Reduce, reuse, and recycle

Paul Boughton

Carly Barry explains how statistical analysis software helped reduce food waste and highlight cost savings.

Rose-Hulman Institute of Technology is a top-ranking college focused on excellence in engineering education. The campus community promotes ethical and moral responsibility in shaping its graduates to become national and global citizens. As part of this endeavour, the college created a sustainability team to investigate how the institution could reduce its own environmental footprint. One of the team's efforts was to reduce, reuse, and recycle waste from campus dining centres.

Dr Diane Evans, a Six Sigma black belt and associate professor of mathematics at Rose-Hulman, saw the food waste problem as an ideal candidate for a Lean Six Sigma project that students in her Six Sigma class could lead.

"I wanted my students to go through the process of completing a project from start to finish," says Evans. "The food waste project provided students with this opportunity, and gave them a chance to put the skills they were learning in class to use in the real world." With Lean Six Sigma tools, the students set out to reduce the amount of food waste generated from Rose-Hulman's campus dining centre during lunch.

The challenge

According to a July 2012 article in Food Policy, US food waste on the consumer level translated into almost 273 pounds per person in 2008. Evans' students converted this number into pounds per day, and to determine the amount of waste per meal, they divided the figure by 2.5 meals per day (they did not count breakfast as a full meal because it typically does not see as much waste as lunch or dinner).

The students ended up calculating an average food waste amount of 4.78 ounces per meal. Using this number as a standard, the class set out to reduce the edible food waste per student by one ounce per meal during the lunch period. With this goal clearly established, the class began by learning more about the current dining centre processes and food waste at Rose-Hulman.

"Our aim with this project was to reduce food waste using standardised quantitative process improvement techniques," says Neel Iyer, mechanical engineering student at Rose-Hulman and a member of the project team.

"Lean Six Sigma project tools make it easy to share the hard savings and prove results statistically, while also giving others a framework to replicate what we have done."

The students used the define, measure, analyse, improve, and control (DMAIC) methodology to manage and complete the project. As part of the define phase, students developed process maps using Quality Companion, Minitab's process improvement software. The process maps helped them understand the current flow of students through the dining center, as well as where potential improvements could be made.

In addition, they created a 'Critical To Quality' or CT Tree to quantify the students' expectations for their dining experience and to visually connect the students' needs to the goals of the project. This brainstorming tool allowed them to verify that sustainability issues were a key concern to students, who wanted their school's dining room to be environmentally friendly. To determine the causes of food waste in the dining room, the class also performed a brainstorming exercise and constructed a fishbone diagram.

With the process defined and the key causes outlined, the project entered the 'measure' phase. The students collected baseline food waste data in order to establish the current capability of the process. They conducted a food audit to form their data set, during which class members stationed themselves at the food disposal area of the dining centre.

After students finished eating, class members collected their trays and scraped and dumped uneaten food and liquid into a bucket. Then the weight of the food waste was recorded. Using control charts, the students determined that the lunch-time waste process was 'in control', and that there were no unusual points or outliers. They also viewed the data using a probability plot, which confirmed it had a normal distribution.

The class chose to measure the capability of their process against the maximum national average waste per meal calculated earlier (4.78 ounces). They ran a capability study in Minitab, and viewed all aspects of the analysis on one chart with the software's Capability Sixpack, which includes Individuals, Moving Range, and R or S Charts, a capability histogram and normal probability plot, and a capability plot to compare the process variability to the specifications. The study confirmed that Rose-Hulman had above-average food waste per person during lunch.

For each process input from the group's current-state process map, they constructed a Cause and Effect (C&E) Matrix, with outputs based on those already listed in the CT Tree. The C&E Matrix helped the students to determine likely relationships between process inputs and outputs, and conducting a Failure Modes and Effects Analysis gave them another tool to identify and prioritise the severity of potential causes of waste. 

Now the project entered the 'Improve' phase. The students formed a list of recommended actions based on the variables they could control and the short time frame they had to complete the project. These actions focused on educating students about food waste using posters, demonstrations, and seminars at the dining centre during lunch hours. They also suggested that dining room staff provide students with smaller serving utensils for condiments, pre-dish more foods, and limit how many glasses and bowls of food or drink students could take per tray.


After the awareness campaign, the class performed a second food waste audit and used Minitab to determine the post-improvement process capability. They were able to show that the process was still capable with respect to the national average, and after providing students with food waste education, they found the process capability had improved over the capability analysis conducted before the education intervention.

Further analysis of food waste data collected before and after the campaign revealed a statistically significant difference in waste amounts. The class's data found an average reduction of 2.66 ounces of waste per person/meal (or 0.166 pounds per person/meal) after the campaign.

Because the dining centre typically serves about 875 students per day during lunch, the class estimated that the centre could see a waste reduction of approximately 2,327.5 ounces, or 145.4 pounds during a single lunch period. Over the 50 lunch periods in a typical quarter at Rose-Hulman, a total of 7,270 pounds of food waste could be saved. And since food waste costs the dining centre $1.60 per pound ($0.10 per ounce) on average, the class calculated a savings of $471.20 during just the two days they held the food waste campaign and collected data. If the campaign were run over an entire quarter, they estimated total savings of $11,781.

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Carly Barry is with Minitab Inc, State College, PA, USA.