Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

Capacity Buffering and Demand Uncertainty of Manufacturing Firms in Rivers State, Nigeria

Christian, Julian Chinyere and, Okwu, Oroma, Wilson, Ebitimi Florence

Abstract

This study examined the relationship between capacity buffering and demand uncertainty of manufacturing firms in Rivers State, Nigeria. The measures of demand uncertainty used are demand forecasting accuracy and inventory turnover. The study adopted a cross-sectional survey research design. The target population was the 34 manufacturing firms registered with the Manufacturing Association of Nigeria, as obtained from the 2023 updated Directory of Rivers State zone of the association. However, the study elements were 80 which comprised managers from the production, marketing and operations departments of the respective 34 manufacturing firms. Data for the study was collected through structured questionnaire. The five-point Likert scale was used to measure the responses from the respondents. Data was analyzed using mean and standard deviations with charts for the primary analysis of the study variables, while inferential statistics such as the Spearman Rank Order Correlation Coefficient was used to test the hypotheses. The results of the study showed that there is moderate positive relationship between capacity buffering and demand forecasting accuracy. The study also revealed a strong positive relationship between capacity buffering and inventory turnover. The study concludes that capacity buffering has a significant relationship with demand forecasting accuracy and inventory turnover of manufacturing firms in Rivers State, Nigeria. The study therefore recommends that manufacturing firms should integrate capacity buffering strategies such as overtime shifts, flexible labor and machine redundancy into their demand forecasting models. Again, this alignment enables firms to better accommodate fluctuations in customer demand, thereby improving the accuracy and reliability of forecasting results.

