Site Project Catalog — Cleveland Works¶
Purpose: Groups Cleveland's 27 initiatives into actionable projects for the plant manager. Each site project bundles related initiatives, sizes the local opportunity, and references the corporate project it feeds into.
Audience: John Stubna (Plant Manager), Cleveland site leadership, internal team
Last updated: 2026-04-16 (consistency pass: corporate project mappings verified against ch5/ch8)
Project Summary¶
| ID | Project | Horizon | Corporate | Bundled Initiatives | Value ($/yr) | Champion | Status |
|---|---|---|---|---|---|---|---|
| CLV-P01 | Ops-Maintenance Data Integration | H1 | PRJ-01 | CLV-01 | $2-5M | Jamie Betts | validated |
| CLV-P02 | Predictive Maintenance Platform | H1→H2 | PRJ-03 | CLV-12, CLV-22, CLV-23, CLV-09 | $3-12M | Paul Aaron Dash | validated |
| CLV-P03 | Maintenance Workflow & Copilot | H1 | PRJ-06 | CLV-07, CLV-08, CLV-24, CLV-25 | $3-11M | Dan Hartman | validated |
| CLV-P04 | Process Risk & Cobble Prediction | H2 | PRJ-05 | CLV-04, CLV-11, CLV-17, CLV-18, CLV-20 | $6-23M | Dan Hartman | validated |
| CLV-P05 | Quality & Yield Traceability | H2→H3 | PRJ-04 | CLV-06, CLV-14, CLV-19 | $2-5M + enabler | Stephen Palmer | partial |
| CLV-P06 | Production Scheduling & S&IOP | H3 | PRJ-02 | CLV-02, CLV-03, CLV-15, CLV-26 | $20-65M | Andrew Mullen | validated |
| CLV-P07 | Intra-Plant Logistics | H2 | PRJ-07 | CLV-10, CLV-16 | TBD | Shipping team | identified |
| CLV-P08 | Caster Chemistry Optimization | H2 | PRJ-08 | CLV-05 | $2-8M | TBD | identified |
| CLV-P09 | Utility Management | H2 | new | CLV-21 | $7-15M | Paul Aaron Dash | validated |
| CLV-P10 | Environmental Compliance | H1 | new | CLV-13 | $0.5-1M | Andrew Mullen | identified |
| CLV-P11 | Enterprise Data Platform Strategy | H2→H3 | enabler | CLV-27 | Enabler | Andrew Mullen | identified |
Project Cards¶
CLV-P01: Ops-Maintenance Data Integration¶
| Field | Value |
|---|---|
| Horizon | H1: Bridge the Gap |
| Corporate project | PRJ-01 — Ops-Maintenance Data Integration |
| Status | validated |
| Champion(s) | Jamie Betts (1SP Maint), Dan Hartman (HSM), Paul Aaron Dash |
Local problem statement:
"Our operational side, it's all captured. The maintenance side, it's all captured. But we need to be able to integrate them." Operations tracks delays in SQL reports by area. Maintenance tracks work orders in Tabware by asset hierarchy. A 14-minute delay logged by operations has no matching work order. Different naming conventions, different systems, same events — no one can have a data-driven conversation about root causes.
Bundled initiatives: - CLV-01: Ops-Maintenance data integration — semantic matching layer linking SQL delay reports to Tabware work orders
Systems involved: Tabware (CMMS, ~95% hierarchy), operational SQL delay reports (custom-built, web dashboards), Axiom (ERP)
Value estimate: $2-5M/yr (reduced misattribution, faster root cause, targeted maintenance spend) Confidence: Medium-High — validated by 7+ stakeholders across 5 transcripts Implementation approach: Map operational delay categories to Tabware asset hierarchy (2-4 weeks) → build semantic matching logic (4-6 weeks) → unified dashboard (2-4 weeks). Both datasets exist and are ~95% structured. Cleveland is the natural entry site — strongest signal.
Dependencies: None — this is the foundation everything else builds on. Feeds CLV-P02 (PdM needs attribution data) and CLV-P04 (cobble prediction needs ops-maint linkage).
