Every plant is measured on output, yield and on-time delivery. Almost none can show the path an order actually took to get there — which site handled it, how many times it went back, and where the days went. RE-ViVE reconstructs that path from records your ERP, MES and quality systems already write.
Figures from a manufacturing order to cash engagement. Read the case study.
1Why
Manufacturing has spent thirty years wiring up the shop floor. Sensors, OEE, SPC, downtime codes — the equipment is one of the most heavily observed things in the enterprise. The work that moves an order through the plant is not.
The order starts in ERP, is scheduled in MES, is inspected in the quality system, is picked in the warehouse and is shipped by the carrier. Each keeps an honest record of its own leg. None keeps the record of the journey.
A single cycle-time number is the average of plants that behave nothing like each other. Improvement programs then get aimed at the average, which no site is running, instead of at the two that are dragging it.
A re-issued schedule, a second inspection, a corrected delivery note and a credit-and-rebill all look like activity in the system that recorded them. Only end to end does it become visible as the same order being handled twice.
Order to cash cycle time, days
8.4 days is the number the business reported. It describes none of these six plants, and it is the number every improvement target was set against.
Shape of a real finding. Site-level values are illustrative; the average, the two-to-sixteen-day range and the five-fold spread are from the engagement.
2Where
Two families of process. The first runs the plant. The second runs through it, and is usually where the money and the customer promise sit.
Where capacity, quality and asset availability are won or lost.
Where cycle time, working capital and customer commitments are decided.
3How
No new instrumentation, no data warehouse program and no curation layer to build first. Four steps, from access to a live view.
Status histories, workflow logs and audit trails in the systems you already run. If a record carries a case identifier, an activity and a timestamp, it is enough.
Records from separate systems are linked back into one case — a single order followed across everything that touched it, in the order it happened.
The process as it was intended, set against every path it really ran. Variants, rework loops, waiting time and team differences are counted rather than estimated.
The view refreshes as the data does, so drift shows up as it happens instead of surfacing in the next review cycle.
4What
Not a score or a maturity rating. The actual behavior of the process, in units your plant managers and finance team already argue about.
The same order type, plant by plant, so improvement effort goes where the spread actually is rather than to the average.
Orders that went round twice — re-scheduled, re-inspected, corrected or credited — separated from orders that went round once.
How much of the cycle was someone doing something, and how much was the order sitting in a queue between two systems.
Where the order crossed a boundary — planning to production, production to warehouse, warehouse to billing — and lost time doing it.
How many distinct paths one designed process actually runs, and which of them carry the delay and the cost.
Which orders are drifting towards an SLA or delivery breach while there is still time to intervene.
Tell us the process that costs you the most to get wrong. We will tell you, before any commitment, whether the data you already hold can reconstruct it — and what you would see if it can.
