W7167691258 Working papers AI × management and organizations

When the Algorithm Explains Itself: Compute-Efficient Machine Learning and Human-AI Teaming for Supply-Chain Backorder Management

Janak Suthar · Paramjit Mahesh Thakur · Mugdha Dogare

Also recorded as: doi:10.2139/ssrn.7081499 · ssrn:7081499

ssrn abstract page · 2026-07-08

Recorded claims
1
coded from the inspected source
Evidence classes
1
ssrn-abstract-page
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 computational study using 1.69 million stock-keeping-unit records reports that a compact genetic algorithm matched random-search held-out accuracy and F1 at about 0.89 across five learners and five random seeds while reducing tuning time by 28.5% and model evaluations by 16.2%; a separate pilot with 21 practicing planners reports positive associations between trust and adoption intention and between transparency and perceived team performance.

confidence: moderate ssrn-abstract-page apw-e1-ssrn-backfill

Scope: Study 1: public dataset of 1.69 million SKU records, five learners, five random seeds, with dataset period unstated. Study 2: 21 practicing planners, with recruitment universe, study period, and condition denominator unstated; reported coefficients were beta=.49 (p=.023) and beta=.65 (p=.004)

Structured relationships

In-corpus citations

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

Cites (0)

No in-corpus outgoing citation is available in the current enrichment coverage.

Cited by (0)

No in-corpus incoming citation is available in the current enrichment coverage.

Boundaries

Limitations & independence

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

Recorded limitations

  • Abstract-level evidence only; full text and PDFs were not inspected.
  • The SSRN page explicitly labels the work a preprint that has not been peer reviewed.
  • The abstract does not state the stock-keeping-unit dataset period or firm universe, nor the pilot recruitment frame, study period, condition structure, uncertainty intervals, or multiple-testing plan.
  • The 21-planner pilot is described as instrument validation; the page says a preregistered controlled experiment is planned rather than completed.

Source independence

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