Palmer readout alignment: - Scalability: 4 sites (every integrated plant has this gap) / Quick-ROI: yes / Palmer named: no (but meets all criteria)
CLV-P02: Predictive Maintenance Platform¶
| Field | Value |
|---|---|
| Horizon | H1→H2 |
| Corporate project | PRJ-03 — Predictive Maintenance Platform |
| Status | validated |
| Champion(s) | Paul Aaron Dash, Phil Thorman, Brian Thompson, Jamie Betts, John Messi |
Local problem statement:
Vibration and condition monitoring is fragmented. AssetWatch at powerhouses only. Cloud vibration sensors on 1SP cranes that plant IT didn't even know existed. BOF bag house runs 4 ID fans — "it works until it doesn't, and then we all freak out." Scrubbing system degrades predictably over a 3-week cycle and "generates a shitload of data" that gets "a bit load of attention." Multiple high-value assets generate data nobody watches.
Bundled initiatives: - CLV-12: PdM platform (multi-asset) — consolidate fragmented condition monitoring, ML anomaly detection - CLV-22: BOF bag house predictive monitoring — PRIMARY PdM PoV target. 4 ID fans, rich sensor data, environmental compliance - CLV-23: BOF scrubbing system predictive monitoring — SECONDARY target. Predictable 3-week degradation curve - CLV-09: Spare parts inventory intelligence — predictive spares management feeds into PdM planning
Systems involved: Pi historian (10yr), AssetWatch (powerhouses), Viz (route vibration), cloud vibration (1SP cranes, Starnet?), Wonderware (powerhouses), Emerson (iron producing)
Value estimate: $3-12M/yr (PdM PoV through full expansion) Confidence: Medium — proven at powerhouses, scaling question. Bag house + scrubbing have richest data = highest PoV confidence. Implementation approach: PdM PoV on bag house (primary) + scrubbing (secondary) + Crane 300 (tertiary) — 8 weeks. Then expand to charging/teeming cranes, HSM work rolls, line shaft tables. Separate SOW governs the PoV phase.
Dependencies: CLV-P01 (ops-maint integration provides attribution data for maintenance events). PdM PoV is a separate project/SOW — implementation vehicle for first assets.
Palmer readout alignment: - Scalability: 4 sites (every site has critical assets) / Quick-ROI: yes (PoV in 8 weeks) / Palmer named: no directly, but PdM is core to value story
CLV-P03: Maintenance Workflow & Copilot¶
| Field | Value |
|---|---|
| Horizon | H1: Bridge the Gap |
| Corporate project | PRJ-06 — Maintenance Workflow Digitization |
| Status | validated |
| Champion(s) | Dan Hartman (HSM), Paul Aaron Dash, John Stubna |
Local problem statement:
Green technicians go to repairs without knowing the history. Paper PM sheets come back crumpled and thrown away. "Where's my Ask Jeeves?" Everything above $500 goes to the plant manager — a $300 power supply takes 2 months to order. Employees buying inventory apps with their own money. Purchasing silently cancels requisitions after 60 days. Caster segment tracking lives in one contractor's Excel spreadsheet.
Bundled initiatives: - CLV-07: Maintenance co-pilot — voice-first mobile assistant for technicians (talk → structured WO) - CLV-08: Procurement decision tree / auto-approval — risk-stratified approval to cut weeks→hours - CLV-24: Caster segment lifecycle tracking — digitize Excel→system, link to campaign tonnage + procurement - CLV-25: Critical spares identification — accelerate Brian Thompson's 500-part effort with AI-assisted item master population
Systems involved: Tabware (CMMS), Axiom (ERP/procurement), custom HVAC tablet app, caster segment Excel
Value estimate: $3-11M/yr (copilot $0.5-2M + procurement $1-3M + segments $1-3M + spares $1-3M) Confidence: Medium-High — deepest visceral pain across every conversation Implementation approach: Copilot MVP on 1 asset class (3-4 months) → procurement rules-based fast-track (parallel) → segment digitization (builds on existing Brian Thompson effort) → critical spares AI-accelerated identification. Must solve Wi-Fi dead zones for copilot.
Dependencies: None significant for copilot. Procurement reform requires corporate buy-in (policy change, not just tech). CLV-25 feeds CLV-09 (spare parts intelligence).
Palmer readout alignment: - Scalability: 4 sites (same corporate procurement policies everywhere) / Quick-ROI: yes / Palmer named: knowledge capture theme aligns
CLV-P04: Process Risk & Cobble Prediction¶
| Field | Value |
|---|---|
| Horizon | H2: Build the Foundation |
| Corporate project | PRJ-05 — Cobble & Process Risk Prediction |
| Status | validated |
| Champion(s) | Dan Hartman (HSM), John Stubna, Chad Helms (Iron Producing) |
Local problem statement:
Cobbles destroy drive spindles "multiple times," cause major downtime, and are increasing as experienced operators retire. "Some people just know the right call to make" — but those people are leaving. New operators override safety checks and cause cobbles. Context-dependent alarming is too simplistic — vibration depends on product, grade, speed. BF thermal management is single-expert dependent.
