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Trends in Food Science & Technology xxx (xxxx) xxx
Please cite this article as: Maria Ioanna Malliaroudaki, Trends in Food Science & Technology, https://doi.org/10.1016/j.tifs.2022.01.015
Available online 14 January 2022
0924-2244/© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Energy management for a net zero dairy supply chain under climate change
Maria Ioanna Malliaroudaki
a
, Nicholas J. Watson
a
, Rebecca Ferrari
a
, Luanga N. Nchari
b
,
Rachel L. Gomes
a
,
*
a
Food Water Waste Research Group, Faculty of Engineering, University of Nottingham, Nottinghamshire, NG7 2RD, UK
b
NIZO, Kernhemseweg 2, Ede, 6718 ZB, the Netherlands
ARTICLE INFO
Keywords:
Energy use
Dairy
Food supply chain
Climate change
Modelling
Net-zero
Sustainability
ABSTRACT
Background: The dairy industry requires substantial energy resources at all stages of production and supply to
meet consumer needs in terms of quantity, quality and food safety. The expected future climate change effects
will cause serious uncertainty to the dairy industry. Adapting to these upcoming conditions is a challenge and
one that is compounded by the continuous increase in food demand, as a result of global population growth.
Predictably, under current conditions, this situation might lead to a signicant increase in the energy re-
quirements of the dairy industry. Therefore, there is a clear need to mitigate energy use through enhanced energy
conservation, waste reduction and waste management.
Scope and approach: This review paper presents and discusses alternative dairy operations and mitigation stra-
tegies that have the potential to lead the dairy industry towards net-zero carbon emissions. Further, the focus of
this work turns to supply chain energy modelling (SCEM) as means to mitigate energy use, while relevant work in
the literature is reviewed.
Key ndings and conclusions: Supply chain energy models can provide a complete overview of the energy demand
and the energy mix of a dairy supply chain. Additionally, they can highlight the most energy consuming pro-
cesses and allow the evaluation of alternative energy-saving operations that can lead towards the net-zero carbon
target. Overall, the development or use of computational tools for simulating the energy demand in the industry
has strong potential for improving sustainability across the dairy supply chain.
1. Introduction
Our planet in 2021 is undergoing drastic changes that will continue
over the next decades to affect the global food sector and its energy
demand. Changes are incurred from the fast-growing world population,
urbanization, and the ever-increasing effects of climate change. Energy
consumption for food production and supply represents between 15 and
20% of the global total (Usubiaga-Lia˜
no et al., 2020). Fig. 1 illustrates
the annual per capita energy use for both food production and con-
sumption across different regions around the world, indicating that
North America and High-Income Asia-Pacic regions (APAC) consume
signicantly more energy in food systems than other parts of the world.
In 2019, the global energy use was estimated to be equal to 173,340
TWh, up 53% compared to 20 years ago and with a global energy mix
comprised mostly of non-renewable resources, >80% of which are
derived from fossil fuels (Ritchie, 2019). Although increasing energy
resources are required to deliver against fast-rising demand, they are
accompanied by signicant environmental challenges which also need
to be addressed. These include the global warming phenomenon, envi-
ronmental degradation, pollution, loss of biodiversity and depletion of
natural resources (Hussain et al., 2020). Not enough has been done to
reduce energy consumption and the focus needs to be shifted to building
a more energy-efcient food production and supply system. One of the
factors bringing about change to our planet is the alarming rise in world
population. An initial appraisal by the Food and Agricultural Organi-
zation of the United Nations (FAO) warns that a signicant increase in
food production will be required to cover demand, which in 2050 is
expected to reach a growth of 70% versus the 2005 values (Alexandratos
& Bruinsma, 2012). This expected rise in food demand predictably leads
to a corresponding growth in energy demand in the food supply chain.
Another critical future threat to the food industry is climate change,
which is inevitably affecting the world’s agricultural production and has
serious repercussions for the food industry, including uncertainty for
food quantity, quality, security and safety (Niles et al., 2018). Given the
* Corresponding author.
E-mail addresses: Maro.Malliaroudaki@nottingham.ac.uk (M.I. Malliaroudaki), Rachel.Gomes@nottingham.ac.uk (R.L. Gomes).
