Implementation of a Performance Dashboard for Key Performance Indicators Monitoring in a Furniture Manufacturing Company

Authors

  • Rui Ferreira School of Technology, Polytechnic University of Cávado and Ave – IPCA Author
  • Sónia Longras 2Ai – Applied Artificial Intelligence Laboratory. School of Technology, Polytechnic University of Cávado and Ave – IPCA Author
  • Vânia Dias 2Ai – Applied Artificial Intelligence Laboratory. School of Technology, Polytechnic University of Cávado and Ave – IPCA Author
  • Natália Rego 2Ai – Applied Artificial Intelligence Laboratory. School of Technology, Polytechnic University of Cávado and Ave – IPCA Author
  • António Rocha 2Ai – Applied Artificial Intelligence Laboratory. School of Technology, Polytechnic University of Cávado and Ave – IPCA Author https://orcid.org/0000-0003-0863-6567

DOI:

https://doi.org/10.14513/tge-jres.00624

Keywords:

Business Intelligence, Manufacturing, Decision Support Systems, Data Visualization

Abstract

The increasing competitiveness in manufacturing sectors demands efficient performance monitoring systems to support strategic decision-making. This study presents the implementation of a comprehensive business intelligence dashboard for monitoring key performance indicators (KPIs) in a Portuguese furniture manufacturing company. The dashboard integrates eight product categories tracked over three years (2021-2023), monitoring metrics including sales volume, average selling price, production costs, profit margins, customer complaints, production defects, average production time, and production line efficiency. The implementation followed the Plan-Do-Check-Act (PDCA) cycle methodology, utilizing Microsoft Power BI as the primary visualization tool integrated with the company's ERP system. Additionally, a pilot inventory control system was developed to address the company's lack of stock management capabilities. Results demonstrate significant improvements in operational visibility, with production efficiency increasing from 64.7% in 2022 to 98.8% in 2023. However, the analysis also revealed concerning trends, including a 30% decrease in sales volume from 2021 to 2023 and declining profit margins across several product categories. The dashboard enabled data-driven identification of bottlenecks in furniture production, quality issues in specific product lines, and opportunities for pricing optimization. The system successfully centralized fragmented data sources, reducing decision-making time and providing management with real-time performance insights. This research contributes practical insights for small and medium-sized manufacturing enterprises seeking to implement business intelligence solutions while highlighting the challenges of data standardization and the critical importance of organizational change management in digital transformation initiatives.

References

Alves, É. A. (2015). O PDCA como ferramenta de gestão da rotina [The PDCA as a routine management tool]. In XI

Congresso Nacional de Excelência em Gestão (pp. 1–12). Rio de Janeiro, Brazil.

Alsakhen, I., Buics, L., & Süle, E. (2024). AI-driven resilience in revolutionizing supply chain management: A systematic

literature review. Journal of Infrastructure, Policy and Development, 8(16), Article 9474. https://doi.org/10.24294/jipd9474

Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the

ACM, 54(8), 88–98. https://doi.org/10.1145/1978542.1978562

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS

Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503

Cruz, J. R. (2021). Aplicação da metodologia Lean Six Sigma numa empresa de indústria automóvel [Application of the Lean Six Sigma

methodology in an automotive industry company] [Master’s thesis, Universidade do Minho]. RepositóriUM.

https://hdl.handle.net/1822/76159

DeNisi, A., & Smith, C. E. (2014). Performance appraisal, performance management, and firm-level performance: A review,

a proposed model, and new directions for future research. Academy of Management Annals, 8(1), 127–179.

https://doi.org/10.5465/19416520.2014.873178

Dowding, D., Randell, R., Gardner, P., Fitzpatrick, G., Dykes, P., Favela, J., Hamer, S., Whitewood-Moores, Z., Hardiker, N.,

Borycki, E., & Currie, L. (2014). Dashboards for improving patient care: Review of the literature. International Journal of

Medical Informatics, 84(2), 87–100. https://doi.org/10.1016/j.ijmedinf.2014.10.001

Duan, L., & Xu, L. D. (2012). Business intelligence for enterprise systems: A survey. IEEE Transactions on Industrial

Informatics, 8(3), 679–687. https://doi.org/10.1109/TII.2012.2188804

Eckerson, W. W. (2011). Performance dashboards: Measuring, monitoring, and managing your business (2nd ed.). John Wiley

& Sons. https://doi.org/10.1002/9781119199984

Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems:

The relationship between business process and organizational performance. International Journal of Accounting

Information Systems, 9(3), 135–153. https://doi.org/10.1016/j.accinf.2008.03.001

Few, S. (2004). Dashboard confusion. Intelligent Enterprise, 7(4), 1–6.

