1. Optimization of Retention Rates of Ready Mix Drivers
Kenneth Correa, Jaime Harmon, Pruthvi Hira, Destiny Sessums
Department of Systems and Industrial Engineering
Figure 2 – Ready Mix USA Comparison of Turnover Rates to NRMCA
RESULTS
INTRODUCTION
After numerous ride-alongs and constant feedback, the team narrowed the potential root causes that led to the high turnover rates. In order to improve the retention rates and reduce turnover rates, Ready Mix USA needs to:
- Increase upper management’s plant attendance
- Have better communication on company updates
- Demonstrate truck driver appreciation (such as new boots twice a year & biweekly morning breakfast)
After distinguishing the root causes enlisted above, the team performed a cost/benefit analysis to help determine options which provide the best approach to achieving benefits while preserving savings & if the benefits outweigh its costs.
Figure 5 displays an estimate of how much the company would have to invest, in order to improve their goal. New boots, biweekly breakfast, pay raise ($1 raise), & management visits are potential solutions based from the root causes that were documented. Determining the cost of the possible solutions were based on hypothetical estimates on average prices (such as boots, breakfast, transportation costs) and an estimate of 200 truck drivers. Figure 5 displays the cost for each solution calculated.
Qualitative data were recorded from the surveys that the team provided for the truck drivers. After various calculations and thorough data analysis, 30% of the drivers agreed to have new boots twice a year, 80% agreed for an increase of pay raise, 33% agreed for biweekly breakfast, and 40% agreed for management visits. In figure 5, the benefit percentages were calculated based from the surveys that the team created for the Ready Mix truck drivers. It was calculated by the percentages received from the surveys divided by the cost of it to get a final calculated benefit percentage.
Figure 6 is a linear programming model created to minimize the cost of the sum of the decision variables in order to decrease the current turnover rate by 30%. There are a couple of constraints that limit the objective function (minimum cost) and the value the decision variables can have. There were two constraints; the solution cost is the benefit percentage from the benefit/cost analysis which we wanted the constraint to be greater than our minimum goal of the turnover improving by 30%. The last constraint is the management visits to not exceed more than twice per month. In conclusion, in order to decrease the current turnover rate by 30%, Ready Mix would have to MINIMALLY invest approximately $1,648 in solely management visits neglecting the other decision variables like new boots, pay raise, & biweekly breakfast.
Ready Mix USA (RMUSA) is a building materials company owned by CEMEX, a giant in the world’s concrete industry, and operates primarily in the Southeastern United States. They perform many residential, commercial, industrial, and government projects of all sizes and capacities. RMUSA makes all of their own concrete mixes and delivers it to customers through mixer trucks operated by many delivery professionals.
Figure 4 – Cumulative Data for Driver Terminations
OBJECTIVE
Figure 1 – Ready Mix USA Monthly Turnover Rates
The main objective for this project is to reduce high turnover rates and increase retention rates for truck drivers at Ready Mix USA. The project team aims to impact the overall goal of RMUSA to reduce turnover rates for the year of 2019 by 30%. The project team distinguished and documented the root causes of high turnover rates and low retention rates for Ready Mix USA and developed solutions that will be implemented to combat these causes.
Figures 3 – Pareto Analysis of Driver Terminations
METHODOLOGY
The team’s approach to the discovery of the root causes of high turnover rate was first through systematic data collection. The team began with Ready Mix USA’s employee terminations list from the past four years and generated a Turnover Rate by Month chart as seen in Figure 1 to establish which months marked peak points of terminations. In Figure 2, the yearly turnover rates for were shown and compared to a benchmark, the NRMCA (National Ready Mixed Concrete Association) for reference. Our team then conducted Pareto Analysis (seen in Figures 3 and 4) to determine the top 20% of plants with the highest turnover rate based on the rule. It was determined that the top 20% of problem plants were the following locations: Downtown Atlanta, Midtown Armour Drive, and Marietta RM.
After the discovery from the Pareto Analysis, the project team decided to approach the next step of data collection by going on driver ride-alongs, where each team member paired up with a cement truck driver to get field experience, understand the physical conditions of the job, and collect qualitative data by asking the drivers questions pertaining to the company. The team used this strategy to determine the root causes of voluntary terminations at the plants from honest feedback and experiences from the employees.
Upon completion of root cause analysis, the team created a survey of possible solutions to encourage retention for distribution among the drivers at the plant with the highest turnover rate (Downtown Atlanta). The team used the results from the surveys to create a Linear Programming optimization problem that would determine most effective financial investments needed to lower the Turnover Rate by 30% for the year of 2019 while minimizing the overall cost on behalf of Ready Mix USA. Once the team found the most optimal solution, the information was given to the Ready Mix USA team for observation and implementation.
Figure 7 – Plant Visits Costs
Figure 5 – Cost/Benefit Analysis of TR Reduction Solutions
CONCLUSION
The objective function of Linear Programming suggested $1,648 to be spent in solely the decision variable of plant visits of upper management. This will not cover every plant under RMUSA because that would cost $2,200. The plants were prioritized by highest individual turnover rates to lowest along with the respective cost to visit the plant round trip from the Woodstock base. Next, the costs were summed until breaching the goal of $1,648. This concluded that the first 11 plants of the list should be visited by the upper management once a month to reduce turnover rates by at least 30%.
Figure 6 – Linear Programming Objective Function