Use benchmarking to improve your production metrics

Features - Management

Companies can use operational benchmarks to improve crop production efficiency and understand the impact of input prices on sale price and profit.

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Large structural or technological changes in any business are difficult. However, even a small change of 1 percent (increase or decrease) can have a major impact on a company.
 
Table 1 shows a 1 percent increase in production yields a 24.63 percent increase in resulting profit. The increase is high because loss is removed from one side of the equation adding more profit on the other. A 1 percent increase in labor costs yields a 5.25 percent reduction in profit. A 1 percent increase in labor hours used can affect profit by as much as 7.17 percent. This is why minimum wage increases are so impactful. If the percent figures are turned into dollars it becomes quite clear that small changes do impact the bottom line.


Reducing shrink
One of the achievable areas that any company can track and improve on is shrink. Whether this loss is caused by pests or diseases, damaged plants or failure to meet contract specifications, if the plants are simply discarded growers have nothing to base improvement on.
 
Horticulture professors Roberto Lopez at Purdue University and Brian Krug at University of New Hampshire have presented examples of how small amounts of shrink affect profitability.
 
A grower is producing a crop of 1,000 4-inch pots. If the plants sell for $3.99 then the grower should ideally make $3,990 gross revenue. The actual cost of growing (metrics not shown here) is $2,650. The net profit would be $1,340. 
 
Production efficiency goes beyond good space utilization. The key question is, how much of the product actually gets sold?In a real world situation, if the grower experienced a 5 percent loss or shrink due to crop quality issues, the gross revenue would decrease to $3,790.50. The cost of growing would remain the same, but the net profit would only be $1,140.50, a loss of $199.50. This would amount to a 14.9 percent decrease in profit.
 
A 15 percent shrink loss would result in gross revenue of $3,391.50 and net profit of $741.50. The grower would incur a $598.50 profit loss and a 44.7 percent profit reduction.
 
A grower with 15 percent shrink would have suffered a nearly 45 percent reduction in profit on just 1,000 pots. If that grower was producing 30,000 pots, the loss would have been $14,000.


Tracking shrink
A simple benchmark metric is to have employees keep track of how many units of each crop they dump. Assign a reason (disease, damage, no sale, etc.) and then use a spread sheet with graphics to tally the data. Keep the shrink file as part of the quarterly report. The data can point to a problem. Additional metrics will tell you the source of the problem(s) and the amount of lost profit.


Production labor metrics
Most growers recognize the loss when they see the dump pile growing at the back of their greenhouse. More difficult to see is the loss from inefficient or poorly-trained workers. Looking at common ways workers are used, there are two main types of analysis for measuring employee productivity.


Independent task assessment
Independent task method is fairly easy to determine, each employee has a distinct task. The output unit, say number of trays transplanted, is assigned to each worker. The supervisor through a test run can determine an initial “guesstimate” of what hourly or daily output should be. The actual output is recorded.
 
Over time, a realistic company-wide metric is developed for that task and the individual capabilities of each worker are documented. The supervisor keeps track of the output of each employee going forward, and can assess if training allows improvement or if some employees are just not keeping up. Keeping these metrics handy and showing graphics to all the employees on a regular basis can also keep workers focused.


In-series metrics
In-series metrics are trickier to measure. Consider a company that runs three packing lines with four members on each line. The unit output should be measured, i.e. the numbeer of boxes packed for each line. If one line is doing far less than the others, the total daily output data process does not tell the business owner what step, employee or piece of equipment is not working to expectations.
 
Factories since Henry Ford have used piece rate studies to assess productivity. Our industry rarely does this. A line manager positioned where he/she can see an entire line can use a stop watch to time how many seconds it takes for one employee to perform a task and then hands it off to a second employee. The line manager should track the first employee for as long as it takes to have a good sample. The manager then tracks the activities of the next employee and so on. By rotating through the lines, the manager can determine the average production time for a unit, which can be used for planning the next time that a task/activity occurs. Knowing where past problems have occurred allows the manager to prepare for them.


Quantifying benchmarks
A greenhouse operates three transplanting lines with four seasonal employees per line supervised by a new line manager. The company has a production goal of 12,000 units per eight-hour day (4,000 units per line or 500 units per hour) based on previously established “historic” benchmark metrics for this activity performed at this time of year.Employee wages are $10.50 per hour. Daily labor costs for this activity should be $1,008. An in-series assessment of the three lines is conducted the first day of production and the results are:

  • Line 1 produced 1,120 units
  • Line 2 produced 2,080 units
  • Line 3 produced 2,480 units

On the first day the employees produce 5,680 units, only 47 percent of the 12,000 unit daily goal. At this rate the task is going to take 16.9 hours to complete instead of eight. At $10.50 per hour, the labor cost increases to $2,130 for that job. This is a $1,122 cost increase for the task and a reduction in profit.
 
If the line manager does the proper piece-work assessment and can identify the issues, improvements should be straight forward. Assume that a 20 percent increase in productivity is possible by repairing equipment, reorganizing the flow of materials and reducing the amount of talking between employees. These changes result in a line yield of 8,040 units. Assume that productivity can be increased another 20 percent by retraining or replacing poorly performing employees and/or rearranging the location of personnel so that the slowest workers are at the end of the line nearest to the line manager. The result is an increase in yield to 10,440 units. With the performance improvements the task now takes only 9.2 hours at a cost of $1,159. This saves the company $971 for the task.


Going one step further
A good manager will ask a larger question. If the four poorest performing employees were let go, could the same task be accomplished with two production lines for less cost, even if it takes a longer time? The answer is yes.
 
If the best personnel are retained and two lines are capable of operating at 67 percent efficiency (no line operates at 100 percent all of the time), the yield is still 8,040 units, but it takes 11.94 hours. The cost is now 8 hours x $10.50 per hour x 11.94 hours equals $1,002. By downsizing to two lines the same savings are realized without any improvements, retraining or re-design. Think about the potential savings if repair and redesign occurred with the new team arrangement. It might be possible to squeeze out a further increase in productivity.


The assessment process
If managers report production metrics on a weekly or quarterly basis, they can assess trends and cost history per crop.
 
Over a year, there may be seasonal fluctuations that are not apparent when data is simply reviewed weekly and then filed. To make benchmark assessment work, one must acquire data consistently, process that data onto visual graphs/tables and hold assessment meetings with management and workers to discuss potential improvements on a regular basis. 
 
Paul Thomas is professor and extension specialist, University of Georgia, Department of Horticulture, pathomas@uga.edu. Charlie Hall is professor and Ellison Chair in International Floriculture, Texas A&M University, Department of Horticultural Sciences, charliehall@tamu.edu.