unstructured. This allows it to act independently as a Control Tower, or integrate with existing ones, to provide higher-quality, more up-to-date, and more extensive supply chain visibility. 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 at hash.ai/solutions to learn more about how our technology and services can support your supply chain.

Roadmap To Value

A typical control tower built on HASH looks like:

1. Baseline & cleanse data – connect key ERPs, TMS/WMS, supplier portals, IoT feeds, and specify any unstructured data sources of interest.
2. Launch visibility layer – real-time event dashboards, basic ML alerts.
3. Add predictive & prescriptive apps – demand forecast, transit-risk scoring, smart inventory.
4. Embed autonomous agents – exception self-healing, automated sourcing or re-routing.
5. Expand to ESG & finance – Scope-3, cash, cost-to-serve analytics.
6. Continuously learn – reinforce models with new data; driving a culture of decision-intelligence through an “OODA loop”