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REFRESH is funded by the Horizon 2020 Framework Programme of the European Union under Grant Agreement no. 641933. The contents of this document are the sole responsibility of REFRESH and can in no way be taken to reflect the views of the European Union
On the effectiveness of food waste reducing actions in the meat supply chain Marjolein Buisman R. Haijema, J.M. Bloemhof, J. Snels
Wageningen UR, 7 June Operations Research and Logistics group, NL CCM 2016, Bonn
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What to expect
REFRESH project
Introduction of study
Methodology
Results
Further research
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REFRESH: Resource Efficient Food and dRink for Entire Supply cHain
Reduce food waste:
At retailers
At consumers
Production chains
Project structure
EU Horizon 2020 project
26 Partners from 12 European countries and China
Duration: July 2015 – June 2019
My role
Develop simulation and optimization models to test food waste reducing actions
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Literature/background
Almost half of the food grown is lost for human consumption (Lundqvist et al., 2008)
20% in the meat supply chain
Need to incorporate food safety in inventory management (Akkerman et al., 2010)
Quality controlled logistics improves supply chains (van der Vorst et al., 2011)
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Need to:
Reduce food waste
Include safety in inventory management
Research question
Effect of dynamic shelf life on food waste?
Objective and research questions of study
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Method
Simulation model of meat supply chain
From processor to retailer
Farm/
Supplier Food processor
Distributor/ Wholesaler
Retailer Customer
Area of focus
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Method
Simulation modelling in MATLAB
Inventory and microbiological growth model
Inputs
Consumer demand
Costs
Time
Temperature
Maximum shelf life
Outputs
Profit
Waste
Microbiological count
Shortages
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Method
Retailer orders product at DC (R,S policy)
Products sold to consumers either FIFO or LIFO
Products are wasted at the end of shelf life
Based on date
Based on microbiological count
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Method
Fixed shelf life: 6 days after production
Dynamic shelf life: based on actual quality
Gompertz model for microbiological growth
N = A + C ∗ e−e−B t−M
Temperature is main influencer of product quality
0
2
4
6
8
10
0 5 10 15
N(t)
log
cfu
/g
r
time (days)
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Scenario’s tested
Fixed and dynamic shelf life
Scenarios Experiment Temperatures (DC, transport
to retailer, shelf)
Change in parameters of
Gompertz curve
1. Base 1 2, 10, 4
2. Temp 2a
2b
2c
2d
0, 8, 2
1, 9, 3
3, 11, 5
4, 12, 6
3. Growth
model 3a
3b
3c
3d
-10 %
-5 %
+5 %
+10 %
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N = A + C ∗ e−e−𝐁 t−𝐌
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Results Temperature changes
0
10
20
30
40
50
60
70
0/2/8°C 1/3/9°C 2/10/°C 3/11/5°C 4/12/6°C
Pro
fit
per w
eek
Temp in SC
Fixed
Dynamic
Profit
Profit increases when temperature decreases
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Results Temperature changes
Shortages and waste
0%
10%
20%
30%
percen
tag
e o
f S
ho
rta
ge
Temp in SC
Fixed
Dynamic
0%
10%
20%
30%
percen
tag
e o
f w
aste
Temp in SC
Fixed
Dynamic
Shortages/waste decreases with lower Temp.
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Results Temperature changes
Fixed shelf life
Basic temperature
Increase of 2°C
!! 0
50
100
150
200
0 2 4 6 8 10
Sale
s
Microbiological count on sold products (log/cfu)
0
50
100
150
200
250
0 2 4 6 8 10
Sale
s
Microbiological count on sold products (log/cfu)
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Results Temperature changes
Dynamic shelf life
Basic temperature
Increase of 2°C
Safer products
0
50
100
150
200
0 2 4 6 8 10
Sale
s
Microbiological count on sold products (log/cfu)
0
50
100
150
200
0 2 4 6 8 10
Sale
s
Microbiological count on sold products (log/cfu)
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Parameter changes
0
1
2
3
4
5
6
7
8
9
0 1 2 3 4 5 6
Mic
ro
bio
log
ical
co
un
t (lo
g/
cfu
)
Days
spoilage point
Basic
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Parameter changes
0
1
2
3
4
5
6
7
8
9
0 1 2 3 4 5 6
Mic
ro
bio
log
ical
co
un
t (lo
g/
cfu
)
Days
spoilage point
-10%
-5%
Basic
+5%
+10%
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Influence of alternative growth models
Microbiological growth modelled with Gompertz
Results are affected by parameter setting
Many more options for predictive modelling of Pseudomonas spp. on meat
Gamma
Ratakowsky
Logistic model
etc.
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Influence of alternative growth models
0
2
4
6
8
10
12
0 5 10 15 20 25
N(t)
(lo
g c
fu/
gr)
time (days)
Bacterial count at 3ºC
Ratakowsky
Gompertz
Gamma
Spoilage point
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Influence of alternative growth models
Growth rate of Pseudemonas spp. differs for the tested models Differences may occur due to
Predictive nature of the models
Tested in laboratory
Many factors important in growth of micro-organisms on food products
0
2
4
6
8
10
12
0 10N(t)
(lo
g c
fu/
gr)
time (days)
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Wrap-up
Dynamic shelf life
Reduces food waste if temperatures are lower then expected
Ensures safe products
Profit levels are maintained with dynamic shelf life
Choice of quality model is important
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Further research
Other actions such as dynamic pricing
Discounting on almost spoiled products
Optimizing replenishment
Consumer behaviour
Towards a TTI sensor
Towards “old” vs. “fresh” products
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Questions and remarks?
More Information about REFRESH E-Mail [email protected]
Website www.eu-refresh.org
Twitter @EUrefresh
Facebook https://www.facebook.com/eurefresh
PhD candidate Wageningen UR
Operations Research and Logistics
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