Contents lists available at ScienceDirect
Trends in Food Science & Technology
journal homepage: www.elsevier.com/locate/tifs
https://doi.org/10.1016/j.tifs.2022.01.015
Received 30 March 2021; Received in revised form 13 October 2021; Accepted 5 January 2022
Trends in Food Science & Technology xxx (xxxx) xxx
2
climate change challenges that need to be addressed, precautionary
practices may require additional energy supply to ensure delivery of
food supply.
One key food sector that is highly vulnerable to the challenges of
growing demand and climate change impact is the dairy industry
(Feliciano et al., 2020). The impact of climate change on the dairy sector
will be predominantly realised in three ways: First, although the global
warming effects will not be adverse everywhere, a relevant increase of
drought events is expected across the globe affecting the crop yield and
as a result reducing availability of animal feed (Nardone et al., 2010).
Secondly, rising temperatures can cause heat stress to cows, leading to
decreased milk production and increased mortality risk (Schifano et al.,
2012). Finally, food safety concerns escalate as a consequence of
food-borne pathogens adapting to global warming, where heat resis-
tance and their survival and/or reproduction rates may alter. In such
scenarios, the current pasteurisation methods may no longer be effective
and the implementation of stricter food safety measures would be
inevitable e.g. more intense heat treatment and/or lower refrigeration
temperatures (Feliciano et al., 2020; Miraglia et al., 2009). These chal-
lenges to the dairy industry are already in motion and need to be
addressed throughout the dairy supply chain before their consequences
become unmanageable and impact of safe and sufcient supply.
Although the dairy sector suffers the consequences of global warm-
ing, it is ironically concurrently responsible for the signicant release of
GHG (greenhouse gas) emissions, which contribute to the global
warming phenomenon. As estimated in 2007, farm activities, product
manufacturing, and logistics of the dairy sector accounted for 2.7% of
the total anthropogenic GHG emissions (FAO, 2010). These are mainly
derived from bovine enteric fermentation and extensive fossil fuel usage
along the supply chain (Ladha-Sabur, Bakalis, Fryer, & Lopez-quiroga,
2019). Regrettably, the sector’s emissions are rising at an alarming
rate with the dairy sector’s GHG emissions have increased by 18% be-
tween 2005 and 2015 levels (FAO & GDP, 2018).
Dairy products play a major role in human diets as they are an
important source of protein and include essential minerals and vitamins
such as calcium and Vitamin B (Caroli et al., 2011). In particular, the
dietary energy intake of dairy products (between cheese, milk and
butter) accounts for an estimated 14% of total consumption in devel-
oped countries and about 5% in developing countries (Gerosa & Skoet,
2012). In view of the fast-growing world population, the higher per
capita income growth and “westernising” diet trends in the East, a sig-
nicant rise in demand for dairy products sets the long-term sustain-
ability of the sector into question (OECD-FAO, 2020; Pingali, 2007). In
fact, an estimated rise of 1.0% per year is expected over the decade
2020–2030 for fresh dairy products (OECD-FAO, 2020). Although dairy
products are important in diets, the energy use and GHG emissions
associated with meat and dairy products production are much higher
than those of plant-based food products (Green et al., 2018). Consumers,
especially in the western world are growing increasingly concerned
about environmental impact and animal welfare, with growing trends in
adopting low-meat, vegetarian and vegan diets (Feh´
er et al., 2020;
Hodson & Earle, 2018). Indeed, dietary GHG emissions of vegan diets
are about half that of meat-eaters’ diets (Scarborough et al., 2014). Thus,
shifting eating habits towards plant-based diets could substantially
contribute to environmental sustainability.
This paper explores the importance of reducing energy consumption
in the dairy industry and recommends energy mitigation actions, in line
with the “net-zero” carbon emissions target set by global organizations
(IPCC, 2018; Bataille, 2020). First, the current patterns of energy use
along the dairy supply chain will be presented, followed by proposed
energy mitigation strategies and alternative technological dairy opera-
tions across the sector. Subsequently, the challenges emerging through
the implementation of energy mitigation are ascertained. Finally, a
discussion will follow on how supply chain energy modelling (SCEM)
can play a catalytic role in addressing those challenges and contribute
towards optimising the energy use along the supply chain.
