Deep Dive

Pre-AI Supply Network Optimization

Supply network optimization solutions pre-date the emergence of AI enabled tools in the space, and have historically involved manual Excel-based modeling and basic scenario planning tools, whilst leveraging only limited structured data drawn primarily from ERP (Enterprise Resource Planning) and production management (Manufacturing Execution Systems, MES) systems. Although an improvement over “no solution”, such manual methods often lead to inefficiencies, suboptimal investment decisions, reactive-only decision-making, and a higher risk of overspending or underutilizing resources.

Key challenges include:

• Fragmented data sources, complicating comprehensive visibility into long-term capacity requirements and constraints.
• Slow, resource-intensive modeling processes that often lagged behind dynamic market changes.
• Difficulty in accurately predicting and responding to demand volatility and new product introductions.

AI-Enabled Supply Network Optimization

Embedding AI models within supply network optimization tools enables significantly enhanced strategic planning and decision-making capabilities.

Comprehensive Data Integration

Modern AI-driven supply optimization tools aggregate structured and unstructured data into unified models, including structured data from integrations with:

• Enterprise Resource Planning (ERP) systems
• Manufacturing Execution System (MES)
• Advanced Planning Solutions (APS)
• Transportation Management Systems (TMS)
• Demand forecast data

Unstructured sources of information, such as competitor announcements, third-party economic forecasts, geopolitical developments, supplier communications, and market sentiment from news or social media can also be used to inform key supply network design decisions. These datapoints from “unstructured” information can be gathered and integrated with the assistance of Large Language Models, and processed with platforms like HASH, which specialize in integrating such unstructured information alongside structured data into highly-trustable knowledge graphs.

Predictive & Prescriptive Analytics

Supply network optimization tools can use both new generative AI and traditional machine learning models to forecast long-term demand scenarios, simulate manufacturing capacities, and anticipate future constraints, combining predictive and prescriptive analytics to recommend the best investment decisions and production distributions:

• Identifying optimal locations for new facilities or expansions based on long-term growth forecasts and logistical efficiency.
• Evaluating the ROI and strategic alignment of capital expenditures for manufacturing expansions or new asset purchases.
• Suggesting site closures or reductions based on declining demand, inefficient operations, or better alternatives.

AI Copilots In Supply Network optimization

Generative AI copilots integrated into the optimization platform allow supply chain planners and executives to intuitively query complex scenarios through natural language ("What happens to capacity needs if Product X grows by 30% in APAC?") and get answers in seconds or minutes. AI copilots can:

• Auto-generate strategic scenario analyses and executive summaries.
• Provide comparative visualizations of different investment scenarios, capacity expansions, and asset optimizations.
• Accelerate strategic decision-making by quickly interpreting vast quantities of structured and unstructured data.

Agentic AI For Autonomous Strategic Optimization

In addition to the base capabilities of an AI driven optimization tool, leveraging consolidated supply network data, AI agents can autonomously initiate scenario simulations based on emerging data, identify and recommend changes in product-site distributions, or adjust capital investment plans.

This automated “red-teaming” or generation of plausible scenario hypotheses can help provide a bank of pre-computed optimization strategies ahead of “black swans” occurring in the real-world, providing firms with a playbook ready for immediate use, while more detailed/precisely tailored simulations can be run incorporating actual parameters observed.

Implementation & Enhancement

Implementing a supply network optimization tool is a great first step to gain insight and control over how a network of interconnected supply chain nodes evolves over time, ensuring it does so in an efficient and risk aware manner.

Selecting A Solution

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

• Data integration and quality: Seamless integration of both structured and unstructured data streams and rigorous master data management to ensure reliable, high-quality inputs.
• Model transparency and explainability: Ensuring recommendations can be understood, validated, and trusted by business stakeholders.
• Scalability and flexibility: Evaluating the system’s capability to expand or adapt to new products, markets, or unforeseen changes.
• Change management: Effective training, clear communication of benefits, and phased adoption to ensure buy-in from key stakeholders.
• Cybersecurity and data governance: Ensuring the system supports granular permissions and access controls consistent with your business requirements.
• 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 supply network optimization solution open-source, or is there risk of platform/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 as a standalone solution for supply network optimization, or integrated with existing tools to provide higher-quality, more up-to-date, and more comprehensive strategic supply chain decisionmaking. 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

Supply network optimization solutions delivered with HASH typically look like:

1. Baseline & cleanse data – connect key ERPs, TMS / APS / MES, forecasting systems, and specify any unstructured data sources of interest.
2. Create a current state supply network model – identify current capacity and understand production performance.
3. Add predictive & prescriptive apps – identify future capacity gaps, assess capacity requirements, recommend optimal future network design.
4. Embed autonomous agents – trigger strategy or CAPEX review in response to assumption changes, recommendation of BCP (business continuity planning) mitigations.
5. Expand to external supply and distribution – “make vs buy” decisions, distribution center optimization.
6. Continuously learn – reinforce models with new data; driving a culture of decision-intelligence and better calibrating future predictions and recommendations against observed actuals