On-time delivery:
See the delay while it is still avoidable
Which order will be late? Classification and survival models detect the looming delay early - while there is still time to prevent it, instead of just booking the penalty.
This module is part of a six-part model calculation about a fictional mid-sized manufacturing company (48 machines, 3-shift operation, €85M revenue). All euro amounts are derived from the documented assumptions of the sample plant. They are not results of real clients and not a performance promise. What values are achievable on your real data is established only in a pilot project.
01 The problem - You learn of the delay when it is too late
On-time delivery decides penalty and customer relationship
A late order costs twice: the agreed contract penalty and the customer's trust. Today the delay often shows only when the deadline is practically unkeepable - then only expensive damage control remains.
The early indicators sit in the data: order lead time, material availability, current bottleneck load, backlog of upstream steps. A model estimates the probability of delay per order - weeks before the deadline.
Because one week of lead time is enough to reschedule capacity instead of paying the penalty.
02 The model - Delay risk per order
Classification and survival analysis on order progress, material and bottleneck load
Gefaehrdete Auftraege: 34 von 410 Mediane Vorwarnzeit vor Liefertermin: 9 Tage
The survival component delivers not just "on time/late" but how much buffer an order still has. This lets orders be prioritised: the one with the highest risk and the least remaining buffer comes first - data-driven sequencing instead of gut feeling.
03 Business impact - Avoid penalties, keep customers
The lever: avoided contract penalties and protected customer relationships - rush costs are counted in module 05
Every assumption comes from the sample plant and is stored centrally. With your real figures only the input changes, not the method.
| Item | Value |
|---|---|
| Contract penalties / year | €340,000 |
| Avoidance via early warning (45%) | €153,000 |
| Protected customer retention (1 customer CM) | €95,000 |
| Result: savings / year | €248,000 |
Assumptions of a sample plant - in a real project your data replaces these values.
Contract penalties can be quantified - the bigger value is often invisible: a customer who can rely on deadlines stays. Even one avoided supplier switch per year adds the contribution margin of a mid-sized customer to the bill.
04 Next steps on your shop floor
From the risk list to control
We connect order progress, material status and bottleneck load from ERP/MES - the fields already exist.
For one order segment the risk model is built. You see the early warnings validated against real past delays.
Daily risk list: "These orders are at risk - recommendation: pull order X forward, reschedule capacity for order Y."