2. Net zero carbon emission in the dairy industry
Global organizations and governments have set sustainability targets
and relevant regulatory directives to address the climate change phe-
nomenon and protect the environment (Gil et al., 2019). The United
Nations has proposed 17 Sustainable Development Goals (SDGs) to be
met by 2030 (Summit, 2019). For the food industry, these SDGs entail
the improvement of natural resources management, the use of
energy-efcient equipment, the production of valuable products from
residues and waste and the limitation of waste and losses while sup-
porting recycling at all supply chain stages (Kazancoglu et al., 2018;
Summit, 2019). In addition, the Paris Agreement in 2015, established
that all sectors will have to reach the net-zero carbon dioxide (CO
2
)
emissions target by 2050–2070 and drastically reduce non-CO
2
emis-
sions that contribute to global warming such as methane (CH
4
), to limit
global warming well below 2 ◦C and towards 1.5 ◦C from the
pre-industrial average temperature levels (IPCC, 2018; Bataille, 2020;
Fuglestvedt et al., 2018).
“Net-zero carbon” or “carbon neutral” means that the anthropogenic
carbon emissions that cross the boundaries of a system should be
balanced by the anthropogenic carbon emissions removals through
those boundaries (IPCC, 2018). Thus, for a whole sector such as the food
industry, the net-zero carbon target indicates that all carbon emissions
produced throughout the supply chain should be limited to a minimum
by implementing efcient sustainability strategies and any remaining
emissions should be balanced by carbon emission removal practices. For
some sectors such as the dairy industry, the mitigation potential for
Fig. 1. Annual per capita energy use due to food
consumption measured in GJ/capita in the year
2000 and 2015. The bar charts involve the share of
energy use for the production and processing of
different foods (meat, sh, dairy/animal products,
grains, vegetables/fruits/nuts, other food products),
the direct energy required for cooking and refrig-
eration and the indirect energy use which refers to
the energy used in the production of the food-
related energy products consumed within the
household. “Grains” include grains and grain-based
products such as bread and pasta as well as other
products such as biscuits, pastries, and cakes.
“Other food products” includes sugar products,
beverages, oil seeds, and other vegetable fats which
are all plant-based products. The data for this chart
were obtained from the supporting information 1 of
the study of Usubiaga-Lia˜
no et al., 2020.
M.I. Malliaroudaki et al.
Trends in Food Science & Technology xxx (xxxx) xxx
3
achieving the net-zero carbon target is limited under the current tech-
nology due to the inability to control the vast methane emissions caused
by biological processes such as bovine enteric fermentation (FAO &
GDP, 2018). Although the bovine-related emissions are the major
challenge for the industry, the pursuit of bovine alternatives and syn-
thetic milk production that can reduce the reliance on bovines for milk
production and is compositionally equivalent (e.g. in terms of protein
content) to dairy milk has yet to become a scalable realisation. Hence,
such alternatives to bovine milk still require signicant innovation to be
viable at a commercial scale (R¨
o¨
os et al., 2017).
All party members of the Paris Agreement will inevitably have to set
their own national governmental regulations to be legislated in order to
enforce companies and stakeholders to become carbon neutral. Today,
companies that choose to ignore the importance of net-zero carbon
target or even delay any rst steps in that direction risk having to
implement legislative regulations under pressure, which may incur
costly misjudgements and sub-optimum solutions (OECD, 2015). On the
other hand, the industries and companies that acknowledge their social
and environmental responsibility and start taking net-zero carbon ac-
tions from an early stage have the potential to signicantly reduce the
downside risks during adaptation (OECD, 2015). Amidst markets where
there is a growing trend in favour of social environmental awareness,
where products and manufacturers are preferred and awarded for their
“green” strategies, companies with net-zero actions will gain social
preference and recognition. Environmental sustainability will become
the new norm of competency among the world company network.