Bundled initiatives: - CLV-04: Cobble prediction & prevention — ML model per slab (temperature, chemistry, mill setup, operator) - CLV-11: Operator decision support (BF/HSM) — AI-assisted guidance, prevent dangerous overrides, context-aware alarms - CLV-17: BOF endpoint prediction — reduce reblows through ML (seed) - CLV-18: Caster breakout prediction — augment breakout detection with ML (seed) - CLV-20: BF thermal state prediction — predict Si/temperature 30-60 min ahead (seed)
Systems involved: L2 (HSM, BF, BOF, caster), Pi historian, operational delay system, Global Gauges (vision — 2 HSM stands), Emerson (BF C5/C6)
Value estimate: $6-23M/yr (cobble $3-10M + operator support $1-5M + BOF $0.5-2M + breakout $1-3M + BF thermal $1-3M) Confidence: Medium — cobble prediction and operator support validated, seed items need further scoping Implementation approach: Cobble prediction model first (6-9 months, needs L2 integration) → operator decision support → BOF/BF/caster models as data matures. Context-aware alarming is an early ML win within the operator support track.
Dependencies: CLV-P01 (attribution data for cobble root cause). L2 data access at all stages. Pi historian depth.
Palmer readout alignment: - Scalability: 3 sites with HSM, 4 with BF / Quick-ROI: cobble prediction can be bounded / Palmer named: BF stove optimization (related to CLV-20/CLV-11 pattern)
CLV-P05: Quality & Yield Traceability¶
| Field | Value |
|---|---|
| Horizon | H2→H3 |
| Corporate project | PRJ-04 — Through-Process Quality & Yield |
| Status | partial |
| Champion(s) | Stephen Palmer (quality/vision AI interest), TBD (quality/metallurgy) |
Local problem statement:
No end-to-end genealogy linking heat→slab→coil→finished product. Customer claims traced manually over days/weeks. 90-day slabs get remelted — a symptom of planning-demand misalignment. SIS exists but accuracy unknown. Slab cut optimization already saving $3M/month (Phil Thorman), so the biggest yield win is captured — remaining opportunity is in traceability and through-process quality.
Bundled initiatives: - CLV-06: 90-day slab remelting reduction — analytics on slab aging, reallocation recommendations - CLV-14: Through-process traceability (heat→coil) — automated genealogy linking L2 across process steps - CLV-19: Surface defect detection (CNN on SIS) — reduce SIS false positives (seed)
Systems involved: Janus (slab WMS), Axiom (ERP/demand), L2 across all stages, SIS cameras
Value estimate: $2-5M/yr direct (slab remelting + defect detection) + enabler value (traceability unlocks $13-27M in yield improvement) Confidence: Medium — slab cut already captured the biggest win. Traceability is foundational but hard to size. Implementation approach: Slab aging dashboard (2-3 months, quick win) → automated genealogy (6-12 months, strategic) → SIS defect detection CNN (6-9 months). Cleveland is NOT the primary site for through-process quality — Middletown's finishing chain makes it the better candidate.
Dependencies: CLV-P06 (scheduling) for systemic slab aging fix. Data architecture for cross-stage L2 linkage.
Palmer readout alignment: - Scalability: 4 sites / Quick-ROI: slab aging dashboard is bounded / Palmer named: surface inspection is his explicit priority (but Middletown is the stronger site for this)
CLV-P06: Production Scheduling & S&IOP¶
| Field | Value |
|---|---|
| Horizon | H3: Predict & Optimize |
| Corporate project | PRJ-02 — Production Scheduling & S&IOP |
| Status | validated |
| Champion(s) | Andrew Mullen, Terry Fedor, John Stubna |
Local problem statement:
"1% improvement = tens of millions." 5-6K orders/week with no optimization logic. No formal S&IOP — commercial, operations, and capacity are in separate silos. Roll changes account for ~50% of HSM operational delays. No demand forecasting (firm orders only). Dynamic pricing doesn't factor in capacity consumption — "we'll take heavy line pipe orders which destroy our equipment." In September (4th crew), the constraint shifts from 1SP to iron producing or HSM — no dynamic constraint identification.
