Financial modeling trends for production companies in the context of Industry 4.0
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DOIhttp://dx.doi.org/10.21511/imfi.18(1).2021.23
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Article InfoVolume 18 2021, Issue #1, pp. 270-284
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Over the years, technological progress has accelerated highly, and the speed, flexibility, human error reduction, and the ability to manage the process in real time have become more critical and required production companies to adapt production and business models according to the needs. The demand for real-time decision support systems adapted to these raising business needs is continuously growing. Nevertheless, businesses usually face challenges in identifying new indicators, data sources, and appropriate financial modeling methods to analyze them. This paper aims to define and summarize the main financial/economic forecasting methods for production companies in the context of Industry 4.0. Main findings show forecasting accuracy of up to 96% when combining economic and demand information, optimal forecasting period from 10 months to five years, more frequent use of soft indicators in forecasting, the relationship between company’s size and production planning. Four groups of indicators used in financial modeling, such as (I) production-related, (II) customers’ and demand-oriented, (III) industry-specific, and (IV) media information indicators, were separated. The analysis forms a suggestion for decision-makers to pay more attention to the forecasting object identification, indicators’ selection peculiarities, data collection possibilities, and the choice of appropriate methods of financial modeling.
Acknowledgment
This work was partly supported by Project No. 0121U100470 “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy”.
- Keywords
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JEL Classification (Paper profile tab)C53, E27, M11, O14
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References55
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Tables0
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Figures2
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- Figure A1. The most relevant literature analysis findings
- Figure A2. Industry 4.0 financial/economic forecasting models and indicators by analyzed authors
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- Agostini, L., & Filippini, R. (2019). Organizational and managerial challenges in the path toward Industry 4.0. European Journal of Innovation Management, 22(3), 406-421.
- Aldarrat, H., Cogum, B. Ö., & Reupke, L. (2018). Minimizing AAC production hard waste costs: A hybrid IMSD and DfE approach in machine-building industry. The Online Collection for Conference papers in Civil Engineering, 2(4), 163-169.
- Amornpetchkul, T. (B.), Duenyas, I., & Şahin, Ö. (2015). Mechanisms to Induce Buyer Forecasting: Do Suppliers Always Benefit from Better Forecasting? Production and Operations Management, 24(11), 1724-1749.
- Banbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2013). Now-Casting and the Real-Time Data Flow. Handbook of Economic Forecasting, 2A(4), 195-237.
- Basl, J. (2017). Pilot study of readiness of Czech companies to implement the principles of Industry 4.0. Management and Production Engineering Review, 8(2), 3-8.
- Bassi, L. (2017). Industry 4.0: hope, hype or revolution? 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), IEEE (pp. 1-6).
- Blackburn, R., Lurz, K., Priese, B., Göb, R., & Darkow, I.-L. (2014). A predictive analytics approach for demand forecasting in the process industry. International Transactions in Operational Research, 22(3), 407-428.
- Boero, G., & Lampis, F. (2016). The Forecasting Performance of Setar Models: an Empirical Application. Bulletin of Economic Research, 69(3), 216-228.
- Boone, T., Ganeshan, R., Hicks, R. L., & Sanders, N. R. (2017). Can Google Trends Improve Your Sales Forecast? Production and Operations Management, 27(10), 1770-1774.
- Bulligan G, Golinelli R, & Parigi G. (2010). Forecasting Monthly Industrial Production in Real-time: From Single Equations to Factor- based Models. Empirical Economics, 36, 303-336.
- Calatayud, A. (2017). The connected supply chain: enhancing risk management in a changing world (Discussion Paper No. 508). Inter-American Development Bank, Washington, DC.
- Calatayud, A., Mangan, J., & Christopher, M. (2019). The self-thinking supply chain. Supply Chain Management: An International Journal, 24(1), 22-38,
- Camacho, M., & Garcia-Serrado, A. (2014). The Euro-Sting Revisited: The Usefulness of Financial Indicators to Obtain Euro Area GDP Forecasts. Journal of Forecasting, 33(3), 186-197.
- Cannella, S., López-Campos, M., Dominguez, R., Ashayeri, J., & Miranda, P. A. (2015). A simulation model of a coordinated decentralized supply chain. International Transactions in Operational Research, 22(4), 735-756.
- Chen, C. W. S., Gerlach, R., Lin, E. M. H., & Lee, W. C. W. (2011). Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis. Journal of Forecasting, 31(8), 661-687.
- Contino, C., & Gerlach, R. H. (2017). Bayesian tail-risk forecasting using realized GARCH. Applied Stochastic Models in Business and Industry, 33(2), 213-236.
- Cui, R., Gallino, S., Moreno, A., & Zhang, D. J. (2018). The Operational Value of Social Media Information. Production and Operations Management, 27(10), 1749-1769.
- Danese, P., & Kalchschmidt, M. (2011). The role of the forecasting process in improving forecast accuracy and operational performance. International Journal of Production Economics, 131(1), 204-214.
- Doszyń, M. (2019). Intermittent demand forecasting in the Enterprise: Empirical verification. Journal of Forecasting, 38(5), 459-469.
- Eurostat. (2019a). Glossary: Production Index.
- Eurostat. (2019b). Economic sentiment indicator.
- Eurostat. (2008). NACE Rev. 2. Statistical classification of economic activities in the European Community.
- Farooq, U., & Qamar M. A. J. (2019). Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria (pp. 1-17). John Wiley & Sons, Ltd Journal of Forecasting.
- Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910-93.
- Girardi, A., Guardabascio, B., & Ventura, M. (2016). Factor-Augmented Bridge Models (FABM) and Soft Indicators to Forecast Italian Industrial Production. Journal of Forecasting, 35(6), 542-552.
