Manufacturing Case Study4
• Three previously disconnected data systems unified into a single batch-level quality-process analytical dataset — 18 months of historical data connected for the first time • Cross-facility benchmarking enabled the Ohio facility's best-practice process parameters to be adopted at all three sites — turning local operational knowledge into a documented, organisation-wide standard • Microbiological rejection rate reduced 82% after CIP cycle anomaly alert implementation — a process control improvement that had been hiding in the data for years, invisible without the analytical connection
WHAT COMES NEXT
DataVines is now building a predictive yield optimisation model for the manufacturer — using the process parameter combinations identified as most strongly correlated with quality outcomes to generate real-time process adjustment recommendations during production runs. The goal is to move from alert-based process monitoring (flagging when a parameter is outside its optimal window) to prescriptive process guidance (recommending the specific adjustments that will maintain the parameter profile within the optimal window throughout the entire run). CASE STUDY 04 · MANUFACTURING · STEEL & METALS FABRICATION · EXECUTIVE OPERATIONS INTELLIGENCE &
DATA PIPELINE AUTOMATION
The COO Got Her Operations Report on Friday. It Reflected Tuesday's Reality. A mid-sized steel fabrication and metals processing company running six fabrication lines across two facilities had an operations reporting process that took four days to produce and delivered data that was already half a week old. DataVines automated the entire operations data infrastructure — and gave the COO a live view of every line, every shift, every metric, by 6 AM every morning.
The Challenge
Every Monday, the operations analyst began building the weekly report. She would export production output data from the MES (Epicor Manufacturing). She would pull labour hours from the payroll system. She would pull scrap weights from the materials management module. She would request the delivery performance data from the logistics coordinator, who maintained it in a separate Excel file. She would pull machine utilisation records from the maintenance team's CMMS spreadsheet. By Wednesday afternoon, she had all five sources in Excel. She would spend Wednesday evening and Thursday consolidating them into the weekly operations dashboard — a 12-tab Excel workbook that had been the company's primary operations intelligence tool for seven years. The finished report went to the COO and the two plant managers on Friday afternoon. The production data in it reflected output through Tuesday of the same week. The COO's Friday review of last week's performance was already operating on data that was 72 to 96 hours old. The consequence showed up in a specific operational pattern that repeated itself every few months: a production efficiency problem would develop in one of the six lines — scrap rate creeping up, labour efficiency deteriorating, throughput falling below target — and the operations leadership would not see it in the weekly report until it had been running for a full week and a half. By then, the problem was established, the causal factors had evolved, and the recovery took twice as long as it would have if the team had seen it on day two. The reporting architecture that was keeping leadership blind: • Five separate data sources — MES, payroll, materials management, logistics spreadsheet, CMMS maintenance spreadsheet — with no automation between any of them • 4-day manual report production cycle consuming 28 hours of the operations analyst's week — leaving minimal capacity for actual analysis, investigation, or process improvement support • 72-to-96-hour data lag in the weekly operations report — by the time leadership reviewed Friday's report, the data inside it was already three to four days old • No intra-week visibility — if a line's scrap rate doubled on Tuesday, the first time anyone in operations leadership saw it was Friday afternoon • No cross-facility comparison — the Cleveland and Pittsburgh facilities were reported separately, with no unified view enabling leadership to benchmark performance across sites • Report dependent on one person — when the analyst was on leave, the COO received no operations data at all; this had happened three times in the prior year
The DataVine Solution
The Solution
- We came in with one design rule: the operations report should produce itself. Not faster with a person — by itself. Every data source needed an automated pipeline. Every KPI needed a live calculation. The analyst's job was to act on what the data showed — not to produce it. Building agreement before building anything • Ran a cross-functional KPI definition workshop with the COO, the two plant managers, and the production planning lead — establishing agreed definitions for all 14 core KPIs: throughput (units per shift per line), first-pass yield, scrap rate (by weight and by unit count), labour efficiency (actual vs standard hours), machine utilisation (runtime vs available time), schedule adherence,
- and on-time delivery — with agreed calculation methodologies documented formally so any analyst could reproduce any number • Established a single data hierarchy: raw source data → standardised line-level metrics → facility-level aggregations → executive summary — ensuring every KPI in the COO's dashboard could be traced to its source record in the MES or payroll system in under five minutes Automating the operations data connections • Built API integrations pulling production output and schedule adherence data from Epicor Manufacturing MES on a 2-hour refresh cycle; labour hours and headcount by shift and line from the payroll/HR system (ADP Workforce Now); scrap weight and materials consumption from the materials management module; and machine downtime and work order data from the CMMS (eMaint) — all flowing into a centralised AWS Redshift warehouse • Built a structured data ingestion pipeline for the logistics delivery performance tracker — converting the logistics coordinator's Excel file into a normalised database table, automatically parsing weekly uploads and flagging any delivery record missing a confirmed ship date or customer confirmation