The Strategic Role of Forecasting in Managerial Planning: A Scientific Literature Review
DOI:
https://doi.org/10.58857/JFAE.2024.v01.i02.p05Keywords:
Forecasting in management, Strategic planning, Data-based decision making, Forecasting research trends, Systematic Literature ReviewAbstract
This study aims to systematically review the academic literature related to the role of forecasting in managerial planning, using the Systematic Literature Review (SLR) approach. This review was conducted on 47 articles published between 2015 and 2024, collected from reputable databases such as Scopus and Web of Science. The focus of the study includes methodological approaches, key findings, research trends, and geographical and sectoral distribution of the analyzed studies. Following the PRISMA protocol, this study successfully identified key topics such as prediction accuracy, forecasting applications in strategic decision making, and challenges in applying forecasting in a dynamic business environment.
The benefits of this study include strengthening the theoretical and practical foundations in forecasting for more accurate and objective decision-making. Academics can use the study results as material for curriculum development and further research directions, while practitioners obtain guidance in strategically implementing forecasting. This study also fills the gap in the literature related to the integration of forecasting theory and practice. It contributes to educating organizations to shift from an intuitive approach to a data-driven approach in future planning.
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Aaronson, D., Brave, S. A., Butters, R. A., Fogarty, M. S., Sacks, D. W., & Seo, B. (2022). Forecasting Unemployment Insurance Claims in Realtime With Google Trends. International Journal of Forecasting, 38(2), 567–581. https://doi.org/10.1016/j.ijforecast.2021.04.001
Achterberg, M. A., Prasse, B., Ma, L., Trajanovski, S., Kitsak, M., & Mieghem, P. V. (2022). Comparing the Accuracy of Several Network-Based COVID-19 Prediction Algorithms. International Journal of Forecasting, 38(2), 489–504. https://doi.org/10.1016/j.ijforecast.2020.10.001
Alinaghian, L., & Razmdoost, K. (2021). How Do Social Enterprises Manage Business Relationships? A Review of the Literature and Directions for Future Research. Journal of Business Research, 136, 488–498. https://doi.org/10.1016/j.jbusres.2021.08.003
Alkhuraiji, A., Liu, S., Oderanti, F. O., & Megicks, P. (2016). New Structured Knowledge Network for Strategic Decision-Making in IT Innovative and Implementable Projects. Journal of Business Research, 69(5), 1534–1538. https://doi.org/10.1016/j.jbusres.2015.10.012
Amankwah‐Amoah, J., Khan, Z., Wood, G., & Knight, G. (2021). COVID-19 and Digitalization: The Great Acceleration. Journal of Business Research, 136, 602–611. https://doi.org/10.1016/j.jbusres.2021.08.011
Annosi, M. C., Martini, A., Brunetta, F., & Marchegiani, L. (2020). Learning in an Agile Setting: A Multilevel Research Study on the Evolution of Organizational Routines. Journal of Business Research, 110, 554–566. https://doi.org/10.1016/j.jbusres.2018.05.011
Azadi, M., Yousefi, S., Saen, R. F., Shabanpour, H., & Jabeen, F. (2023). Forecasting Sustainability of Healthcare Supply Chains Using Deep Learning and Network Data Envelopment Analysis. Journal of Business Research, 154, 113357. https://doi.org/10.1016/j.jbusres.2022.113357
Bag, S., Dhamija, P., Bryde, D., & Singh, R. K. (2022). Effect of Eco-Innovation on Green Supply Chain Management, Circular Economy Capability, and Performance of Small and Medium Enterprises. Journal of Business Research, 141, 60–72. https://doi.org/10.1016/j.jbusres.2021.12.011