- Gólcher-Barguil, L. A., Nadeem, S. P., & Garza-Reyes, J. A. (2019). Measuring operational excellence: an operational excellence profitability (OEP) approach. Production Planning & Control, 30(8).
- Hassani, H., Webster, A., Silva, E. S., & Heravi, S. (2015). Forecasting U.S. tourist arrivals using optimal singular spectrum analysis. Tourism Management, 46, 322-335.
- Heinisch, K., & Scheufele, R. (2018). Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence. German Economic Review.
- Hozdić, E. (2015). Smart factory for Industry 4.0: a review. International Journal of Modern Manufacturing Technologies, 7(1), 28-35.
- Lau, R. Y. K., K., Zhang, W., & Xu, W. (2017). Parallel Aspect-Oriented Sentiment Analysis for Sales Forecasting with Big Data. Production and Operations Management, 27(10), 1775-1794.
- Lee, W.-I., Shih, B.-Y., & Chen, C.-Y. (2012). Retracted: A hybrid artificial intelligence sales-forecasting system in the convenience store industry. Human Factors and Ergonomics in Manufacturing & Service Industries, 22(3), 188-196.
- Li, L. (2017). China’s manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”. Technological Forecasting & Social Change, 135, 66-74.
- Markit Economics. (2017). Interpreting PMI data.
- Melnyk, L., Dehtyarova, I., Kubatko, O., Karintseva, O., & Derykolenko, A. (2019a). Disruptive technologies for the transition of digital economies towards sustainability. Economic Annals-XXI, 179(9-10), 22-30.
- Melnyk, L., Kubatko, O., Dehtyarova, I., Matsenko, O., & Rozhko O. (2019b). The effect of industrial revolutions on the transformation of social and economic systems. Problems and Perspectives in Management, 17(4), 381-391.
- McLemore, P. (2018). Industry Costs of Equity: Incorporating Prior Information. The Financial Review, 53(1), 153-183.
- Nascimento, D. L. M.; Alencastro, V., Quelhas, O. L. G., Rodrigo Goyannes Gusmão Caiado, R. G. G., Garza-Reyes, J. A., Lona, L. R., &Tortorella, G. (2018). Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context: A business model proposal. Journal of Manufacturing Technology Management, 30(3), 607-627.
- Osadchiy, N., Gaur, V., & Seshadri, S. (2013). Sales Forecasting with Financial Indicators and Experts’ Input. Production and Operations Management, 22(5), 1056-1076.
- Prüser, J. (2019). Forecasting with many predictors using Bayesian additive regression trees. Journal of Forecasting, 38(7), 621-631.
- Reis, M. S., & Kenett, R. (2018). Assessing the value of information of data-centric activities in the chemical processing industry 4.0. Process Systems Engineering, 64(11), 3868-3881.
- Sanders, A., Elangeswaran, C., & Wulfsberg, J. P. (2016). Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management (JIEM), 9(3), 811-833.
- Schröder, D., & Yim, A. (2017). Industry Effects in Firm and Segment Profitability Forecasting. Contemporary Accounting Research, 35(4), 2106-21306.
- Shen, H., Xia, N., & Zhang, J. (2018). Customer-based Concentration and Firm Innovation. Asia-Pacific Journal of Financial Studies, 47(2), 248-279.
- Sikorski, J. J., Haughton, J., & Kraft, M. (2017). Blockchain technology in the chemical industry: machine-to-machine electricity market. Applied Energy, 195(1), 234-246.
- Silva, E. S., Hassani, H., & Heravi, S. (2018). Modeling European industrial production with multivariate singular spectrum analysis: A cross-industry analysis. Journal of Forecasting, 37(3), 371-384.
- Simon, J. P. (2019). Artificial intelligence: scope, players, markets and geography. Digital Policy, Regulation and Governance, 21(3), 208-237.
- Sony, M.; Naik, S. (2019). Key ingredients for evaluating Industry 4.0 readiness for organizations: a literature review. Benchmarking: An International Journal, 27(7), 2213-2232.
- Tsarouhas, P. H., & Arvanitoyannis, I. S. (2012). Reliability and maintainability analysis to improve the operation of the limoncello production line. International Journal of Food and Science + Technology, 47(8), 1669-1675.
- Ulbricht, D., Kholodilin, K. A., & Thomas, T. (2016). Do Media Data Help to Predict German Industrial Production? Journal of Forecasting, 36(5), 483-496.
- Valdez, A. C., Brauner, P., Schaar, A. K., Holzinger, A., & Zieflea, M. (2015). Reducing complexity with simplicity-Usability Methods for Industry 4.0. Proceedings 19th Triennial Congress of the IEA.Melbourne, Australia, RWTH Publications, Germany, 9-14.
- Van der Maas, J. (2014). Forecasting inflation using time-varying Bayesian model averaging. Statistica Neerlandica, 68(3), 149-182.
- Vereecke, A., Vanderheyden, K., Baecke, P., & Van Steendam, T. (2018). Mind the gap – Assessing maturity of demand planning, a cornerstone of S&OP. International Journal of Operations & Production Management, 38(8),1618-1639.
- Wan, J., Cai, H., & Zhou, K. (2015). Industry 4.0: Enabling technologies. International Conference on Intelligent Computing and Internet of Things (ICIT) (pp. 135-140). IEEE, Harbin, China.
- Woo, J., & Owen, A. L. (2018). Forecasting private consumption with Google Trends data. Journal of Forecasting, 38(2), 81-91.
- Zou, Z., Pan, J., Xin, X., Yang, J., Chen, X., Jiang, Y., & Zhang, X. (2018). P-6.9: Big Data and Cloud Service – A Mask Intelligent Handling System. Society for Information Display International Conference on Display Technology (ICDT 2018), 49(S1), 623-624.