Manufacturing Case Study2
• Cross-plant fleet health visibility established for the first time — Plant A and Plant B maintenance teams sharing one dashboard, one health score framework, one spare parts tracker • Spare parts procurement shifting from emergency reactive orders to planned preventive purchasing triggered by health score trends — premium expediting costs eliminated for the seven failure types the model can predict
WHAT COMES NEXT
DataVines is now building a production scheduling optimisation layer for the manufacturer — integrating machine health scores into the weekly production planning process so that high-risk machines are not scheduled for the highest-intensity production runs during their degradation window. The goal is to close the gap between the maintenance function and the production planning function: when a machine's health score enters the amber zone, the production schedule automatically considers routing its most demanding orders to a healthier machine in the same capability class. CASE STUDY 02 · MANUFACTURING · SPECIALTY CHEMICALS · SUPPLY CHAIN & PRODUCTION PLANNING
INTELLIGENCE
Production Planning Took Three Days Every Month. The Plan Was Outdated Before the Ink Dried. A specialty chemicals manufacturer running 14 reactor lines across three sites had a monthly production planning process that consumed an entire week of the planning team's time — and produced a plan that customer demand had already invalidated by the time it was distributed. DataVines automated the data infrastructure and built the planning intelligence layer that cut plan generation from days to hours.
The Challenge
Every month, the production planning cycle began on the first Monday. The supply chain planning manager would export the open order book from SAP. The plant scheduling teams would email their available capacity estimates — manually calculated from the prior month's reactor utilisation and the scheduled maintenance calendar. The procurement team would export the raw material availability position from a separate inventory system. And then the planning team would spend three days in Excel attempting to reconcile customer demand against production capacity, raw material constraints, and the 14 reactor lines' scheduling windows. The plan that emerged from that process was always a compromise between what the data showed and what the planners believed based on experience, because the data was never quite current enough to trust completely. SAP's open order book was accurate as of the prior evening's batch run. The capacity estimates from each plant were based on last week's actuals, not current-week real conditions. Raw material availability was correct as of the most recent inventory cycle count, which might have been three days ago. The plan was built on data from three different time horizons that had never been synchronised. The customer impact was measurable and growing. On-time-in-full delivery performance had averaged 71 percent over the prior four quarters — 29 percent of orders either late, short-shipped, or substituted with an alternative grade. Customer complaints about delivery reliability had increased in three consecutive quarterly reviews. The planning team knew the root cause: they were planning against an incomplete picture of their own capacity. They just had no mechanism to build a complete picture faster than three days. Where planning was being structurally defeated by its own data: • Three plants, 14 reactor lines, nine planning team members — and no real-time view of combined capacity versus committed demand at any point in the planning cycle • SAP order book, manual plant capacity estimates, and a separate inventory system all operating on different time horizons — the monthly plan built on three data snapshots that were never synchronised • Three-day manual plan generation cycle consuming the planning team's first week of every month — leaving no capacity for demand signal analysis, customer constraint management, or proactive exception handling • No scenario planning capability — when a reactor went offline unexpectedly or a key raw material supplier missed a delivery, replanning required starting the Excel process from scratch • Customer allocation decisions made during the plan build based on planner judgment rather than analytical customer prioritisation — no consistent framework for deciding which customers received priority when supply was constrained • OTIF tracking maintained in a separate spreadsheet by the logistics team — never connected to the production plan, making root cause analysis of late deliveries a multi-day manual exercise after each quarter
The DataVine Solution
The Solution
- Before we built a single pipeline, we spent two weeks mapping the planning process in detail — every data source, every manual step, every decision point, and every place where a judgment call was being made because the data was not current enough to make the decision automatically. That map became the design blueprint for everything we built. Automating the planning data foundation
- Built real-time API integrations pulling open order data (quantities, delivery dates, customer
- priority tier, product grade) from SAP ERP on a 4-hour refresh cycle; reactor capacity and actual
- utilisation data from each plant's DCS (Distributed Control System) historian; raw material
- on-hand and inbound shipment status from the warehouse management system; and scheduled
- maintenance events from the CMMS — all flowing into a centralised Snowflake warehouse
- Built dbt transformation models producing a clean, synchronised planning dataset updated every
- four hours — one agreed methodology for available capacity calculation across all 14 reactor
- lines, one definition of committed demand versus forecast demand, explicit handling rules for
- reactor grade changeover time and minimum run length constraints
- Orchestrated all data pipeline runs through Apache Airflow, with row-count validation and Slack
- alerts ensuring the planning team's data was never silently stale — if any source failed to refresh