Bantis, E., Clements, M. P., & Urquhart, A. (2023). Forecasting GDP Growth Rates in the United States and Brazil Using Google Trends. International Journal of Forecasting, 39(4), 1909–1924. https://doi.org/10.1016/j.ijforecast.2022.10.003
Behl, A., Jayawardena, N. S., Pereira, V., Islam, N., Giudice, M. D., & Choudrie, J. (2022). Gamification and E-Learning for Young Learners: A Systematic Literature Review, Bibliometric Analysis, and Future Research Agenda. Technological Forecasting and Social Change, 176, 121445. https://doi.org/10.1016/j.techfore.2021.121445
Bergmeir, C., Hyndman, R., & Benítez, J. M. (2016). Bagging Exponential Smoothing Methods Using STL Decomposition and Box–Cox Transformation. International Journal of Forecasting, 32(2), 303–312. https://doi.org/10.1016/j.ijforecast.2015.07.002
Boone, T., Boylan, J. E., Fildes, R., Ganeshan, R., & Sanders, N. R. (2019). Perspectives on Supply Chain Forecasting. International Journal of Forecasting, 35(1), 121–127. https://doi.org/10.1016/j.ijforecast.2018.11.002
Bortoló, G. M., Valdés, J. Á., & Nicolás-Sans, R. (2023). Sustainable, Technological, and Innovative Challenges Post Covid-19 in Health, Economy, and Education Sectors. Technological Forecasting and Social Change, 190, 122424. https://doi.org/10.1016/j.techfore.2023.122424
Brown, L. D., & Zhou, L. (2015). Interactions Between Analysts’ and Managers’ Earnings Forecasts. International Journal of Forecasting, 31(2), 501–514. https://doi.org/10.1016/j.ijforecast.2014.10.002
Chowdhury, S. B., Dasgupta, R., Choudhury, B. K., & Sen, N. (2023). Evolving Alliance Between Corporate Environmental Performance and Financial Performance: A Bibliometric Analysis and Systematic Literature Review. Business and Society Review, 128(1), 95–131. https://doi.org/10.1111/basr.12301
Claeskens, G., Magnus, J. R., Vasnev, A. L., & Wang, W. (2016). The Forecast Combination Puzzle: A Simple Theoretical Explanation. International Journal of Forecasting, 32(3), 754–762. https://doi.org/10.1016/j.ijforecast.2015.12.005
Cruz‐Cárdenas, J., Забелина, Е., Guadalupe-Lanas, J., Palacio-Fierro, A., & Ramos-Galarza, C. (2021). COVID-19, Consumer Behavior, Technology, and Society: A Literature Review and Bibliometric Analysis. Technological Forecasting and Social Change, 173, 121179. https://doi.org/10.1016/j.techfore.2021.121179
Del-Castillo-Feito, C., Blanco‐González, A., & Hernández‐Perlines, F. (2022). The Impacts of Socially Responsible Human Resources Management on Organizational Legitimacy. Technological Forecasting and Social Change, 174, 121274. https://doi.org/10.1016/j.techfore.2021.121274
Derwik, P., Hellström, D., & Karlsson, S. (2016). Manager Competences in Logistics and Supply Chain Practice. Journal of Business Research, 69(11), 4820–4825. https://doi.org/10.1016/j.jbusres.2016.04.037
Diks, C., & Fang, H. (2020). Comparing Density Forecasts in a Risk Management Context. International Journal of Forecasting, 36(2), 531–551. https://doi.org/10.1016/j.ijforecast.2019.07.006
Do, H., Budhwar, P., Shipton, H., Nguyen, H.-D., & Nguyen, B. (2022). Building Organizational Resilience, Innovation Through Resource-Based Management Initiatives, Organizational Learning and Environmental Dynamism. Journal of Business Research, 141, 808–821. https://doi.org/10.1016/j.jbusres.2021.11.090
Doornik, J. A., Castle, J. L., & Hendry, D. F. (2022). Short-Term Forecasting of the Coronavirus Pandemic. International Journal of Forecasting, 38(2), 453–466. https://doi.org/10.1016/j.ijforecast.2020.09.003
