A local factory approached Marketways because demand changes, production bottlenecks, quality losses and supplier risk were being managed separately. Early discovery showed that the underlying problem was not a single forecast or process delay. A faster machine could simply build inventory before the real constraint, while a cheaper supplier could increase rework and disrupt the production plan. We connected commercial demand to feasible production and tested how changes travelled through the plant before recommending them.
The engagement objective
Through the initial discovery, Marketways defined the objective: connect demand, S&OP, bottlenecks, quality and supplier risk.
How Marketways translated the problem
We began with a practical question: How should production and inventory respond to uncertain demand? The first analysis used orders, forecast, inventory, routings, capacity, suppliers and service.
That evidence could not be read in isolation. A consensus forecast can still be infeasible when mix and shared-resource constraints are ignored. The apparent bottleneck changes with mix, downtime, changeover and quality loss.
Higher local throughput could increase inventory before the true constraint and make the plant look efficient while weakening flow. We tested changes against the complete demand-to-production system.
We did not judge each component by its isolated KPI. We examined how people, assets, decisions and constraints affected one another, then used the evidence to test whether an apparent improvement would strengthen the complete system or merely move cost, pressure or risk elsewhere.
How the engagement developed
The initial work on sales and operations planning exposed dependencies with yield, throughput and bottleneck diagnostic, quality variation and root-cause analysis, supplier and material-risk intelligence. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Sales and operations planning: Align commercial plans with feasible capacity and working capital.
- Yield, throughput and bottleneck diagnostic: Improve throughput without shifting defects, queues or cost downstream.
- Quality variation and root-cause analysis: Control important variation earlier in the process.
- Supplier and material-risk intelligence: Prioritise qualification, development and contingency action.
Evidence we examined
- Orders, forecast, inventory, routings, capacity, suppliers and service.
- Cycle time, downtime, scrap, WIP, routing, shifts and product mix.
- Measurements, batches, materials, equipment, operators, environment and defects.
- Supplier, material, inspection, delivery, claims, inventory and dependency.
Industry conditions we accounted for
- A consensus forecast can still be infeasible when mix and shared-resource constraints are ignored.
- The apparent bottleneck changes with mix, downtime, changeover and quality loss.
- Inspection finds defects; it does not automatically identify when or why they were created.
- Low unit price can conceal variability, long recovery or single-source exposure.
How our engagement contributed to business impact
We connected every method to a decision and a business measure. The organisation could assess the engagement through operating results as well as model performance.
- Sales and operations planning
- Method: Forecasting, Risk & Optimisation, Process, Workflow & Systems.
- Evidence: Orders, forecast, inventory, routings, capacity, suppliers and service.
- Decision supported: Align commercial plans with feasible capacity and working capital.
- Impact measure: Service, schedule stability, inventory and plan adherence.
- Yield, throughput and bottleneck diagnostic
- Method: Statistics & Econometrics, Process, Workflow & Systems.
- Evidence: Cycle time, downtime, scrap, WIP, routing, shifts and product mix.
- Decision supported: Improve throughput without shifting defects, queues or cost downstream.
- Impact measure: Good output, flow time, WIP and constraint utilisation.
- Quality variation and root-cause analysis
- Method: Statistics & Econometrics, Data Foundations & Business Intelligence.
- Evidence: Measurements, batches, materials, equipment, operators, environment and defects.
- Decision supported: Control important variation earlier in the process.
- Impact measure: First-pass yield, defect escape, variation and verified cause.
- Supplier and material-risk intelligence
- Method: Statistics & Econometrics, Forecasting, Risk & Optimisation.
- Evidence: Supplier, material, inspection, delivery, claims, inventory and dependency.
- Decision supported: Prioritise qualification, development and contingency action.
- Impact measure: Disruption, incoming quality, total cost and recovery readiness.
Implementation
The engagement was structured as diagnostic and improvement waves. We connected the analysis to the decisions, operating constraints and measures that the organisation would continue to use.
How success was assessed
The overall assessment considered service, good output, stability and working capital. The supporting measures included:
- Service, schedule stability, inventory and plan adherence.
- Good output, flow time, WIP and constraint utilisation.
- First-pass yield, defect escape, variation and verified cause.
- Disruption, incoming quality, total cost and recovery readiness.
Services and methods used
Services: Sales Forecasting & Demand Planning, Business Systems Design & Architecture, Operational Performance Diagnostic, Process & Workflow Analysis & Redesign, Decision Assurance, Risk Detection.
Methods: Forecasting, Risk & Optimisation, Process, Workflow & Systems, Statistics & Econometrics, Data Foundations & Business Intelligence.