timestamp • Built Python ETL scripts with full error handling for every integration — any API failure triggers automatic retry, logs the specific error, and sends a Slack alert to the data operations lead before 5 AM; no failure ever propagates silently into the morning report • Orchestrated all five source pipelines through Apache Airflow with scheduled runs beginning at 3:30 AM — all source data loaded, dbt transformation models run, and Power BI dashboard refreshed before 6 AM every weekday morning Building the executive operations intelligence layer • Built a Power BI dashboard suite with five views: a COO executive operations summary (all six lines, both facilities, all 14 KPIs on one screen readable in under 60 seconds), a line-level performance deep-dive for each production line, a cross-facility benchmarking view comparing equivalent lines at Cleveland and Pittsburgh on each KPI, a shift performance tracker comparing current-shift metrics against the line's trailing 4-week average, and a weekly trends summary covering 13-week performance trajectories for all KPIs • Built a real-time exception alert layer — any production line where scrap rate exceeds its 4-week average by more than 15% for two consecutive shifts triggers an automatic Slack alert to the plant manager and the COO; any line where throughput falls more than 20% below the daily production target by 10 AM triggers an intra-day alert with the current throughput gap and the estimated end-of-shift shortfall • Built an automated Monday morning executive briefing landing in the COO's and both plant managers' inboxes at 6:30 AM — covering the prior week's performance summary across all six lines, the week's top three efficiency variances with the specific lines and shifts responsible, and the current on-time delivery position against all open customer commitments • Built an automated customer delivery risk tracker flagging any open order where the current production schedule placed the completion date within 48 hours of the committed delivery date — giving the operations team a daily view of at-risk deliveries before they became late ones
Operational Impact
The COO described the first Monday after go-live as the first time in seven years she had started her week with a current, complete picture of her operations. The Monday morning briefing was in her inbox at 6:30 AM. Every line, both facilities, every KPI — reflecting Friday's third shift and Saturday's production. She reviewed it on her phone before arriving at the office. The cross-facility benchmarking view surfaced something that had been invisible in the prior reporting structure: the Pittsburgh facility's precision machining Line 6 had a labour efficiency ratio of 94% against standard — 11 percentage points above the Cleveland equivalent Line 3, running the same product family on similar equipment. The difference was not equipment age or operator experience. The Pittsburgh plant manager had implemented a shift handover protocol that included a 10-minute structured tooling check — a practice that had never been documented or shared. Within three weeks of the benchmarking view going live, Cleveland Line 3 had adopted the same protocol. Its labour efficiency moved from 83% to 91% over the following four weeks. The real-time exception alert caught its first problem on day four of operation. At 10:47 AM on a Thursday, Line 2 at the Cleveland plant triggered a scrap rate alert — 31% above its 4-week average across the morning's first two shifts. The plant manager was in the facility within 20 minutes. The root cause: a batch of incoming steel bar stock had a surface inclusion defect that was causing inconsistent cut quality across the line. The batch was quarantined, the supplier notified, and a replacement delivery arranged. Total production impact: 4 hours of reduced output. In the prior system, that scrap rate spike would have appeared in Friday's weekly report — three and a half days after the problem had started, by which point the defective stock would have processed through multiple shifts.
Operations data lag reduced from 72–96 hours (Friday report reflecting Tuesday) to 6 hours (6
AM briefing reflecting the prior day's final shift)
28 hours of weekly analyst time reclaimed from manual report production — redeployed to
process improvement analysis, customer delivery planning, and cost reduction project support
100% of 14 core KPIs now automated, live, and refreshing on a 2-hour cycle — no manual
compilation, no weekend data gaps, no single point of failure
Real-time exception alerts catching production deviations within hours rather than days —
demonstrated impact on day four with a defective raw material batch caught before it processed
through more than two shifts
Cross-facility benchmarking enabled best-practice sharing from Pittsburgh to Cleveland — labour
efficiency improvement of 8 percentage points on Line 3 within four weeks of benchmarking
visibility
Zero reporting incidents in nine months post-launch — every pipeline failure caught and
auto-recovered before business hours, no data gaps reaching the executive team
WHAT COMES NEXT
DataVines is now building a capacity planning intelligence model for the fabricator — using historical throughput by line, product mix, and operator configuration to generate a forward-looking capacity position for each of the six lines on a rolling 8-week horizon. The goal is to give the sales team a real-time view of available production capacity when quoting delivery dates to customers, replacing the current practice of checking with the plant manager by phone and receiving a best-guess estimate based on memory and experience.
Seen enough? Let's build yours. DataVines is a boutique data analytics company that builds tailored dashboards, predictive intelligence systems, and automated data pipelines for manufacturers, plant operators, and industrial enterprises across automotive, chemicals, food & beverage, metals, and more. We don't do retainers without proving value first. Start with a free 5-day Proof of Concept. We build something real with your data. You decide if it's worth continuing. www.data-vines.com Mumbai, India · Serving industrial clients across North America, Europe & Asia-Pacific
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