Carbon neutrality may not always be achievable for each site owned
by a company. The dairy industry is such a case and net-zero carbon
target may only be achievable from a holistic perspective, within the
framework of industrial symbiosis. Specically, to achieve the net-zero
carbon levels within the dairy supply chain, those supply chain actors
who are net-negative, meaning that they release carbon emissions, can
collaborate with other supply chain actors or industries out of the dairy
sector which are or can become net-positive, meaning that their activ-
ities overall absorb carbon emissions, in order to achieve carbon
neutrality altogether (Abreu & Camarinha-Matos, 2008). Creating a
net-zero carbon dairy sector can provide signicant benets to both the
environment and industry stakeholders (Abreu & Camarinha-Matos,
2008). From an air quality perspective, net-zero carbon can also
contribute to a reduction in a particulate matter arising from fossil fuel
combustion for energy generation, leading to considerable environ-
mental and public health benets (Wang et al., 2020). From the indus-
trial perspective, energy mitigation strategies can make the sector
energy-autonomous or semi-autonomous leading to fewer running ex-
penses for energy use and fewer nancial charges since the govern-
mental tax incentives favour net-zero activities. Furthermore, the
investment in activities such as the production of energy from waste or
residues can generate an additional source of prot for the stakeholders.
This review article presents the opportunities arising for the dairy sector
to mitigate energy use, that can signicantly contribute towards the
net-zero carbon target.
3. Energy mapping for the dairy supply chain
The rst step in the process of mitigating the energy use in the dairy
supply chain is to assess the current energy use of every single compo-
nent of the chain. This will clearly indicate the hotspots of energy use
where mitigation actions will deliver the most value and savings. This
section aims to offer an energy map of the dairy supply chain by
providing estimates of the energy consumption at each supply chain
stage obtained from the current literature. The dairy supply chain begins
at the farm where raw milk is produced, and then is temporarily cold
stored before being transported to the dairy plant for product
manufacturing and packaging. The packaged products are then deliv-
ered via refrigerated trucks to the retail outlets via distribution centres.
Finally, dairy products are bought by consumers for domestic use, which
includes refrigeration and/or cooking. In this study, the dairy supply
chain was divided into four major stages for convenient presentation of
the energy patterns: the dairy farm, manufacturing, the cold-chain and
consumption. Fig. 2 outlines the processes taking place in each of these
four supply chain stages. After separately analysing the energy use in
each dairy supply stage, a nal overview of the carbon emissions derived
from energy use along the entire dairy sector is provided.
This section aims to provide an energy map of the dairy supply chain
by individually analysing each of the supply chain stages from the dairy
farm, manufacturing, cold chain and consumption use. Developing an
energy map of the dairy supply chain requires energy use quantication
in suitable units of measurements, which enables the reader to develop a
sense on how energy use is allocated along the dairy supply chain. Fig. 3
is an energy map illustration for the dairy supply chain providing energy
data which can be used for broad estimation of the energy use of
different dairy products along the dairy supply chain.
The majority of studies express energy consumption either in energy
units or as carbon footprint per kg of product depending on their
objective. The reason why energy use or carbon footprint is measured
per unit of product is to allow the comparison of energy use between the
supply chain stages. In studies that aim to measure the amount of
electrical or thermal energy input, energy is usually expressed in MJ per
kg of product. In studies that aim to assess the impact of energy use on
global warming, they evaluate the carbon footprint of energy use,
measured in kg of carbon dioxide equivalent (CO₂e) per kg of product
(Flysj¨
o et al., 2014). It is important to note that the carbon footprint in
CO₂e of 1 MJ of energy use may vary signicantly, depending on the fuel
burned or the energy mix of electricity from the grid. The energy use of a
process or product can be translated into the carbon footprint only if the
energy mix and the emission factors of the fuels or energy resources
consumed are known. Overall, the carbon footprint of energy use and
the actual energy use express different values and should not be
Fig. 2. Illustration of the four stages proposed for dividing the dairy supply
chain including an outline of the processes taking place at each stage.
M.I. Malliaroudaki et al.
Trends in Food Science & Technology xxx (xxxx) xxx
4
compared with each other. The target of energy mitigation should aim
for both an improvement in energy efciency and thus reduction of the
energy use and in carbon emissions reduction by utilising alternative
fuels or electricity resources. In the following energy mapping process
for each supply chain stage presented in Sections 3.1 – 3.4 energy is
quantied in MJ per unit of product, while Section 3.6 presents carbon
emissions data for the dairy supply chain.
