ERP integration and logistics optimization: key indicators for graduate theses

Integrating an ERP with supply chain management is not just a technical problem: it is a fertile field for high-impact research. This guide synthesizes frameworks, indicators and methods so your thesis, within the Supply Chain, Logistics and Operations line, delivers rigorous, publishable evidence.

Theoretical frameworks to anchor your research

Every solid study starts with a clear theoretical lens. Three complementary perspectives coexist for ERP-SCM integration. The resource-based view (RBV) holds that the ERP, as a valuable and hard-to-imitate resource, creates competitive advantage when combined with logistics capabilities. To study adoption and acceptance, the TAM (Technology Acceptance Model) explains use intention via perceived usefulness and ease of use, while the TOE (Technology-Organization-Environment) framework adds organizational and contextual factors. A frequent and publishable combination is RBV (for impact) + TOE (for adoption drivers). What matters is justifying why you chose that framework: committees penalize decorative theory.

Logistics indicators and KPIs worth measuring

The empirical heart of many theses lies in indicators. The most robust and comparable are: OTIF (On-Time In-Full), measuring orders delivered on time and complete; total logistics cost as a percentage of sales; lead time for procurement and delivery; inventory turnover and days of stock; and the World Bank’s Logistics Performance Index (LPI) for cross-country comparison. Stević et al. (2024) show that LPI rankings require complementary decision frameworks, since a single indicator hides inefficiencies. For a quantitative thesis, define each KPI operationally, specify the calculation formula and the unit of analysis (order, line, shipment): poor operationalization is the main cause of major revisions.

Common methods: PLS-SEM, DEA and simulation

Methodological choice should follow the research question, not the other way around. PLS-SEM is ideal when testing causal relationships with latent constructs and medium samples (100-300 observations), for example to model how ERP integration affects performance via mediating capabilities. DEA (Data Envelopment Analysis) evaluates the relative efficiency of comparable units (plants, suppliers, countries) without assuming a functional form; Ansari, Kant and Shankar (2022) illustrate its use in supply chains. Simulation (Monte Carlo or system dynamics) is best when exploring disruption scenarios. Always report assumptions, software (SmartPLS, DEAP, Arena) and validity tests.

Trends 2020-2026: resilience, analytics and ERP-SCM integration

The pandemic redirected the agenda toward resilience. Zhao, Hong and Lau (2023) show that supply chain digitalization strengthens absorption, response and recovery capabilities, and that these mediate performance. Advanced analytics and cloud ERP are replacing monolithic ERPs, while sustainability and TOE-based adoption (Damali, Kocakulah and Ozkul, 2021) open new lines. An opportunity for your thesis: integrate the post-COVID resilience perspective with classic KPIs, a crossover still little explored in Latin American studies.

Frequent methodological mistakes (and how to avoid them)

Avoid five traps students repeat: (1) confusing integration with implementation —measuring whether the ERP was installed is not the same as measuring whether it was integrated with SCM—; (2) common-method bias from using a single self-administered questionnaire; (3) indicators without a benchmark that make it impossible to tell whether 85% OTIF is good or bad; (4) generalizing from a single case without justifying the design; and (5) forgetting triangulation. Anticipate these critiques in your design: it raises defensibility.

References

• Ansari, Z. N., Kant, R., & Shankar, R. (2022). Measuring operational efficiency of remanufacturing supply chain: a data envelopment analysis approach. International Journal of Logistics Systems and Management, 42(3), 328. https://doi.org/10.1504/IJLSM.2022.124232
• Damali, U., Kocakulah, M., & Ozkul, A. S. (2021). Investigation of Cloud ERP Adoption in the Healthcare Industry Through Technology-Organization-Environment (TOE) Framework. International Journal of Healthcare Information Systems and Informatics, 16(4), 1-14. https://doi.org/10.4018/IJHISI.289463
• García Prado, N. J., Tigre Ortega, F. G., Lema Chicaiza, F. R., Rosero Mantilla, C. A., Sánchez Rosero, C. H., & Sigcha Quezada, A. (2024). Indicadores de rendimiento de la cadena de suministro 5.0 para la agroindustria: revisión de literatura. ConcienciaDigital, 7(3), 112-134. https://doi.org/10.33262/concienciadigital.v7i3.3089
• Stević, Ž., Ersoy, N., Başar, E. E., & Baydaş, M. (2024). Addressing the Global Logistics Performance Index Rankings with Methodological Insights and an Innovative Decision Support Framework. Applied Sciences, 14(22), 10334. https://doi.org/10.3390/app142210334
• Tarigan, Z. J. H., Siagian, H., & Jie, F. (2021). Impact of Enhanced Enterprise Resource Planning (ERP) on Firm Performance through Green Supply Chain Management. Sustainability, 13(8), 4358. https://doi.org/10.3390/su13084358
• Zhao, N., Hong, J., & Lau, K. H. (2023). Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model. International Journal of Production Economics, 259, 108817. https://doi.org/10.1016/j.ijpe.2023.108817