Smart manufacturing: turning forecasts into budget and capacity planning

About client

A leading US-based manufacturer specializing in precision metal processing and parts supply, the company operates large-scale facilities that manage everything from storage to processing to shipping finished goods. Serving major OEMs, it is known for high-volume output, strict quality standards, and on-time delivery across complex automotive and industrial supply chains.

Challenges

Low-visibility process with data all over the places

Clients send weekly requirements and forecasts in Excel format. Part to workcenter attributes exist somewhere else. Meaning planners had to spend hours just matching parts manually to machines before any real planning could happen.

Manual data handling causing delays

They had to manually do everything—mapping part to machinery, merging forecast vs. shipped data, updating min/max levels in ERP. This was an error-prone and time-consuming process, which to plan-vs-actual gaps and sometimes misplaced priorities (which customer, which part, which week). They wanted to be agile, make decisions instantly reducing the time taken to plan everything.

Inadequate granularity & slow decision cycles

They could deduce forecast vs. actual shipping, but it took mammoth efforts to break that down by machinery, part, or customer. So, variances couldn’t be attributed to root causes quickly. Without insights from recent performance or the ability to adjust forecasts quickly, future plans often carried higher risks of missed deadlines, excess costs, or underutilized capacity.

Solution

We built an automated forecasting & production planning system that ingests the forecast files, maps them with master data and capacity attributes, and delivers actionable dashboards to drive decision making. Key steps included:

• Automating forecast ingestion: Weekly forecast files are ingested into a unified data platform automatically, replacing many hours of manual merging.

• Data enrichment: Pulling part-level info from Plex (master data), mapping parts to work centers, using machine capability data to compute parts per hour, and determining required processing hours per part.

• Forecast vs. shipment reconciliation: Visualizing last 8 weeks plus 40-week rollout forecasts, comparing what was forecast vs. what shipped, by part, source plant, and customer.

• Forecast analysis dashboards: Delivered via Power BI showing forecasts, variance, fulfillment status, fulfilment variances, customer wise forecasts, and detailed drilldowns; with near-real-time updates so planners can act quickly.

• We also built another set of dashboards combining market research forecast data shared by a research organization. This highlights monthly part requirements, budget needs, labor hours, and trend variations. This also highlights gaps between customer forecasts and independent market projections.

Impact Created

• Enhanced operational visibility: Business leaders can see which programs, parts, or customers are underperforming, enabling corrective actions earlier (e.g. shifting capacity, reallocating labor, budget planning).

• Time savings: Automated pre-planning eliminated ~90% of manual effort, saving ~240 hours annually.

• Budget allocation: Budget needs are now projected months ahead with high confidence, supporting precise allocation of labor and processing costs.

• Multi-dimensional view into forecast data: By aligning internal and external data, leaders gained a more realistic view of demand shifts.

• Strategic confidence: The ability to compare customer forecasts with market intelligence from the accredited research partner strengthened leadership’s credibility in planning discussions with their parent company.

Final thoughts

With OEMs and tier 1 suppliers expecting high-precision outputs and real-time visibility, smarter forecast-to-fulfillment systems, reducing these costs while ensuring resources are used optimally. To be an agile, resilient and competitive, having a system that continuously compares forecast vs. actuals, learns from past performance, and simulates different scenarios is paramount.