3.1. Dairy farm energy mapping
Starting with the energy mapping of in the dairy farm, according to a
recent review analysing of the energy consumption in dairy farms the
total energy use was estimated to range from 2.7 MJ/kg of ECM in
organic dairy farms to 4.1 MJ/kg of ECM in conventional ones (Shine
et al., 2020). Here, ECM stands for energy corrected milk which stan-
dardises milk of any fat and protein content, to the caloric equivalent
amount of milk of 3.5% fat and 3.2% protein content respectively
(Bernard, 1997). The difference between conventional and organic
farming energy use is attributed to the fact that conventional farming
requires more indirect energy use for feed and fertiliser production
compared to organic farming (Kumar & Chakabarti, 2019). In conven-
tional farming, energy use for fertiliser and feed production is important
although it is associated with indirect energy use. In fact, fertilisers and
feed production are associated with 13% and 43% of the total energy
consumption in conventional dairy farms. On the other hand, energy
consumption for feed production in organic farming is related to direct
energy use, due to the free-range grazing on-farm and is associated with
34% on average of total primary energy use, while the energy require-
ment for organic fertiliser production is negligible at <1% (Shine et al.,
2020).
Across all studied dairy farms between conventional and organic, out
of the total energy use of dairy farms, 32% on average is related to direct
energy use from on-farm activities, while the remaining 68% is related
to indirect energy use from out-of-farm activities such as feed and fer-
tilisers production (Shine et al., 2020). Focusing on the direct usage, the
energy mix is comprised of electricity and liquid fuels at a rate of 48%
Fig. 3. Infographic providing information for the
energy use in the dairy supply chain separated in 4
stages: the dairy farm (Fig. 3a), manufacturing
(Fig. 3b), cold chain (Fig. 3c) and consumption use
(Fig. 3d). The energy values provided in the tables
and charts of Fig. 3 can be used for broad estimation
of the energy use of different dairy products along
the dairy supply chain. [GHG, greenhouse gas
emissions]
[1] Information obtained from the study of (Shine
et al., 2020). (Where, ECM stands for energy cor-
rected milk (Bernard, 1997)).
[2] Data obtained from the study of (Flysj¨
o et al.,
2014) in Table 4, showing the energy use of
different product categories manufacturing.
[3] Information obtained from (Xu et al., 2009).
[4] Adapted from data presenting the energy use in
terms of fuel and electricity in kJ/kg of product for
the manufacturing stage of different dairy products.
The products were categorised into fresh and high
process products and the average share of fuel and
electricity was calculated for each category
(Ladha-Sabur et al., 2019).
[5] Information adapted from the LCA study of
(Thoma et al., 2013).
[6] Data obtained from the study of (Tassou et al.,
2009) presenting the energy use per kg of product
and km of transportation distance consumed by
different type of refrigerating trucks (medium rigid,
large rigid and 32-tonne articulated trucks).
[7] Data obtained from the study of (Burek &
Nutter, 2020) in Fig. 15b.
[8] Information adapted from the LCA study of
(Thoma et al., 2013).
M.I. Malliaroudaki et al.
Trends in Food Science & Technology xxx (xxxx) xxx
5
and 52% respectively (Fig. 3a). Electricity is used in refrigeration for
milk cooling, pumping for milk harvesting, water heating, water
pumping, lighting, while the other main source of direct energy con-
sumption are fuels such as diesel, kerosene, natural gas, liqueed pe-
troleum gas (LPG), and lubricants which are used mainly for on-farm
activities for water heating and powering mechanical machinery (Shine
et al., 2020).
3.2. Manufacturing energy mapping
After the primary production of raw milk on the farm, the milk is
transported to the dairy manufacturing plant where various processes
occur to create the end-products. This includes the raw milk storage at
the dairy plant reception, heat treatment, separation, bacterial fermen-
tation, ripening, packaging and storage of the nal product ready for
distribution (Fig. 4). Dairy manufacturing requires substantial levels of
energy due to the extensive heating and cooling processes taking place.