Keywords

CapacityInventory TurnoverDemand Forecasting Accuracy

References

Adebayo, O., & Adebola, T. (2021). "Capacity Management Strategies in Nigerian Manufacturing Firms: A Review of Best Practices." African Journal of Business and Economic Research, 17(3), 45-63. Adhikari, N. C. D., Domakonda, N., Chandan, C., Gupta, G., Garg, R., Teja, S., … Misra, A. (2019). An intelligent approach to demand forecasting. In International Conference on Computer Networks and Communication Technologies (pp. 167–183). Singapore: Springer Singapore. Akinyemi, B. A., & Awolusi, O. D. (2020). Capacity Planning and Buffering Strategies for Effective Production Scheduling in Nigeria’s Manufacturing Sector. African Journal of Management Studies, 6(1), 37–54. Akpoviroro, K. S., & Vareckova, L. (2023). Correlate of inventory management and organizational performance. Economic and Managerial Spectrum, 17(1), 1–13. doi:10.26552/ems.2023.1.1-13 Aktepe, A., Yan?k, E., & Ersöz, S. (2021). Demand forecasting application with regression and artificial intelligence methods in a construction machinery company. Journal of Intelligent Manufacturing, 32(6), 1587–1604. Ali, K., Showkat, N., & Chisti, K. A. (2022). Impact of inventory management on Operating Profits: Evidence from India. Journal of Economics Management and Trade, 22–26. doi:10.9734/jemt/2022/v28i930435 Alnaim, M., & Kouaib, A. (2023). Inventory turnover and firm profitability: A Saudi Arabian investigation. Processes (Basel, Switzerland), 11(3), 716. doi:10.3390/pr11030716 Bagshaw, K. B. (2014). Assessing the application of production scheduling: Demand uncertainty and the performance of manufacturing firms in Rivers State, Nigeria. International Review of Management and Business Research, 3, Retrieved from https://www.semanticscholar.org/paper/81cc1086092796282428a57d7d7e469305f84116 Bourgeois, L. J., & Eisenhardt, K. M. (2020). "Strategic Decision Making in High-Velocity Environments: The Role of Capacity Flexibility." Academy of Management Review, 45(2), 311-328. Cheng, L., Podolsky, M., & Jarvis, R. (2012). Manufacturing flexibility and firm performance: The moderating role of demand uncertainty. International Journal of Operations & Production Management, 32(3), 295–318. https://doi.org/10.1108/01443571211212564 Chopra, S., & Meindl, P. (2020). Supply Chain Management: Strategy, Planning, and Operation (7th ed.). Pearson. Christopher, M., & Holweg, M. (2017). "Supply Chain 4.0: Managing Supply Chains in the Digital Era." Logistics & Transport Focus, 19(4), 24-27. Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275 Dou, Z., Sun, Y., Zhang, Y., Wang, T., Wu, C., & Fan, S. (2021). Regional manufacturing industry demand forecasting: A deep learning approach. Applied Sciences (Basel, Switzerland), 11(13), 6199. doi:10.3390/app11136199 Fisher, M. L., Gallino, S., & Li, J. (2021). "Retail Operations in the Era of Omnichannel and Big Data Analytics." Manufacturing & Service Operations Management, 23(3), 509-523. Gaur, V., Fisher, M. L., & Raman, A. (2005). An econometric analysis of inventory turnover performance in retail services. Management Science, 51(2), 181–194. https://doi.org/10.1287/mnsc.1040.0292 Goldratt, E. M., & Cox, J. (2016). The Goal: A Process of Ongoing Improvement (4th ed.). North River Press. Gonçalves, J. N. C., Cortez, P., Carvalho, M. S., & Frazão, N. M. (2020). A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain. Decision Support Systems, 142(113452), 113452. Gunessee, S., & Subramanian, N. (2015). Impact of operational performance on demand forecasting effectiveness. Production Planning & Control, 26(6), 469–482. https://doi.org/10.1080/09537287.2014.927144 Hopp, W. J., & Spearman, M. L. (2021). Factory Physics (4th ed.). Waveland Press. Jayawardane, K., Musthaffa, S., & Dias, M. (2022). Impact of lean manufacturing on inventory turnover performances: Evidence from the Sri Lankan apparel industry. 2022 Moratuwa Engineering Research Conference (MERCon), 1–6. IEEE. Ketokivi, M., & Jokinen, M. (2020). "Managing Manufacturing Flexibility: The Role of Capacity Buffers in Uncertain Demand Environments." Journal of Operations Management, 66(2), 89-103. Koumanakos, D. P. (2008). The effect of inventory management on firm performance. International Journal of Productivity and Performance Management, 57(5), 355–369. https://doi.org/10.1108/17410400810881827 Kwak, J. K. (2019). Analysis of inventory turnover as a performance measure in manufacturing industry. Processes (Basel, Switzerland), 7(10), 760. doi:10.3390/pr7100760 Lee, H. L., & Whang, S. (2019). "Decentralized Supply Chain Coordination through Capacity Buffering and Demand Forecasting." Management Science, 65(7), 3174-3189. Nwokoro, O. E., & Chukwuemeka, O. J. (2021). Capacity Buffering and Forecasting Accuracy in Nigerian Manufacturing SMEs. Journal of African Operations Management, 5(2), 45–58. Obasi, I. K., & Okeke, J. O. (2022). Capacity Buffering and Inventory Management Performance of Manufacturing Firms in Nigeria. Journal of Operations and Supply Chain Research, 8(1), 15–29. Okwu, O., Bagshaw, K. B. & Wilson, F. E. (2024). Customer focus and operational performance of manufacturing firms in Rivers State, Nigeria. Nigerian Journal of Managament Sciences Research, 4(1), 9-18 Olhager, J., & Rudberg, M. (2002). Linking manufacturing strategy decisions on process choice with manufacturing planning and control systems. International Journal of Production Research, 40(10), 2335–2351. https://doi.org/10.1080/00207540210133452 Podile, V., Sivasree, C. H. V., & Shanmugam, S. (2020). Inventory turnover in Engineering Micro and Small Enterprises. Solid State Technology, 532–537. Retrieved from https://www.semanticscholar.org/paper/19287e9ff1d23ebfade154453cd20a1327b77495 Rahman, M. A., & Hossain, S. (2019). Impact of Time Buffering on Production Scheduling Efficiency in Bangladesh’s Textile Sector. Production Planning & Control, 45(1), 112-130. Rosienkiewicz, M. (2021). Artificial intelligence-based hybrid forecasting models for manufacturing systems. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 23(2), 263–277. doi:10.17531/ein.2021.2.6 Singh, R. K., & Garg, S. K. (2009). Supply chain management in SMEs: A case study. International Journal of Manufacturing Research, 4(1), 59–76. https://doi.org/10.1504/IJMR.2009.022754 Slack, N., Brandon-Jones, A., & Johnston, R. (2019). Operations Management (9th ed.). Pearson. Sun, H., Wang, C., & Li, S. (2016). Capacity strategies and forecasting accuracy: Evidence from semiconductor industry. Journal of Manufacturing Systems, 41, 134–142. https://doi.org/10.1016/j.jmsy.2016.09.008 Syntetos, A. A., Babai, M. Z., & Boylan, J. E. (2010). On the appropriateness of stock-keeping- oriented and customer-oriented forecasting performance measures. Supply Chain Forum: An International Journal, 11(2), 24–33. https://doi.org/10.1080/16258312.2010.11517233 Tang, C. S., & Tomlin, B. (2022). "The Power of Flexibility: Enhancing Supply Chain Resilience with Capacity Management." Production and Operations Management, 31(5), 1322-1337. Torrico, B. C. H., & Oyola, S. A. (2021). A case study of inventory management system for an international lifestyle product retailer in Bolivia. Proceedings of the International Conference on Industrial Engineering and Operations Management. Michigan, USA: IEOM Society International. Volling, T., & Spengler, T. S. (2011). Modeling demand uncertainty and capacity flexibility in make-to-order production systems. International Journal of P

More Articles from INTERNATIONAL JOURNAL OF SOCIAL SCIENCES AND MANAGEMENT RESEARCH

Building National Analytics Capacity: Advances and Future Pathways

Author: Uchechi Mary-Linda Unamma, Ifeanyichukwu Jeffrey Okwesa, Uzoamaka Iwuanyanwu

Root-Cause and Thematic Analysis for Major Incident Management: A Review

Author: Ifeanyichukwu Jeffrey Okwesa, Uchechi Mary-Linda Unamma, Uzoamaka Iwuanyanwu

Data-Quality and Single-Source-Of-Truth Frameworks for Inter- Agency Reporting: A Review

Author: Uchechi Mary-Linda Unamma, Ifeanyichukwu Jeffrey Okwesa, Uzoamaka Iwuanyanwu

Human-In-The-Loop Decision Systems: Advances and Future Directions

Author: Funmilayo Ashore-Onisemo, Uchechi Mary-Linda Unamma, Ifeanyichukwu Jeffrey, Okwesa,

Roles of Oil Subsidy Removal on Transportation Cost and Water Factory in Cross River South, Nigeria

Author: Onwuzurike Peter Tobechi, Owoh Akwa Owoh, Unoh Grace Inyang, Umaru Musa