- on schedule, an alert reached the data operations lead within 15 minutes
- Built a raw material availability model connecting supplier delivery schedules, in-transit shipment
- tracking, and current warehouse on-hand positions into a single forward-looking raw material
- availability timeline per product line — replacing the weekly inventory count snapshot with a
- continuously updated supply position
- Building the production planning intelligence layer
- Built a Power BI planning dashboard with five views: a supply-demand balance summary showing
- committed orders against available capacity by reactor line for the next 8 weeks, a raw material
- constraint tracker flagging any product line where raw material availability limited production
- within the planning horizon, a reactor scheduling board showing current and planned run
- sequences across all 14 lines, an OTIF performance tracker connected in real time to actual
- shipment data, and a customer priority allocation view for constrained supply scenarios
- Built a constraint-aware production planning model that automatically generated an initial feasible
- production schedule — matching open orders to available reactor capacity while respecting
- changeover time constraints, minimum run lengths, raw material availability, and customer priority
- tiers — producing a draft plan in under 20 minutes from the current data state
- Built a scenario modelling capability allowing the planning team to simulate the impact of supply
- disruptions (reactor downtime, raw material delays, sudden demand spikes) on OTIF performance
- before committing to customer commitments — replacing the reactive replanning exercise with a
- proactive simulation that could be run in real time during a customer call
- Built an OTIF root cause analysis model connecting each late or short shipment in the logistics
- data to the specific planning decision, production event, or supply constraint that caused it —
- enabling the planning team to distinguish systemic issues (capacity constraints, supplier
- reliability) from execution failures (scheduling errors, incorrect order prioritisation)
Operational Impact
The planning manager described the first month-end after go-live as the first time in four years she had not spent the first week of the month in a planning spreadsheet. The constraint-aware model generated the initial draft plan at 6 AM on the first Monday. She spent the morning reviewing exceptions and making judgement calls on the four customer allocation conflicts the model had flagged — not building the plan from scratch. The planning cycle compressed from three days to four hours: one hour to review the model's draft plan and exception flags, two hours of planner review and customer priority decisions on constrained lines,
one hour to finalise and distribute. The plan was in the plants' hands by noon on the first Monday of the month — two and a half days earlier than the prior process allowed. The OTIF improvement materialised faster than the team had expected. The root cause analysis model, applied retrospectively to the prior six months of late deliveries, showed that 41 percent of OTIF failures had a single identifiable upstream cause: raw material availability had constrained a reactor line that the plan had not flagged as constrained because the inventory data had been three days old when the plan was built. With the planning data refreshing every four hours, those constraints were visible before commitments were made, not after shipments were missed.
Monthly plan generation time reduced from 3 days to 4 hours — 78% reduction in planning cycle
time, with the plan distributed to plants two and a half days earlier each month
31% improvement in on-time-in-full delivery rate within 60 days of go-live — from 71% to 93%
OTIF across all three plants
Planning data synchronised across all three plants on a 4-hour refresh cycle — replacing three
time-lagged manual exports with a single continuously updated planning foundation
Scenario modelling capability enabling real-time supply disruption impact assessment —
replanning for a reactor outage moved from a 2-day manual exercise to a 20-minute simulation
OTIF root cause analysis automated — late delivery investigation time reduced from 2 days per
incident to a same-day dashboard query
Customer priority allocation framework embedded in the planning model — constrained supply
decisions made consistently against agreed criteria, not individual planner judgment
WHAT COMES NEXT
DataVines is now building a demand sensing model for the manufacturer — using customer order history, weather patterns, agricultural season indices, and commodity price trends to generate a 12-week rolling demand forecast at the customer and product grade level. The goal is to shift production planning from a monthly order-book-driven exercise to a forward-looking process that positions reactor capacity against probable demand 8 to 12 weeks before orders are placed — reducing the proportion of the plan consumed by reactive short-cycle order management. CASE STUDY 03 · MANUFACTURING · FOOD & BEVERAGE · QUALITY CONTROL & PRODUCTION YIELD ANALYTICS 9% of Their Output Was Being Rejected at Final QC. Nobody Could Explain Why. A large-scale food and beverage manufacturer was experiencing a 9% end-of-line rejection rate across its three processing facilities — above the industry benchmark by a factor of three. DataVines connected process sensor data, quality lab results, and production run parameters for the first time, and built the analytics layer that identified the specific upstream process variables driving downstream quality failure.
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one hour to finalise and distribute. The plan was in the plants' hands by noon on the first Monday of the month — two and a half days earlier than the prior process allowed. The OTIF improvement materialised faster than the team had expected. The root cause analysis model, applied retrospectively...