Operations Management Essay Example

Published: 2018-02-01
Operations Management Essay Example
Type of paper:  Essay
Categories:  Management Strategy Productivity
Pages: 4
Wordcount: 1034 words
9 min read


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Metrics at the operation manager’s level measures the accurate operational data and focus on the day-to-day business. As an operation manager, a lot of planning, managing, controlling and coordination are needed at its level; a successful operation runs on its productive and engaged workforce. A manager can make the positive impact on the success of every operation when they possess the ability to get to know what is going on in the organization (Das, 2015). This is through accurately focusing on labor needs, measuring team performance and every individual employee, motivating leaders to spend more time working to drive change and ensuring corporate social responsibility. Additionally, for employees to be managed effectively, operation managers are supposed to generate appropriate performance data which are objective to know which factors and staff are having issues and which employees are performing well. Metrics are made to empower operation managers through making them achieve real-time feedback on the performance of customers and staff.

Key metrics

Some of the critical parameters for operation managers include customer satisfaction score, productivity score, employee satisfaction and financial metrics. Focusing on customer satisfaction score, many metrics exist to perform the general measure of customer satisfaction score; some of the actions include customer satisfaction, the voice of clients, the net promoter scores and corporate social responsibility performance where the organization ensures recycling of their final products from customers and developing customers through equipping them with social amenities. Customer satisfaction scores as a metrics are in place to communicate performance and control the production of resources used to satisfy customers. Additionally, it helps in identifying gaps between performance and expectation hence assist in making the adjustment when it comes to fulfilling the difference in satisfying customers (Roth, 2008). Customer satisfaction score supports the overall financial performance of an organization, in that, every team goals are to meet and satisfy organizational goals. Customer satisfaction score, on the other hand, ensures improvement on how customers are handled and even their level of need satisfaction also improves, hence attracting more clients and even ensure that need of existing customers are met, and an organization which attracts and maintain clients experience increased financial performance. The environment in which consumers are living is also protected through effective implementation of recycling methods for their final product.

The data that will be used to support customer satisfaction score will be collected through face to face data collection, postal means, telephone and online means of data collection, the level of score experienced in every collection method will provide a reflection of customer satisfaction and their views on the performance, to ensure the quality of data achieved, the sample size will be reasonable and making sure that specific actions are taken in response to customer satisfaction. Additionally, the level of participation in data collection will be increased and going an extra mile of setting interviewing quotas to ensure that consumers view accurately represented. Data analytics support customer satisfaction score through factors like repeat purchase intention, overall customer satisfaction, likelihood to recommend to friends and affective and cognitive satisfaction.

Notably, productivity as a metric offer support to the general financial performance of the organization by measuring the amount of return on each investment is identified. Specific goals generated by managers are measured regarding sales to identify whether the business performance is more than the marketing expenses and the sales. Productivity as a metric act as leverage in measuring the general financial performance of an organization through analyzing marketing and sales expenses to identify business productivity (Wilson & Hill, 2013). Additionally, productivity ensures reduced pollution to the environment and ensuring that the environment, in which the organization is operating, is well kept and preserved. Some of the data that will be used to support productivity as a metric are the customer ratings, statistics on sales performance, labor productivity, equivalent units, asset productivity, environment preservation and total people productivity. The data supporting productivity will be of sufficient quality through making sure that verification is made and the right data is collected to support the first statistics. Data analysis can help productivity metrics since it cleans inspects, remodel and transform data with the view of determining the level of expenses, general performance and productivity level (Anderson, Parker & Anderson, 2013).

The third metrics is the financial metrics which is recognized as the lifeblood of any business and the most significant aspect of performance management. Technically, the metrics are the driver to the financial results of any organization it decides on the budget and the general spending. Through efficient management of financial goals of any organization, success is inevitable. Some of the data that will be essential in support to financial metrics include hourly labor burden rate which includes wages or salaries, cost of employee benefits, overtime, a cost of reverse logistics, recycling and pollution control, and cost of applicable task (Slack & Lewis, 2005). The additional data involves gross margin which is the percentage of the overall service revenue which is retained by the service organization. Financial data are not theoretical but are based on mathematical analysis, through this confirmation from the original financial document and complete verification on their validity should be done to ensure the data are of sufficient quality. Data analytic support the metrics, through providing the comparison between different investments and actions, making a justification for a chosen action on financial terms and making anticipation on the economic consequences in future.


In conclusion, the three major metrics that determine the general performance of the organization include customer satisfaction score, productivity metrics, and financial metrics. All the parameters are essential for operation level managers when it comes to managing general organization productivity, employee development, monitoring corporate responsibility performance and preservation of the existing environment in which the company is operating in.


Anderson, M. A., Parker, G., & Anderson, E. (2013). Operations Management For Dummies. John Wiley & Sons.

Das, A. (2015). An Introduction to Operations Management: The Joy of Operations. Routledge.

Roth, A. V. (2008). Handbook of Metrics for Research in Operations Management: Multi-item Measurement Scales and Objective Items. SAGE.

Slack, N., & Lewis, M. (2005). Operations Management. Psychology Press.

Wilson, R., & Hill, A. V. (2013). The Operations Management Complete Toolbox (Collection). FT Press.

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