Freitas, C. D. (2021). Proposta de transição da norma NP EN ISO 9001:2008 para a norma NP EN ISO 9001:2015 numa

empresa de produção de linhas de costura [Proposal for the transition from standard NP EN ISO 9001:2008 to

standard NP EN ISO 9001:2015 in a sewing thread production company] [Master’s thesis, Universidade do

Minho]. RepositóriUM. https://hdl.handle.net/1822/74279

Hou, C.-K. (2012). Examining the effect of user satisfaction on system usage and individual performance with

business intelligence systems: An empirical study of Taiwan’s electronics industry. International Journal of

Information Management, 32(6), 560–573. https://doi.org/10.1016/j.ijinfomgt.2012.03.001

Hódosi, G., Süle, E., & Bodis, T. (2023). Multi-criteria decision making: A comparative analysis. In 103rd International

Scientific Conference on Economic and Social Development – Digital Entrepreneurship in the Context of the UN Sustainable

Development Goals (pp. 181–190).Indelicato, G. (2012). Book review: Project management metrics, KPIs, and dashboards: A guide to measuring and monitoring project performance. Project Management Journal, 43(3), 95–96. https://doi.org/10.1002/pmj.21263

Jagusiak-Kocik, M. (2020). PDCA cycle as a part of continuous improvement in the production company–A case

study. Production Engineering Archives, 14(5), 19–22. https://doi.org/10.30657/pea.2017.14.05

Johnson, C. N. (2002). The benefits of PDCA. Quality Progress, 35(5), 120.

Kaplan, R. S., & Norton, D. P. (1996). Linking the Balanced Scorecard to strategy. California Management Review39(1), 53–79. https://doi.org/10.2307/41165876

Malik, S. (2005). Enterprise dashboards: Design and best practices for IT. John Wiley & Sons.

Müller, J. M., Buliga, O., & Voigt, K. I. (2018). Fortune favors the prepared: How SMEs approach business model

innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17.

https://doi.org/10.1016/j.techfore.2017.12.019

Olszak, C. M., & Ziemba, E. (2012). Critical success factors for implementing business intelligence systems in

small and medium enterprises on the example of Upper Silesia, Poland. Interdisciplinary Journal of Information,

Knowledge, and Management, 7, 129–150. https://doi.org/10.28945/1584

Priyono, A., Moin, A., & Putri, V. N. A. O. (2020). Identifying digital transformation paths in the business model

of SMEs during the COVID-19 pandemic. Journal of Open Innovation: Technology, Market, and Complexity, 6(4),

Article 104. https://doi.org/10.3390/joitmc6040104

Ramakrishnan, T., Khuntia, J., Kathuria, A., & Saldanha, T. J. (2020). An integrated model of business intelligence

& analytics capabilities and organizational performance. Communications of the Association for Information Systems,

46, 722–749. http://doi.org/10.17705/1CAIS.04631

Rejeb, A., Rejeb, K., Süle, E., Hassoun, A., & Keogh, J. G. (2025). Knowledge flows in Industry 4.0 research: A

longitudinal and dynamic analysis. Journal of Data, Information and Management, 7, 123–145.

https://doi.org/10.1007/s42488-025-00146-3

Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital

transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901.

https://doi.org/10.1016/j.jbusres.2019.09.022

Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96–99.

https://doi.org/10.1109/MC.2007.331

Zelles, T., Biliniovics-Sipos, J., & Remsei, S. (2024). Literature review: Understanding the role of reporting. In

111th International Scientific Conference on Economic and Social Development – Navigating into the Future: The New

Employee Experience, Budapest, 9–10 May 2024 (pp. 99–107).

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Published

2026-06-01

Issue

Section

Original Research Articles

How to Cite

Ferreira, R., Longras, S., Dias, V., Rego, N., & Rocha, A. (2026). Implementation of a Performance Dashboard for Key Performance Indicators Monitoring in a Furniture Manufacturing Company. Tér - Gazdaság - Ember Journal of Region, Economy and Society. https://doi.org/10.14513/tge-jres.00624

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