Dovern, J., & Jannsen, N. (2017). Systematic Errors in Growth Expectations Over the Business Cycle. International Journal of Forecasting, 33(4), 760–769. https://doi.org/10.1016/j.ijforecast.2017.03.003
Durst, S., Hinteregger, C., & Zięba, M. (2019). The Linkage Between Knowledge Risk Management and Organizational Performance. Journal of Business Research, 105, 1–10. https://doi.org/10.1016/j.jbusres.2019.08.002
Fildes, R., Kolassa, S., & Ma, S. (2022). Post-Script—Retail Forecasting: Research and Practice. International Journal of Forecasting, 38(4), 1319–1324. https://doi.org/10.1016/j.ijforecast.2021.09.012
Gardner, E. S. (2015). Conservative Forecasting With the Damped Trend. Journal of Business Research, 68(8), 1739–1741. https://doi.org/10.1016/j.jbusres.2015.03.033
Giarmoleo, F. V., Ferrero, I., Rocchi, M., & Pellegrini, M. M. (2024). What Ethics Can Say on Artificial Intelligence: Insights From a Systematic Literature Review. Business and Society Review, 129(2), 258–292. https://doi.org/10.1111/basr.12336
Goodwin, P., Gönül, M. S., & Önkal, D. (2013). Antecedents and Effects of Trust in Forecasting Advice. International Journal of Forecasting, 29(2), 354–366. https://doi.org/10.1016/j.ijforecast.2012.08.001
Gunasekaran, A., Παπαδόπουλος, Θ., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B. T., & Akter, S. (2017). Big Data and Predictive Analytics for Supply Chain and Organizational Performance. Journal of Business Research, 70, 308–317. https://doi.org/10.1016/j.jbusres.2016.08.004
Gürbüz, M. Ç., Yurt, Ö., Ozdemir, S., Sena, V., & Yu, W. (2023). Global Supply Chains Risks and COVID-19: Supply Chain Structure as a Mitigating Strategy for Small and Medium-Sized Enterprises. Journal of Business Research, 155, 113407. https://doi.org/10.1016/j.jbusres.2022.113407
Habicher, D., Windegger, F., Heiko A. von der Gracht, & Pechlaner, H. (2022). Beyond the COVID-19 Crisis: A Research Note on Post-Pandemic Scenarios for South Tyrol 2030+. Technological Forecasting and Social Change, 180, 121749. https://doi.org/10.1016/j.techfore.2022.121749
Harris, L. C., & Ogbonna, E. (2011). Antecedents and Consequences of Management-Espoused Organizational Cultural Control. Journal of Business Research, 64(5), 437–445. https://doi.org/10.1016/j.jbusres.2010.03.002
Hill, A. V., Zhang, W., & Burch, G. F. (2015). Forecasting the Forecastability Quotient for Inventory Management. International Journal of Forecasting, 31(3), 651–663. https://doi.org/10.1016/j.ijforecast.2014.10.006
Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G. (2023). Governance of Artificial Intelligence Applications in a Business Audit via a Fusion Fuzzy Multiple Rule-Based Decision-Making Model. Financial Innovation, 9(1). https://doi.org/10.1186/s40854-022-00436-4
Ibrahim, R. N., Ye, H., L’Ecuyer, P., & Shen, H. (2016). Modeling and Forecasting Call Center Arrivals: A Literature Survey and a Case Study. International Journal of Forecasting, 32(3), 865–874. https://doi.org/10.1016/j.ijforecast.2015.11.012
Jancenelle, V. E. (2021). Executive Cues of Organizational Virtue and Market Performance: Creating Value During Times of Earnings Uncertainty. Business and Society Review, 126(2), 193–209. https://doi.org/10.1111/basr.12235
Jiang, M., Jia, F., Chen, L., & Xing, X. (2024). Technology Adoption in Socially Sustainable Supply Chain Management: Towards an Integrated Conceptual Framework. Technological Forecasting and Social Change, 206, 123537. https://doi.org/10.1016/j.techfore.2024.123537