The energy demand per kg of raw milk processed in dairy plants can
exhibit variations ranging from 0.8 to 1.9 MJ, depending on the products
and the scale of production (Xu et al., 2009). The processing plant also
includes Clean-In-Place (CIP) processes to clean the inner surfaces of
processing equipment and may also include a wastewater treatment
process (or the used water is otherwise sent to a third-party wastewater
treatment site).
Energy consumption mainly results from electricity and fuel use,
with Fig. 3b presenting an overview of the energy use in dairy
manufacturing. Processes such as refrigeration, packaging, homogeni-
sation, standardisation, milk pumps and plant automation require
electricity, whilst heat treatment processes require mainly steam that is
produced from fossil fuel combustion (Tomasula et al., 2014). The table
in Fig. 3b provides some estimates for the energy input for the produc-
tion of dairy products from different categories, indicating that powder
and butter products require the highest energy input (Flysj¨
o et al.,
2014). Ladha-Sabur et al. (2019) presented the manufacturing energy
demand among different dairy products providing the share of elec-
tricity and fuel demand per product. Based on their data, it can be
observed that fresh dairy products (cheese, fresh milk, butter, cream and
ice cream) had a similar pattern in the energy mix, which was on average
composed of 43.9% electricity and 56.1% fuel utility for the
manufacturing stage. Whilst the long shelf-life products (casein and
lactose, milk powder, whey powder, and concentrated milk) show
similarities in the energy mix as well, which were on average 8.1% of
electricity and 91.9% of fuel on average (Fig. 2b). This increased elec-
tricity ratio of the aforementioned fresh products is due to their exten-
sive refrigeration needs, while the high fuel ratio in the aforementioned
long shelf-life products is due to the multiple processes required for their
production.
Finally, it is worth highlighting the signicant energy demand of the
CIP processes due to the high hygiene standards’ requirements in the
dairy industry. The CIP operations have been reported to utilise 9.5% of
the energy needed for uid milk production, 19% of cheese production
and 26% of butter production (Ramirez et al., 2006). The high pro-
cessing temperature of pasteurisation, burns milk onto the equipment’s
inner surfaces, requiring extended contact times with water and de-
tergents at high temperatures to adequately clean. The reason why the
energy needed for CIP is so high is the consequence of the high tem-
perature of uids required to clean fouled surfaces. For instance, the
thermal energy requirement for the CIP process for a milk pasteuriser in
an average-sized dairy was estimated to be 3.96 GJ/cleaning cycle (Eide
et al., 2003).
3.3. Cold chain energy mapping
Upon leaving the manufacturing plant, the highly perishable nature
of fresh dairy products requires controlled environmental conditions
with an optimal storing and transportation temperature ranging from
0 to 2 ◦C (Mercier et al., 2017). This ensures food quality and safety
between manufacture and consumer purchase. In the food industry, the
cold-chain has a huge impact on the environment, accounting for
approximately 1% of global CO
2
emission and the use of refrigeration
worldwide is responsible for about 15% of the total electricity con-
sumption (James & James, 2010). Fig. 3c provides information about
the energy use in the dairy cold chain. According to a study that eval-
uated the energy consumption of diesel-fuelled refrigeration trucks, it
was found that medium rigid trucks, large rigid trucks and 32-tonnes
articulated trucks consumed on average 2.97 MJ, 1.31 MJ and 0.94
MJ per tonne of products transported and kilometre of distance
respectively (Tassou et al., 2009). According to these estimates, the
energy use per kg of product transported as a function of the distribution
distance for these three types of trucks is presented in the graph of
Fig. 3c. Fig. 3c also includes a further consideration of energy use spe-
cic to the varying duration of cold storage in retail outlets. The infor-
mation for the energy usage for retail storage, was obtained from a study
that has evaluated the fossil fuel usage and carbon footprint per day of
cold storage for different categories of perishable food products (dairy
products included) over a 30-day storage period (Burek & Nutter, 2020).
For instance, for high-temperature short-time (HTST) pasteurised milk
of an average 10-days shelf-life (Lorenzen et al., 2011), the energy use
for cold storage in the retail outlet may be up to 0.2 MJ/kg of milk
(Burek & Nutter, 2020).
