W4200224444 Peer-reviewed research AI × management and organizations

Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing

Julian Senoner · Torbjørn Netland · Stefan Feuerriegel

Also recorded as: doi:10.1287/mnsc.2021.4190

Management Science

Recorded claims
1
coded from the inspected source
Evidence classes
1
crossref-api-metadata
Recorded limitations
4
stated, never hidden

From the hash-bound evidence releases

Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

A decision model combining explainable AI with quality-management theory was validated at a semiconductor manufacturer, where a field experiment and subsequent rollout reported improved production yield and revealed process-quality drivers missed by traditional methods.

confidence: high crossref-api-metadata apw-c1-obot-journals

Scope: publisher-deposited abstract metadata; manufacturing process management, improvement decisions, and operational performance; experiment denominator and period are not stated

Structured relationships

In-corpus citations

Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.

Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

  • Abstract-level Crossref metadata only; the blocked publisher surface was not retried.
  • The captured abstract does not state the experiment denominator or period; numerical yield effects are not carried forward.
  • Crossref supplies only a year and month for publication, so published_date is null rather than an invented day.
  • Full text was not inspected.

Source independence

publisher-only