From Software Acquisition to Operational Capability: Measuring ERP-Enabled Performance: A Transaction-Level Benchmarking Study and Prospective Matched-Case Evaluation Framework for SMES And Government Vendors

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[45] Appendix A. Detailed Field KPI Dictionary

[46] Table 19. Publication-ready KPI dictionary for prospective studies

[47] KPI

[48] Definition

[49] Source

[50] Recommended analysis

[51] Control note

[52] Order-to-cash cycle

[53] Calendar days from accepted order to cleared cash

[54] Order, shipment, invoice, receipt timestamps

[55] Median and 90th percentile; separate customer terms

[56] Excludes disputed orders only if reason documented

[57] Inventory accuracy

[58] 1 - sum absolute count variance / sum system quantity

[59] Cycle-count and ERP stock records

[60] By site, class and item criticality

[61] Negative or zero denominators require defined handling

[62] Reporting latency

[63] Hours/days from reporting cutoff to approved report availability

[64] Close calendar, workflow audit log

[65] Median by report; 95th percentile

[66] Definition must distinguish draft from approved

[67] Invoice rejection

[68] Rejected or returned invoices / submitted invoices

[69] Billing workflow, customer portal, contract system

[70] By reason, customer/agency and value

[71] Duplicates and administrative returns separated

[72] Delivery reliability

[73] On-time complete deliveries / due deliveries

[74] Promise date, shipment, proof of delivery

[75] By service class, contract and root cause

[76] Promise-date changes logged and governed

[77] User adoption

[78] Active competent users / assigned users

[79] Audit log plus task-based assessment

[80] Role specific; monthly/quarterly

[81] Logins alone are insufficient

[82] Control exception rate

[83] Material exceptions / eligible transactions

[84] Control engine and workflow

[85] By control, cause, owner and recurrence

[86] Control changes versioned

[87] Corrective-action closure

[88] Actions closed with verified evidence / actions due

[89] Issue tracker

[90] On-time and overdue ageing

[91] Reopened actions reported separately

[92] Data-quality defect rate

[93] Validated critical defects / records sampled or processed

[94] Data-quality rules and sampling

[95] Completeness, validity, consistency, uniqueness, timeliness

[96] Automated passes do not replace accuracy sampling

[97] Benefit realisation

[98] Verified cumulative benefit / approved target

[99] Benefit register and finance validation

[100] By benefit owner and intervention component

[101] Avoid double counting and unsupported attribution

[102] Appendix B. ERP Capability Evidence Checklist

[103] Approved current-state and future-state process maps with end-to-end ownership.

[104] Requirements traceability linking process, data, control, report and integration needs to testing.

[105] Master-data standards, ownership, migration reconciliation and unresolved-defect register.

[106] Role/access matrix, segregation-of-duties analysis and approval-workflow evidence.

[107] Test coverage for normal, exception, reversal, security, integration and recovery scenarios.

[108] Role-based training materials, task assessments, attendance and remediation evidence.

[109] Super-user and support model with severity, response, escalation and closure expectations.

[110] Baseline KPI pack with signed definitions and historical trends.

[111] Cutover decision, rollback plan, open-risk acceptance and hypercare plan.

[112] Post-go-live adoption, data-quality, control, service and benefits dashboard.

[113] Change-control and release records linking configuration changes to retesting.

[114] Post-implementation review documenting benefits, failures, lessons and next actions.

[115] Appendix C. Interpretation Guide for the EOCI

[116] Table 20. EOCI interpretation bands

[117] Score

[118] Tier

[119] Recommended response

[120] 70-100

[121] Strong

[122] Observed group-level outcomes are comparatively controlled; investigate sustainability, hidden workarounds and cost

[123] 60-69.9

[124] Controlled

[125] Generally functioning with targeted weaknesses; prioritise the lowest component and recurrent exceptions

[126] 50-59.9

[127] Fragile

[128] Material delivery/control weakness; conduct root-cause review and define corrective programme

[129] Below 50

[130] Critical

[131] Systematic capability mismatch or severe outcome failure; executive ownership and immediate redesign required

[132] Note: Bands are managerial heuristics developed for this paper. They require validation before external benchmarking or high-stakes use.

