Precision Metal Solutions – AI-Powered Order Processing

Client Overview

Precision Metal Solutions is a leading European supplier of precision-engineered metal components, serving industries such as automotive, aerospace, construction, and heavy machinery. With a 30-year legacy, a loyal customer base, and a growing catalog of over 20,000 SKUs, the company is known for reliability, technical expertise, and just-in-time delivery.

But as demand increased and product complexity grew, their internal operations started to strain under pressure. Manual workflows, outdated forecasting algorithms, and inefficient order management processes led to growing delays, rising administrative overhead, and strained customer relationships.

That’s when they approached us—TDCM.ai—for a solution.

The Challenge

Precision Metal Solutions had grown steadily for years, but they hit a bottleneck when order volume outpaced their ability to process and fulfill requests efficiently.

Their challenges fell into three main categories:

1. Order Processing Inefficiencies

Each order—especially large, customized ones—required manual input, validation, and quote generation by the sales support team. The average processing time for bulk orders exceeded 48 hours, and urgent orders often caused disruption across departments.

2. Sales Administration Overload

The sales team, comprised of technical specialists and account managers, spent nearly 60% of their time on routine administrative tasks: checking inventory, chasing approvals, compiling quotes, and manually inputting data into the ERP system. This distracted them from high-value activities like nurturing client relationships and expanding into new markets.

3. Inaccurate Forecasting and Pricing

Their legacy forecasting model was rule-based and static. It couldn’t keep up with the volatile demand patterns caused by global supply chain shifts, raw material price changes, and customer behavior trends. As a result, their pricing sometimes undercut margins—or worse, led to missed opportunities due to overcautious quoting.

They didn’t just need software—they needed intelligent, scalable automation that could evolve with their business.

Our Approach

We proposed a dual-solution strategy:

• ProcessAI to automate and streamline the entire order intake and validation pipeline.

• CalculateAI to enhance pricing intelligence, forecast demand more accurately, and guide procurement.

But before we could implement anything, we had to understand the fine details of how Precision Metal Solutions operated. We embedded ourselves within their environment and took the project in four distinct phases.

Phase 1: Discovery and Deep Process Mapping (Month 1–2)

We began with an immersive discovery period. Our team of AI engineers, process analysts, and industrial workflow consultants worked alongside Precision Metal’s internal IT, operations, and sales departments to map the entire order lifecycle—from quote request to fulfillment.

We conducted:

• Workshops with sales, operations, and finance teams

• System audits of the existing ERP and CRM tools

• Data audits of historical sales, quotes, inventory movements, and delivery timelines

This phase helped us surface two key insights:

• 30% of order delays were caused by data validation and quote adjustments (often repeated multiple times).

• Sales forecasting models were overly conservative, leading to excess inventory of slow-moving parts and stockouts of fast-selling ones.
  

Phase 2: Solution Design and Prototyping (Month 3–4)

Armed with a clear understanding, we got to work building and testing two key solutions:

✅ ProcessAI

This intelligent workflow engine used natural language processing (NLP) and computer vision to ingest incoming RFQs (quotes via PDF, Excel, email), extract product and volume data, validate it against existing inventory and specs, and generate quotes automatically.

Key features:

• Order intent recognition via NLP

• Intelligent form parsing and error checking

• Approval workflows for custom pricing or urgent requests

• API integration with the ERP system (SAP)
  

✅ CalculateAI

This engine leveraged machine learning algorithms to predict demand patterns, optimize price points based on market fluctuations, and make procurement suggestions for parts based on lead times, historical volume, and supplier reliability.

Key models included:

• Random forest regression for demand forecasting

• Dynamic pricing algorithms based on competitor data and raw material indexes

• Anomaly detection for spotting sudden deviations in customer order behavior

We built a sandbox environment and tested both solutions with live data, enabling stakeholders to assess the recommendations without committing to action just yet.

Phase 3: Pilot Launch and Feedback Loop (Month 5–6)

We rolled out the solution to two departments and five sales reps for a six-week trial.

Results were almost immediate:

• ProcessAI cut average order processing time from 48 hours to under 24, even for complex multi-item quotes.

• CalculateAI produced pricing suggestions that were, on average, 6% more profitable, while still remaining competitive in the market.

• Sales reps reported a 40% reduction in admin workload, allowing them to spend more time on strategic accounts.

The pilot also gave us valuable feedback:

• Some quote generation logic needed adjustment for highly customized parts.

• We added a "human override" dashboard so reps could tweak AI suggestions for special circumstances.

• We refined the visual interface to show forecast confidence intervals, improving transparency and trust.
  

Phase 4: Full Deployment and Optimization (Month 7–9)

With the pilot declared a success, the company greenlit a phased rollout across all sales and operations departments. Over the next three months, we:

• Trained 60+ team members through workshops and onboarding modules

• Integrated the tools fully with their SAP system and internal document management software

• Set up Power BI dashboards for leadership to monitor KPIs and AI recommendations in real-time

We also instituted monthly performance reviews to fine-tune the models and ensure consistent results.

Results

The final impact after 6 months of full deployment was impressive:

✅ Order processing time reduced by 50% — thanks to automation from inquiry to quote.✅ Sales team workload decreased by 40% — freeing up time for high-value client engagement.✅ AI-powered forecasting improved supply chain efficiency — leading to a better balance of inventory and fewer urgent procurement costs.✅ Customer satisfaction increased significantly — due to faster response times and more accurate quoting.

Perhaps the most telling feedback came from the Operations Director, who said:

"TDCM.ai’s AI solutions transformed our operations, allowing us to process orders faster and scale with confidence."

Technology Stack

• ProcessAI: Python, FastAPI, AWS Lambda, NLP via spaCy and OpenCV

• CalculateAI: Scikit-learn, XGBoost, Pandas, NumPy

• Data Storage: AWS RDS and Snowflake

• Visualization: Power BI and Streamlit dashboards

• Integration: SAP APIs, Zapier for interim automation, internal CRM connectors

  
  

Lessons and Challenges

Like all large-scale digital transformations, we hit a few bumps:

• Data inconsistency from legacy systems required extra time for cleaning and normalization.

• Change resistance from experienced team members who were used to manual control—overcome through workshops and AI explainability features.

• Real-time integration latency during peak load periods, which we solved by optimizing caching and batch processing.
  

What made the project successful was collaboration—across both our team and the client’s—and a shared belief in using AI not just as a tool, but as a strategic asset.

What’s Next

With the foundation in place, Precision Metal Solutions is now looking to extend AI applications into:

• Predictive maintenance on production machines

• AI-driven procurement negotiation modeling

• Real-time logistics optimization

The future is intelligent—and they’re ready for it.

Explore ProcessAI