Kapoor, K. K., Bigdeli, A. Z., Dwivedi, Y. K., Schroeder, A., Beltagui, A., & Baines, T. (2021). A Socio-Technical View of Platform Ecosystems: Systematic Review and Research Agenda. Journal of Business Research, 128, 94–108. https://doi.org/10.1016/j.jbusres.2021.01.060
Kusi‐Sarpong, S., Mubarik, M. S., Khan, S. A., Brown, S., & Mubarak, M. F. (2022). Intellectual Capital, Blockchain-Driven Supply Chain and Sustainable Production: Role of Supply Chain Mapping. Technological Forecasting and Social Change, 175, 121331. https://doi.org/10.1016/j.techfore.2021.121331
Kwon, I. G., Kim, S., & Martin, D. G. (2016). Healthcare Supply Chain Management; Strategic Areas for Quality and Financial Improvement. Technological Forecasting and Social Change, 113, 422–428. https://doi.org/10.1016/j.techfore.2016.07.014
Lee, S. M., & Lee, D. (2021). Opportunities and Challenges for Contactless Healthcare Services in the Post-Covid-19 Era. Technological Forecasting and Social Change, 167, 120712. https://doi.org/10.1016/j.techfore.2021.120712
Lee, S. M., & Trimi, S. (2021). Convergence Innovation in the Digital Age and in the COVID-19 Pandemic Crisis. Journal of Business Research, 123, 14–22. https://doi.org/10.1016/j.jbusres.2020.09.041
Leonidou, E., Christofi, M., Vrontis, D., & Thrassou, A. (2020). An Integrative Framework of Stakeholder Engagement for Innovation Management and Entrepreneurship Development. Journal of Business Research, 119, 245–258. https://doi.org/10.1016/j.jbusres.2018.11.054
Lim, W. M., Yap, S. F., & Makkar, M. (2021). Home Sharing in Marketing and Tourism at a Tipping Point: What Do We Know, How Do We Know, and Where Should We Be Heading? Journal of Business Research, 122, 534–566. https://doi.org/10.1016/j.jbusres.2020.08.051
Lyngdoh, T., Chefor, E., Hochstein, B., Britton, B. P., & Amyx, D. (2021). A Systematic Literature Review of Negative Psychological States and Behaviors in Sales. Journal of Business Research, 122, 518–533. https://doi.org/10.1016/j.jbusres.2020.09.031
Makridakis, S., Williams, T., Kirkham, R., & Papadaki, M. (2019). Forecasting, Uncertainty and Risk Management. International Journal of Forecasting, 35(2), 641–643. https://doi.org/10.1016/j.ijforecast.2018.10.002
Malik, S., Chhabra, M., & Chandra, G. M. (2022). Understanding the Impact of Pandemics on Society With a Special Focus on COVID‐19. Business and Society Review, 127(4), 835–861. https://doi.org/10.1111/basr.12288
Mariadoss, B. J., Chi, T., Tansuhaj, P., & Pomirleanu, N. (2016). Influences of Firm Orientations on Sustainable Supply Chain Management. Journal of Business Research, 69(9), 3406–3414. https://doi.org/10.1016/j.jbusres.2016.02.003
Martelo-Landroguez, S., Castro, C. B., & Cepeda‐Carrión, G. (2013). The Use of Organizational Capabilities to Increase Customer Value. Journal of Business Research, 66(10), 2042–2050. https://doi.org/10.1016/j.jbusres.2013.02.030
Mayer, M. J., & Yang, D. (2023). Calibration of Deterministic NWP Forecasts and Its Impact on Verification. International Journal of Forecasting, 39(2), 981–991. https://doi.org/10.1016/j.ijforecast.2022.03.008
Mostaghel, R., Oghazi, P., Patel, P. C., Parida, V., & Hultman, M. (2019). Marketing and Supply Chain Coordination and Intelligence Quality: A Product Innovation Performance Perspective. Journal of Business Research, 101, 597–606. https://doi.org/10.1016/j.jbusres.2019.02.058
Munir, A., Hussain, A., Farooq, M. S., Rehman, A. U., & Masood, T. (2024). Building Resilient Supply Chains: Empirical Evidence on the Contributions of Ambidexterity, Risk Management, and Analytics Capability. Technological Forecasting and Social Change, 200, 123146. https://doi.org/10.1016/j.techfore.2023.123146
