Introduction
Maduro Cleaning is a small company that provides cleaning services for homes and businesses. Each morning, the owner assigns crews of two, three, or four workers to different jobs but lacks a reliable way to estimate how long each job should take. To improve planning, the owner began recording crew sizes and job times to measure productivity.
Average productivity per crew:
- 2 workers: 3,765 square meters per day
- 3 workers: 4,915 square meters per day
- 4 workers: 6,309 square meters per day.
This paper presents an analysis of the productivity of Maduro Cleaning, aimed at assessing labor input in terms of the number of employees and output quality in terms of the number of square meters cleaned. Operations management plays a crucial role in this case, as resource allocation for the director of Maduro Cleaning is a key determinant of the company’s competitiveness and potential for further growth (Reid & Sanders, 2019). Although this work evaluates only one input parameter of resources, it can serve as the basis for assessing productivity in a comprehensive implementation of other indicators, such as time, consumables, and many more. Consequently, this work will consider potential further developments in this area to more accurately assess productivity.
Productivity Calculation
The Maduro Cleaning company provides cleaning services, where the cost factors include cleaning products, the number of people, the time spent, and the amount of work done in the area. Accounting for inputs and outputs of services is more complex than for goods because the work requires both quantitative and, in some cases, qualitative measures to monitor the effectiveness of the resources spent (Diwas, 2020). Creating a multifactor calculation in this problem is impossible since there is no data on the consumption of cleaning products, for example (Stevenson, 2021). The work considers only one factor: the number of people in the team on the day the services are provided.
Therefore, to calculate productivity for each team of different numbers of people, it is necessary to resort to the formula:
In this case, for 2, 3, and 4 people in a team for one day, the calculations will take the following form:
Therefore, from these calculations, it is clear that the fewer people involved in the cleaning team, the higher their productivity. However, it should be noted that this function is unlikely to be linear – one person is very likely not as productive as two or more. The opposite is also true – with more people on the team, they may be able to cover a more extensive cleaning area during the same period under consideration.
Results Interpretation
In this case, such results can be explained by several reasons. First, the specific nature of the cleaning service provided may depend on other factors that limit the potential conclusions to varying degrees. For example, the complexity of cleaning commercial premises after renovation or small residential premises with scheduled cleaning can vary significantly in terms of time and effort. Additionally, the need to use specialized chemical equipment increases the time required to provide the service, and a large number of orders limits the area that can be covered per day due to the need to consider travel time.
Secondly, the provided sample of measurements can only show a particular team’s productivity without reference to an individual employee’s skill. In other words, each company resource, represented by a cleaner, can have different productivity at different sites and can be significantly ranked depending on the specifics of orders. Such comprehensive accounting is necessary for the company in the long term to optimize logistics and order schedules, supporting the organization’s overall development and providing fair financial incentives for individual workers (Stone et al., 2020).
Considering that the assessment may be inaccurate, the director of Maduro Cleaning needs to conduct such monitoring as often as possible, followed by connecting other resource accounts. For large orders, it is possible to resort to a work breakdown structure, Gantt charts, or, at a minimum, multifactor productivity measurement (Bdair et al., 2023; Stevenson, 2021). Therefore, it is possible to rely on these calculations, subject to limitations and further work.
Finally, in addition to the specificity and number of orders, another reason for such distribution may be the company’s distribution strategy for workgroups. If a team of two people is sent for small orders by area, then a team of three or four is sent for large orders. Larger jobs often require commercial premises cleaning, where work, regardless of area, tends to be more extensive due to potentially more complex contamination, such as post-construction (Liang et al., 2021). Combining the above reasons, a similar statistic can arise when a small team copes with a simple order faster than a larger team.
Forecasting
The goal of predicting productivity for a team of five is to understand the movement of this efficiency function as the amount of resources expended increases. In this case, a linear regression method suggests itself, which will consider previous measurements. However, in specific situations, this approach has several limitations outlined above. It is almost certain that using regression will show that a larger team will handle less work per day. To some extent, this statement will be accurate, as the limited resources at a small facility will not be optimized, leading to downtime or other reasons for decline. In this regard, it is best to implement Gantt charts that include calculations of capacity, free hours, and preliminary individual productivity indicators for employees, depending on the complexity of the services performed.
However, in this situation, due to the lack of data, it is possible to use linear regression exclusively. Likely, the number of employees is inversely related to productivity, and in the absence of further data, the work is based on this assumption. Figure 1 shows the linear regression trend line on the pre-calculated productivity graph.
Using the Excel software package, a regression analysis was performed to determine the relationship between the known three productivity values and the number of team members. The trend line equation became
where y represents the number of square meters harvested per day and x represents the number of team members. Using this formula, it is possible to calculate productivity for a group of five employees; therefore:
This suggests that with a team of five employees, productivity naturally decreases, which supports the hypothesis that the larger the team, the less productive it becomes.
Conclusion
In this work, operational management methods were adapted to calculate the productivity of teams with varying employee numbers for Maduro Cleaning, and a forecast was also provided for a larger team. The calculations indicate that a larger number of people results in lower cleaning productivity, measured in square meters per person. However, to conclude, one should consider the limitations and make the necessary calculations for each employee. Ideally, this would involve calculating individual productivity for each employee, factoring in the costs of other resources, and taking into account the specifics of each order.
References
Bdair, N., Alzyadat, W., & AlZubi, S. (2023). Intelligent Model for Optimizing Gantt Chart in the Planning Stage. In 2023 International Conference on Information Technology (ICIT) (pp. 556-560). IEEE.
Diwas, K. C. (2020). Worker productivity in operations management. Foundations and Trends® in Technology, Information and Operations Management, 13(3), 151-249.
Liang, G., Xu, L., & Chen, L. (2021). Optimization of enterprise labor resource allocation based on quality optimization model. Complexity, 2021, 1-10.
Reid, R. D., & Sanders, N. R. (2019). Operations management: an integrated approach. John Wiley & Sons.
Stevenson, W. (2021). Operations management (14th ed.). McGraw-Hill Irwin.
Stone, R. J., Cox, A., & Gavin, M. (2020). Human resource management. John Wiley & Sons.