Bundled initiatives: - CLV-02: Cross-stage scheduling / S&IOP — integrated business planning + AI scheduling optimization - CLV-03: Roll change sequencing optimization — minimize HSM changeovers - CLV-15: Dynamic pricing by capacity consumption — profitability scoring per order - CLV-26: Raw materials cost optimization — burden/alloy mix optimization + supply chain risk
Systems involved: Axiom (ERP), L2 (process control), Tabware (maintenance windows), scheduling tools (unknown — likely Excel)
Value estimate: $20-65M/yr (scheduling $10-30M + roll changes $5-15M + pricing $5-20M + raw materials TBD) Confidence: Low-Medium — massive scope, validated as real pain but needs decomposition Implementation approach: Phase 1: cross-stage visibility dashboard (3-4 months) → Phase 2: roll change sequencing (6-9 months) → Phase 3: full scheduling optimizer (12-18 months) → Phase 4: S&IOP process + AI planning. Highest value, longest horizon.
Dependencies: CLV-P01 (ops-maint integration), CLV-P05 (traceability), enterprise data architecture.
Palmer readout alignment: - Scalability: 4 sites / Quick-ROI: NO — this is H3, long horizon / Palmer named: no. This is the strategic bet, not the entry point.
CLV-P07: Intra-Plant Logistics¶
| Field | Value |
|---|---|
| Horizon | H2: Build the Foundation |
| Corporate project | PRJ-07 — Intra-Plant Logistics Optimization |
| Status | identified |
| Champion(s) | Facilities division manager, shipping team |
Local problem statement:
Coils touched 3-4 times before shipping (target: 1-2). Rail/truck mode switching adds unnecessary moves. 600+ coil increase in shipping volume recently. Slab yard is LIFO instead of FIFO. 33 bottle cars are a hard constraint across the river.
Bundled initiatives: - CLV-10: Intra-plant coil movement optimization — warehouse-style placement, reduce crane touches - CLV-16: Rail car inventory visibility — real-time tracking with class 1 railroads (NS, CSX)
Systems involved: Custom SQL coil tracking, shipping systems, external railroad data (NS, CSX — PDFs/Excel today)
Value estimate: TBD — need movement data to calculate crane hours saved + throughput impact Confidence: Low — need data Implementation approach: Analyze coil movement data → storage allocation optimization → rail car visibility integration. Rail car component depends on external cooperation (railroads).
Dependencies: None internally. Rail car visibility requires railroad API/data feed access.
Palmer readout alignment: - Scalability: 4 sites / Quick-ROI: analysis phase is fast / Palmer named: YES — coil logistics is his #1 cross-site priority
CLV-P08: Caster Chemistry Optimization¶
| Field | Value |
|---|---|
| Horizon | H2: Build the Foundation |
| Corporate project | PRJ-08 — Caster Chemistry Optimization |
| Status | identified |
| Champion(s) | TBD (caster/metallurgy team) |
Local problem statement:
"We've got a Prime Metals model. We don't even use. We spent millions on it." Chemistry transitions in continuous casting generate scrap when the cut point between chemistries is wrong. The model they bought doesn't get used — need to understand why (adoption? accuracy? integration?).
Bundled initiatives: - CLV-05: Caster chemistry transition optimization — predict optimal cut points, sequence chemistries, recover in-between grades
Systems involved: L2 (caster), Prime Metals model (status unknown), Axiom (grades/orders)
Value estimate: $2-8M/yr Confidence: Medium Implementation approach: Phase 1: understand Prime Metals failure (adoption vs. accuracy vs. integration). Phase 2: build chemistry transition optimizer leveraging existing infrastructure if possible. Company-wide problem.
Dependencies: CLV-P05 (traceability — need heat→slab chemistry lineage).
Palmer readout alignment: - Scalability: 3 sites with casters / Quick-ROI: depends on Prime Metals investigation / Palmer named: no
CLV-P09: Utility Management¶
| Field | Value |
|---|---|
| Horizon | H2: Build the Foundation |
| Corporate project | new (site-specific, may validate at other sites) |
| Status | validated |
| Champion(s) | Paul Aaron Dash (20-year champion), Dan Hartman |
Local problem statement:
No utility management system at Cleveland. Cannot track water, gas, O2, N2, or compressed air by area. Compressed air alone = $7M/yr savings opportunity identified in engineering study. Nitrogen pressure drops affect 1SP operations with no diagnostic capability. Paul Aaron Dash has worked on this for 20+ years with budget constraints — he is the last utility engineer. Descale water system: 5 pumps, 12 headers, 18 variables manually charted weekly.
