Metrics are used by operational managers to assess the performance, compare results, and track the required data to adjust operations to improve an organization's overall financial performance. One crucial metric is the organization’s product. The operations manager should ensure products delivered to customers are of high quality and are sold at fair prices. This can be tracked by periodically taking reviews and insights from their customers.
One important metric is the retail order status that analyzes whether customers ‘orders have been shipped, received, or canceled. Such is vital as it helps firms avoid customers' potential loss due to dissatisfaction caused by delivery complaints (Perez et al., 2019). As more orders are received and delivered, the organizational revenue increases. Another important metric is the organizational sales in particular regions. Sales records in different regions are used to determine high and low performing regions. Operations managers will increase marketing in poor-performing regions and boost sales, leading to increased organizational revenue. Another important metric is the organizational overall transportation cost (Perez et al., 2019). Data from the logistics manager are crucial in assessing all distributions done by the organization. Such will help the operations manager assess how best to reduce transportation costs and ensure a high-quality delivery process.
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Employees’ performance is another critical metric for operations managers, ensuring that employees work at optimum levels. The use of human resource software can be used to determine how long they take to complete assigned tasks and whether their productivity is efficient enough for the organization (Kozjek et al., 2019). Data analytics plays a crucial role in operations management. It is more efficient due to automation, therefore coming up with high quality and reliable data needed for the organizational metrics (Misic & Perakis, 2019). It provides accurate solutions on how to tackle any challenges that may arise in the given metrics.
References
Perez, A., Gheriss, F., & Bedford, D. (2019). Metrics in business and knowledge management. Designing and Tracking Knowledge Management Metrics , 3-19. https://doi.org/10.1108/978-1-78973-723-320191003
Kozjek, D., Vrabič, R., Rihtaršič, B., & Butala, P. (2018). Big data analytics for operations management in engineer-to-order manufacturing. Procedia CIRP , 72 , 209-214. https://doi.org/10.1016/j.procir.2018.03.098
Misic, V., & Perakis, G. (2019). Data analytics in operations management: A review. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3381324