Naccarato, A., Falorsi, S., Loriga, S., & Pierini, A. (2018). Combining Official and Google Trends Data to Forecast the Italian Youth Unemployment Rate. Technological Forecasting and Social Change, 130, 114–122. https://doi.org/10.1016/j.techfore.2017.11.022
Nava, L. (2022). Rise From Ashes: A Dynamic Framework of Organizational Learning and Resilience in Disaster Response. Business and Society Review, 127(S1), 299–318. https://doi.org/10.1111/basr.12261
Obeng, P., Dogbe, C. S. K., & Boahen, P. A. N. (2023). Nexus Between GHRM and Organizational Competitiveness: Role of Green Innovation and Organizational Learning of MNEs. Business and Society Review, 128(2), 275–303. https://doi.org/10.1111/basr.12310
Önkal, D., Lawrence, M., & Sayım, K. Z. (2011). Influence of Differentiated Roles on Group Forecasting Accuracy. International Journal of Forecasting, 27(1), 50–68. https://doi.org/10.1016/j.ijforecast.2010.03.001
Önkal, D., Sayım, K. Z., & Gönül, M. S. (2013). Scenarios as Channels of Forecast Advice. Technological Forecasting and Social Change, 80(4), 772–788. https://doi.org/10.1016/j.techfore.2012.08.015
Palacios, M., Martínez-Corral, A., Nisar, A., & Grijalvo, M. (2016). Crowdsourcing and Organizational Forms: Emerging Trends and Research Implications. Journal of Business Research, 69(5), 1834–1839. https://doi.org/10.1016/j.jbusres.2015.10.065
Paul, S. K., Chowdhury, P., Moktadir, Md. A., & Lau, K. H. (2021). Supply Chain Recovery Challenges in the Wake of COVID-19 Pandemic. Journal of Business Research, 136, 316–329. https://doi.org/10.1016/j.jbusres.2021.07.056
Petropoulos, F., Makridakis, S., & Stylianou, N. (2022). COVID-19: Forecasting Confirmed Cases and Deaths With a Simple Time Series Model. International Journal of Forecasting, 38(2), 439–452. https://doi.org/10.1016/j.ijforecast.2020.11.010
Pinson, P. (2022). Editorial: Epidemics and Forecasting With a Focus on COVID-19. International Journal of Forecasting, 38(2), 407–409. https://doi.org/10.1016/j.ijforecast.2021.12.009
Polanski, A., & Stoja, E. (2017). Forecasting Multidimensional Tail Risk at Short and Long Horizons. International Journal of Forecasting, 33(4), 958–969. https://doi.org/10.1016/j.ijforecast.2017.05.005
Qader, G., Junaid, M., Abbas, Q., & Mubarik, M. S. (2022). Industry 4.0 Enables Supply Chain Resilience and Supply Chain Performance. Technological Forecasting and Social Change, 185, 122026. https://doi.org/10.1016/j.techfore.2022.122026
Radziwon, A., & Bogers, M. (2019). Open Innovation in SMEs: Exploring Inter-Organizational Relationships in an Ecosystem. Technological Forecasting and Social Change, 146, 573–587. https://doi.org/10.1016/j.techfore.2018.04.021
Raj, A., Mukherjee, A. A., Ana Beatriz Lopes de Sousa Jabbour, & Srivastava, S. K. (2022). Supply Chain Management During and Post-Covid-19 Pandemic: Mitigation Strategies and Practical Lessons Learned. Journal of Business Research, 142, 1125–1139. https://doi.org/10.1016/j.jbusres.2022.01.037
Ray, E. L., Brooks, L., Bien, J., Biggerstaff, M., Bosse, N. I., Bracher, J., Cramer, E. Y., Funk, S., Gerding, A., Johansson, M. A., Rumack, A., Wang, Y., Zorn, M., Tibshirani, R. J., & Reich, N. G. (2023). Comparing Trained and Untrained Probabilistic Ensemble Forecasts of COVID-19 Cases and Deaths in the United States. International Journal of Forecasting, 39(3), 1366–1383. https://doi.org/10.1016/j.ijforecast.2022.06.005
Salas‐Molina, F., Martin, F. J., Rodríguez-Aguilar, J. A., Serrà, J., & Arcos, J. L. (2017). Empowering Cash Managers to Achieve Cost Savings by Improving Predictive Accuracy. International Journal of Forecasting, 33(2), 403–415. https://doi.org/10.1016/j.ijforecast.2016.11.002