Bundled initiatives: - CLV-21: Utility management system — AI on metering infrastructure for energy/utility optimization
Systems involved: Wonderware (powerhouses, water treatment), Emerson (new water meters being installed), manual charting (descale system)
Value estimate: $7-15M/yr ($7M compressed air + water + gas + nitrogen) Confidence: Medium — compressed air backed by engineering study, others need scoping Implementation approach: Build AI layer on metering infrastructure as water meters (Emerson) come online. Anomaly detection for leaks and pressure drops. Automated reporting replacing manual charting. 6-12 months.
Dependencies: Metering infrastructure completion (water meters in progress).
Palmer readout alignment: - Scalability: TBD — need to validate at other sites / Quick-ROI: compressed air already scoped / Palmer named: no
CLV-P10: Environmental Compliance¶
| Field | Value |
|---|---|
| Horizon | H1: Bridge the Gap |
| Corporate project | new (site-specific) |
| Status | identified |
| Champion(s) | Andrew Mullen |
Local problem statement:
Environmental compliance (SDS tracking, refrigerant reporting) is manual and paper-based. Tied to employee bonuses. Exceedances at Burns Harbor already this year.
Bundled initiatives: - CLV-13: Environmental compliance automation — automated SDS tracking, purchase-to-storage-location linkage
Systems involved: Manual pencil-and-paper tracking
Value estimate: $0.5-1M/yr Confidence: Low — needs scoping Implementation approach: Rules-based automation. Quick Win.
Dependencies: None.
Palmer readout alignment: - Scalability: 4 sites / Quick-ROI: yes / Palmer named: no
CLV-P11: Enterprise Data Platform Strategy¶
| Field | Value |
|---|---|
| Horizon | H2→H3 |
| Corporate project | enabler (not a standalone project) |
| Status | identified |
| Champion(s) | Andrew Mullen |
Note: This is NOT a standalone project — it is the emergent outcome of doing the work. Every H1 project connects data sources; every H2 project adds new streams. By H3, the foundation exists. Databricks, Snowflake, and Microsoft Fabric are all under evaluation. CEO wants enterprise-wide data platform. Our recommendation: build bottom-up through project delivery, not top-down through a data lake initiative.
Bundled initiatives: - CLV-27: Enterprise data platform (cloud strategy)
Value estimate: Enabler (unlocks $50-100M+ across all projects) Confidence: Low — early exploratory Implementation approach: Progressive data foundation through project delivery. Each project ingests new sources. Platform decision (Databricks/Snowflake/Fabric) can happen in parallel without blocking project work.
Corporate Project Cross-Reference¶
| Corporate Project | Site Projects | Validation Strength |
|---|---|---|
| PRJ-01: Ops-Maint Integration | CLV-P01 | Strong — strongest signal, every stakeholder |
| PRJ-02: Production Scheduling | CLV-P06 | Strong — massive value, validated but H3 |
| PRJ-03: PdM Platform | CLV-P02 | Strong — multi-asset, PoV targets selected |
| PRJ-04: Quality & Yield | CLV-P05 | Partial — slab cut already captured biggest win |
| PRJ-05: Cobble & Process Risk | CLV-P04 | Strong — cobbles + operator support + BF |
| PRJ-06: Maint Workflow | CLV-P03 | Strong — deepest visceral pain |
| PRJ-07: Logistics | CLV-P07 | Partial — need movement data |
| PRJ-08: Caster Chemistry | CLV-P08 | Partial — Prime Metals investigation needed |
Site-Specific Notes¶
- 1SP is THE constraint: 28 heats/day target, all casters booked through May. Every missed production unit = lost revenue.
- 70/30 reactive/planned maintenance. Zero close-the-loop. 2001 ISG restructuring merged planner + reliability engineer + area manager into one role — structural root cause for broken information loops.
- Union: USW. Generally cooperative but change management matters.
- Systems landscape: Tabware (CMMS), Pi (historian, 10yr steel producing), Wonderware (powerhouses), Emerson (iron producing), Axiom (ERP), Janus (slab WMS), L2 (process control), Viz (route vibration), cloud vibration on cranes.
- Addressable value: $26-69M/yr across all projects at Cleveland alone.
- PdM PoV targets (revised Day 5): BOF bag house (primary), scrubbing system (secondary), Crane 300 (tertiary).
- Key relationship: John Stubna (plant manager) has "full support of corporate" and young leadership team open to change.