[133] Appendix D. Reproducibility and Quality-Control Notes

[134] Use Order Id as the delivery-level aggregation key and verify within-order consistency before aggregation.

[135] Exclude realised outcomes from predictors when building pre-dispatch risk models.

[136] Retain a temporally separated or site-separated external test set where available; random holdout is a minimum, not a final validation.

[137] Report discrimination and calibration together. A high AUC does not guarantee accurate probabilities.

[138] Version all metric definitions, data transformations, exclusion rules, model code and dashboard logic.

[139] Preserve a cohort-flow table showing source rows, exclusions, duplicates, aggregation and final samples.

[140] Assess missingness and data-quality mechanisms; do not interpret complete fields as necessarily accurate.

[141] Perform subgroup and site-level analyses before operational deployment, and document where estimates are unstable.

[142] Use human review for exceptions and record overrides, rationales and outcomes so that the workflow itself can be evaluated.

[143] Reproduce the present analysis on the full source dataset before journal submission and archive the exact extract checksum.

@article{1722515,
      author = {Nkosana Mkandla, Marlon Munjoma, Melody Sydney, Flora Phiri, Munashe Naphtali Mupa},
      title = {From Software Acquisition to Operational Capability: Measuring ERP-Enabled Performance: A Transaction-Level Benchmarking Study and Prospective Matched-Case Evaluation Framework for SMES And Government Vendors},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {3127-3153},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722515.pdf},
      abstract = {Enterprise resource planning (ERP) projects are often evaluated at acquisition, configuration or go-live, although the economic and operational value of an integrated system depends on whether the organisation redesigns processes, governs data, embeds controls, develops user capability and measures realised benefits. This study develops an acquisition-to-capability framework and tests its operational measurement layer using a public 70,000-record analytical extract of the DataCo Smart Supply Chain dataset. The extract contains 44,026 unique orders dated from 2015 to 2018. Analysis was conducted at order and order-line levels using descriptive statistics, cross-segment heat maps, robust logistic regression, holdout prediction, calibration assessment, bootstrap confidence intervals and sensitivity testing of a six-component ERP Operational Capability Index (EOCI). The order-level late-delivery rate was 54.7%, severe delay affected 23.6%, negative-profit exposure affected 19.6%, and hard workflow exceptions affected 10.8%. Shipping configuration was the dominant observable delivery factor: compared with Standard Class, adjusted late-delivery odds were 34.22 times higher for First Class, 5.42 times higher for Second Class and 1.40 times higher for Same Day. A leakage-controlled holdout model achieved an area under the receiver operating characteristic curve of 0.731, a Brier score of 0.197, a calibration intercept of 0.008 and a calibration slope of 0.967. Observed on-time delivery rose from 11.2% in the lowest readiness quartile to 62.9% in the highest, a 51.6-percentage-point difference. EOCI rankings were stable under alternative weights (Spearman rho=0.998). The shipping-mode odds ratios reflect this dataset's own service-level design and are reported as a demonstration of what transaction-level analytics can reveal, not as a general finding about ERP-enabled delivery performance. The findings show that transaction visibility can diagnose capability gaps, but software records alone cannot establish implementation causality. The paper therefore specifies a matched difference-in-differences field protocol, KPI dictionary, governance gates and implementation playbook for SMEs and small government vendors. The central conclusion is that ERP value is realised through a governed operating system, not through software possession.},
      keywords = {enterprise resource planning; operational capability; process redesign; data governance; internal controls; user adoption; SME digital transformation; government vendors; supply-chain analytics; benefits realization},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722515}
  }