Santos, D. (2018). Nowcasting and Forecasting Aquaponics by Google Trends in European Countries. Technological Forecasting and Social Change, 134, 178–185. https://doi.org/10.1016/j.techfore.2018.06.002
Sarpong, D., & Maclean, M. (2016). Cultivating Strategic Foresight in Practise: A Relational Perspective. Journal of Business Research, 69(8), 2812–2820. https://doi.org/10.1016/j.jbusres.2015.12.050
Shafique, M. N., Yeo, S. F., & Tan, C. L. (2024). Roles of Top Management Support and Compatibility in Big Data Predictive Analytics for Supply Chain Collaboration and Supply Chain Performance. Technological Forecasting and Social Change, 199, 123074. https://doi.org/10.1016/j.techfore.2023.123074
Shah, M. I., Foglia, M., Shahzad, U., & Fareed, Z. (2023). Corrigendum to ‘Green Innovation, Resource Price and Carbon Emission During the COVID-19 Times: New Findings From Wavelet Local Multiple Correlation Analysis’ [Technol. Forecast. Soc. Change, 184 (2022) 121957]. Technological Forecasting and Social Change, 191, 122394. https://doi.org/10.1016/j.techfore.2023.122394
Shoukohyar, S., & Seddigh, M. R. (2020). Uncovering the Dark and Bright Sides of Implementing Collaborative Forecasting Throughout Sustainable Supply Chains: An Exploratory Approach. Technological Forecasting and Social Change, 158, 120059. https://doi.org/10.1016/j.techfore.2020.120059
Taka, M. (2016). Emerging Practice in Responsible Supply Chain Management: Closed‐Pipe Supply Chain of Conflict‐Free Minerals From the Democratic Republic of Congo. Business and Society Review, 121(1), 37–57. https://doi.org/10.1111/basr.12080
Talay, C., Oxborrow, L., & Brindley, C. (2020). How Small Suppliers Deal With the Buyer Power in Asymmetric Relationships Within the Sustainable Fashion Supply Chain. Journal of Business Research, 117, 604–614. https://doi.org/10.1016/j.jbusres.2018.08.034
Taleb, N. N., Bar‐Yam, Y., & Cirillo, P. (2022). On Single Point Forecasts for Fat-Tailed Variables. International Journal of Forecasting, 38(2), 413–422. https://doi.org/10.1016/j.ijforecast.2020.08.008
Thomson, M. E., Pollock, A. C., Gönül, M. S., & Önkal, D. (2013). Effects of Trend Strength and Direction on Performance and Consistency in Judgmental Exchange Rate Forecasting. International Journal of Forecasting, 29(2), 337–353. https://doi.org/10.1016/j.ijforecast.2012.03.004
Town, S., Weber, J., & Nagy, N. (2022). Changes in Business Students’ Value Orientations After the COVID‐19 Outbreak: An Exploration. Business and Society Review, 127(S1), 253–282. https://doi.org/10.1111/basr.12257
Verma, S., & Gustafsson, A. (2020). Investigating the Emerging COVID-19 Research Trends in the Field of Business and Management: A Bibliometric Analysis Approach. Journal of Business Research, 118, 253–261. https://doi.org/10.1016/j.jbusres.2020.06.057
Yu, L., Zhao, Y., Tang, L., & Yang, Z. (2019). Online Big Data-Driven Oil Consumption Forecasting With Google Trends. International Journal of Forecasting, 35(1), 213–223. https://doi.org/10.1016/j.ijforecast.2017.11.005
Yuan, X., & Cai, Y. (2021). Forecasting the Development Trend of Low Emission Vehicle Technologies: Based on Patent Data. Technological Forecasting and Social Change, 166, 120651. https://doi.org/10.1016/j.techfore.2021.120651
Zheng, W., Yang, B., & McLean, G. N. (2010). Linking Organizational Culture, Structure, Strategy, and Organizational Effectiveness: Mediating Role of Knowledge Management. Journal of Business Research, 63(7), 763–771. https://doi.org/10.1016/j.jbusres.2009.06.005

