Forecasting and Inventory Management

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Introduction

Every business organization aims at maximizing profits while functioning at minimal cost and promoting business sustainability. Operations management is a unique business management component that entails the administration of strategic business functions to create the highest efficiency standards within an organization (Russell & Taylor, 2019). It focuses on converting labor and materials into services and goods with high efficiency, to maximize profits. Inventory management and forecasting are crucial operations management elements (Goltsos et al., 2019). The latter entails applying or deploying scientific techniques and managerial-intuition to ascertain or predict prospective business needs (Agostino et al., 2020). On the other hand, inventory management involves storing, ordering, and utilizing an entity’s stockpile – both finished products and raw materials (Fattah et al., 2016). This paper provides a comprehensive evaluation of the purpose of inventory management and forecasting within operations management.

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Forecasting: Regression Methods

Forecasting plays a fundamental role in operations management since it supports an organization’s decision-making process. In practice, forecasting employs scientific methods to derive predictions for a business entity’s future needs. One of the significant forecasting approaches in operations management is regression analysis (Russell & Taylor, 2019). This technique involves examining the relationship between two unique variables; the independent and dependent variables. The regression model helps operations managers determine the critical factors and the least essential elements in a system while effectively demonstrating their interrelationships (Agostino et al., 2020). The above-mentioned forecasting procedure enhances the attainment of efficient and cost-effective production processes that boost profitability (Galtsos et al., 2019). In essence, this approach employs data analysis methods to project variables deemed crucial in maximizing productivity and the lucrativeness while limiting the focus on the non-essential ones.

The logistic regression methods are tremendous tools for binary categorization. This statistical forecasting approach can be applied in the healthcare setting to classify a patient’s likelihood of contracting diseases such as cancer due to the environmental variables such as smoking habits. It provides healthcare institutions with the opportunity of targeting at-risk patients who need a more tailored behavioral care plan to help improve their day-to-day health habits (Galtsos et al., 2019). This, in turn, promotes better health for clients and minimal hospital costs. The above-mentioned methodology can also be used by assisted living professionals when conducting medical research motivated by the increasing interest in managing public expenditure to foster the adoption of cost-effective treatments or interventions. It promotes these experts’ capacity to analyze general and clinical healthcare-related costs as well as their determinants.

Inventory Management: Economic Order Quantity Models (EOQ)

Inventory management plays a critical role in supply-chain regulation by maintaining the balanced patterns of supply and demand. The above-mentioned approach is instrumental in overseeing the control of finished products, components, and raw materials. It integrates other procedures, such as processing and warehousing (Russell & Taylor, 2019). Furthermore, this strategy supports operations management by limiting wastage and resource misappropriation to promote efficiency and maximize profits. The risks associated with inventory shortages and gluts are typically common for business organizations that handle complex manufacturing units and vast supply chains (Fattah et al., 2016). To address this issue effectively, operations managers within production units often consider high-level inventory management techniques such as Materials-Resource-Planning (MRP) and Just-In-Time (JIT).

Economic order quality, typically abbreviated as EOQ is a measurement commonly used in supply, logistics, and operations management. The above-mentioned tool refers to an inventory management tool used to ascertain the frequency and volume of orders required to meet a specific demand level while reducing the cost per order (Kazemi et al., 2018). It is a set point intended to help organizations minimize the expenses incurred when holding and commissioning inventory. Inventory and material management are crucial functions that enhance a healthcare facility’s proper functioning (Kazemi et al., 2018). To ensure the effective inventory control within the healthcare setting, I would use the EOQ technique to facilitate the adequate reservation of supplies considered essential in conducting day-to-day business operations. The aforementioned approach would also be applied to avoid excessive stocking, which increases maintenance and storage costs and minimize the likelihood of supplies shortages, which could impact treatment, delay medical procedures, and trigger medical errors. The EOQ methodology could also be applied to resolve issues linked to supplier lead time.

The Importance of Forecasting and Inventory Management in Operations Management

Forecasting facilitates decision-making and planning process within an organization’s operations management framework. Projections in financial planning and strategic management help realize efficiency in operations while maximizing profitability through reduced costs (Russell & Taylor, 2019). Operations managers find forecasting approaches such as regression models useful for predicting prospective variables in production and other business functions. In regards to operations management, inventory control facilitates the efficient storage, ordering, and utilization of an entity’s stockpile (Russell & Taylor, 2019). The aforementioned approach helps limit wastage and resources misappropriation to promote efficiency and maximize profits. Inventory management serves a fundamental role in supply-chain regulation by ensuring the appropriate maintenance of reasonable supply and demand cycles (Fattah et al., 2016). These two techniques facilitate operations management by encouraging a significant increase in profits and considerable cost reductions; they also promote an entity’s sustainability.

Conclusion

Operations management represents a critical business management segment that focuses on the administration of particular business functions to establish the highest standards of efficiency achievable within an organization. In a practical scenario, forecasting employs scientific methods to draw predictions needs of business organizations. Regression models help operations-managers effectively ascertain the crucial factors and the least essential components in a business operation. Inventory management facilitates the operations management in multiple ways by reducing wastage and poor allocation of business resources to boost efficiency and optimize profits.

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References

Agostino, I. R. S., Vieira da Silva, W., Pereira da Veiga, C., & Souza, A. M., (2020). Forecasting models in the manufacturing processes and operations management: Systematic literature review. Journal of Forecasting, 39(7), 1043-1056. Web.

Fattah, J., Ezzine, L., Moussami, H. E., & Lachhab, A. (2016). Analysis of the performance of inventory management systems using the SCOR model and batch deterministic and stochastic petri nets. International Journal of Engineering Business Management, 8, 1–11. Web.

Goltsos, T. E., Syntetos, A. A., & Van der Laan, E., (2019). Forecasting for remanufacturing: The effects of serialization. Journal of Operations Management, 65(5), 447-467. Web.

Kazemi, N., Abdul-Rashid, S. H., Ghazilla, R. A. R., Shekarian, E., Zanoni, S. (2018). Economic order quantity models for items with imperfect quality and emission considerations. International Journal of Systems Science: Operations & Logistics, 5(2), 99–115. Web.

Russell, R. S., & Taylor, B. W. (2019). Operations and supply chain management. John Wiley & Sons.

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