9.3 Operations strategy

Operations strategy chooses long-term process, capacity, location, technology, supply/inventory, quality and workforce systems to deliver cost, quality, speed, dependability and flexibility objectives. Evaluate the whole system and trade-offs, not one efficiency metric.

Define customer/strategic performance priorities → quantify demand/resources/current capability → generate feasible process/capacity/technology/supply alternatives → compare total cost, quality, speed, dependability, flexibility, cash and risk → pilot/sequence and assign cross-functional owners → monitor outcomes/assumptions and adapt.

Set business objective/constraint, validate data/model against real outcomes, pilot with fallback, train/involve affected employees, protect cyber/privacy, retain human escalation for safety/quality/ethics, monitor drift and total performance. Automation may lower variable labour or defects but create fixed cost, inflexibility and redeployment/training needs.

Need increases with volatile/seasonal demand, diverse/fast-changing customer needs, short life cycles, competitor innovation, uncertain supply and service recovery. Standard stable high-volume markets may value efficiency/consistency more; choose the dimension that supports the market promise rather than pursuing flexibility as an end.

Define customer/performance problem and baseline → map process/waste/constraint → generate technology and non-technology alternatives → test technical, workforce, quality, finance, cyber/ethical and capacity effects → pilot/prototype → train/redesign roles/suppliers/data → stage implementation with fallback → measure cost, quality, speed, dependability, flexibility and employee/customer response → scale/adapt.

Main features: one master-data source/common definitions; modular integrated processes; automated transaction/workflow/approval; real-time visibility and traceability; role-based access/audit trail; planning/forecasting/reporting dashboards; alerts and links to suppliers/customers/other systems; configurable standard processes.

A customer order can reserve inventory, trigger purchasing/production, update capacity/delivery promise, create finance entries and signal staffing need without re-entering data. This reduces errors/delay only if product, supplier, lead-time, cost and skill master data are accurate.

Map/redesign processes and owners → clean/govern master data → choose/configure modules/integration/access → migrate/test including exception/cyber/recovery → train and manage role change → pilot/phased rollout with fallback → measure adoption, data quality, lead time, stock, utilisation, errors and decision outcomes → improve.

High licence/customisation/integration/training and transition cost; disruption/data migration error; employee resistance/skill and process-fit gaps; cybersecurity/privacy/vendor dependence; overstandardisation; inaccurate common data spreading faster; implementation delay/overrun and single-system outage. Benefits depend on scale/complexity and management capability.

Define customer value/performance → map value stream and baseline waste/constraint → stabilise quality/equipment/suppliers/data → involve/train employees → pilot suitable methods → create pull/flow and visible problem escalation → measure lead time, inventory, defects, productivity, delivery and safety → improve continuously and retain risk-based resilience.

Lean may support cost, quality and delivery objectives, but is not sufficient if product/market strategy, obsolete capacity, finance, supplier reliability, culture/skills or demand forecast remain weak. Judge implementation cost/time and resilience, especially under volatile demand or fragile supply.

Operations planning translates objectives into activities, dependency order, time, people/equipment/material/cash needs, ownership, milestones and controls. It coordinates scarce resources, exposes bottlenecks/risks and enables response; planning quality depends on estimates and updates.

Forward pass for earliest event times: set start EET = 0. For each arrow, candidate arrival = start-node EET + activity duration. At a merge node use the maximum incoming arrival because every predecessor must finish. Final-node EET is the minimum project duration.

Backward pass for latest event times: set final LET equal to final EET. Moving backward, candidate latest start event = end-node LET − activity duration. At a burst/start node use the minimum outgoing candidate so no successor is delayed.

Total float is how long an activity can be delayed without delaying minimum project completion. Free float is delay possible without delaying the earliest start of any immediate following activity. Activities on a critical path have zero total float; the critical path is a start-finish chain whose durations sum to minimum project duration. There may be multiple critical paths.

For activity C from node i with EET 7 to node j with EET 15 and LET 18, duration 5: total float = 18 − 7 − 5 = 6; free float = 15 − 7 − 5 = 3. Up to 3 time units does not affect any immediate successor's earliest start; more than 3 but no more than 6 uses downstream flexibility but not project completion float.

Use CPA to identify minimum completion/date feasibility, prioritise monitoring and scarce resources on critical/near-critical tasks, reschedule non-critical work within float, compare acceleration ('crashing') cost with benefit, coordinate suppliers/cash/people and run what-if updates. Protect near-critical paths because delays can consume float and create a new critical path.