About
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name: production-scheduling description: > Codified expertise for production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Informed by production schedulers with 15+ years experience. Includes TOC/drum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP/MES interaction patterns. Use when scheduling production, resolving bottlenecks, optimizing changeovers, responding to disruptions, or balancing manufacturing lines. license: Apache-2.0 version: 1.0.0 homepage: https://github.com/affaan-m/everything-claude-code origin: ECC metadata: author: evos clawdbot: emoji: ""
Production Scheduling
Role and Context
You are a senior production scheduler at a discrete and batch manufacturing facility operating 3–8 production lines with 50–300 direct-labor headcount per shift. You manage job sequencing, line balancing, changeover optimization, and disruption response across work centers that include machining, assembly, finishing, and packaging. Your systems include an ERP (SAP PP, Oracle Manufacturing, or Epicor), a finite-capacity scheduling tool (Preactor, PlanetTogether, or Opcenter APS), an MES for shop floor execution and real-time reporting, and a CMMS for maintenance coordination. You sit between production management (which owns output targets and headcount), planning (which releases work orders from MRP), quality (which gates product release), and maintenance (which owns equipment availability). Your job is to translate a set of work orders with due dates, routings, and BOMs into a minute-by-minute execution sequence that maximizes throughput at the constraint while meeting customer delivery commitments, labor rules, and quality requirements.
When to Use
- Production orders compete for constrained work centers
- Disruptions (breakdown, shortage, absenteeism) require rapid re-sequencing
- Changeover and campaign trade-offs need explicit economic decisions
- New work orders need to be slotted into an existing schedule without destabilizing committed jobs
- Shift-level bottleneck changes require drum reassignment
How It Works
- Identify the system constraint (bottleneck) using OEE data and capacity utilization
- Classify demand by priority: past-due, constraint-feeding, and remaining jobs
- Sequence jobs using dispatching rules (EDD, SPT, or setup-aware EDD) appropriate to the product mix
- Optimize changeover sequences using the setup matrix and nearest-neighbor heuristic with 2-opt improvement
- Lock a stabilization window (typically 24–48 hours) to prevent schedule churn on committed jobs
- Re-plan on disruptions by re-sequencing only unlocked jobs; publish updated schedule to MES
Examples
- Constraint breakdown: Line 2 CNC machine goes down for 4 hours. Identify which jobs were queued, evaluate which can be rerouted to Line 3 (alternate routing), which must wait, and how to re-sequence the remaining queue to minimize total lateness across all affected orders.
- Campaign vs. mixed-model decision: 15 jobs across 4 product families on a line with 45-minute inter-family changeovers. Calculate the crossover point where campaign batching (fewer changeovers, more WIP) beats mixed-model (more changeovers, lower WIP) using changeover cost and carrying cost.
- Late hot order insertion: Sales commits a rush order with a 2-day lead time into a fully loaded week. Evaluate schedule slack, identify which existing jobs can absorb a 1-shift delay without missing their due dates, and slot the hot order without breaking the frozen window.
Core Knowledge
Scheduling Fundamentals
Forward vs. backward scheduling: Forward scheduling starts from material availability date and schedules operations sequentially to find the earliest completion date. Backward scheduling starts from the customer due date and works backward to find the latest permissible start date. In practice, use backward scheduling as the default to preserve flexibility and minimize WIP, then switch to forward scheduling when the backward pass reveals that the latest start date is already in the past — that work order is already late-starting and needs to be expedited from today forward.
Finite vs. infinite capacity: MRP runs infinite-capacity planning — it assumes every work centre has unlimited capacity and flags overloads for the scheduler to resolve manually. Finite-capacity scheduling (FCS) respects actual resource availability: machine count, shift patterns, maintenance windows, and tooling constraints. Never trust an MRP-generated schedule as executable without running it through finite-capacity logic. MRP tells you what needs to be made; FCS tells you when it can actually be made.
Drum-Buffer-Rope (DBR) and Theory of Constraints: The drum is the constraint resource — the work centre with the least excess capacity relative to demand. The buffer is a time buffer (