3.4. Consumer energy use
In the consumption stage, energy utilisation is mainly required for
transportation from the retail outlets to the households and refrigeration
(Fig. 3d). The average electricity demand per kg of refrigerated milk is
estimated to be 0.35 MJ/kg (Thoma et al., 2013). Regarding the travel
distance for grocery shopping, the average route was estimated to be
equal to 10.9 km per trip with 175 trips taking place annually per 3-per-
son household in the US (Thoma et al., 2013). It is worth mentioning
that unconsumed products wasted in the consumption stage cause in-
direct energy consumption, which is due to all the energy consumed
Fig. 4. Dairy manufacturing plant step-by-step operations for processing fresh dairy products spanning milk, cream, butter, cheese, and powdered products.
M.I. Malliaroudaki et al.
Trends in Food Science & Technology xxx (xxxx) xxx
6
from product manufacture, transportation and refrigeration being
effectively wasted. Every day 1.13 million tonnes of food products are
wasted globally, corresponding to 178 g of food per capita per day (Chen
et al., 2020). This total food waste comprises of 9% from dairy products
on global average, while in high income nations this estimated rate is
much higher, equal to 17% (Chen et al., 2020). Finally, it is important to
emphasise that limited research exists on the energy consumption for
dairy products at the consumer stages, and one may anticipate that this
will vary signicantly due to different global climates and consumer
behaviours.
3.5. Waste in the dairy supply chain
Waste is an important component for understanding and offering
opportunities to improve sustainability, with waste produced
throughout all four stages of the dairy supply chain. Waste increases the
energy use indirectly, due to the energy input required for the produc-
tion of unused material and products, also being wasted. Dairy product
loss accounts for approximately 20% of the total dairy products pro-
duced. The estimation of losses from dairy plant stage to consumption
stage corresponds to 17–30% in the manufacturing stage, 9–12% at the
transportation stage, 2–9% at the retail level, with the highest contri-
bution in waste produced at the consumer level correspond to a share of
53–71% of total dairy products wasted (Brˇ
sˇ
ci´
c, 2020).
3.6. Carbon emissions by the dairy sector
Most studies in the literature that present energy data focus either on
a particular stage in the supply chain or are Life Cycle Assessment (LCA)
studies. The LCA studies take energy use, bovine enteric fermentation
emission and other emissions resources into consideration and estimate
the carbon footprint (Guzm´
an-Luna et al., 2021). An LCA study evalu-
ated all GHG emissions released from the farm, transportation, and
manufacturing stages and identied a summative estimated carbon
footprint for 1 kg of milk as 1 kg CO
2
e, and 1 kg of yogurt as 1.75 kg
CO
2
e, which included the carbon footprint for packaging (Verg´
e et al.,
2013). The same study clearly showed that when considering these three
stages, it is the farm activities that contribute the most GHG emissions
for these two dairy products (86.9% and 72.2% of total GHG emissions
for milk and yogurt respectively). For the manufacturing stage, the
contribution of different utilities between fossil fuels, electricity,
waste/water, chemicals and refrigerants was also determined. This
revealed that fossil fuels and electricity made the largest contribution to
GHG emissions for the manufacturing stage. Specically, fossil fuels and
electricity were found to be responsible for 95% of milk’s and 98.3% of
yogurt’s manufacturing GHG emissions This results in the contributions
of water and wastewater, chemicals, and refrigerants to the product
carbon footprint being comparatively small, corresponding to only 5%
and 1.7% of milk and yogurt manufacturing respectively (Verg´
e et al.,
2013).
The carbon footprint of dairy product manufacturing is attributed
mainly to energy use and is product-dependent. For one study, where
carbon footprint has been equated to the global warming potential
(GWP), milk and cream was estimated to be 0.114 kg CO
2
e per kg of
product (Finnegan et al., 2017), which is a relatively low value due to
the low energy-intensive processes of pasteurisation and separation.
Comparatively, butter and cheese are more energy-demanding products
since they require additional processes such as churning and ripening,
with their carbon footprint estimated to be 0.415 and 0.464 kg CO
2
e per
kg of product respectively. Finally, powdered products, have the highest
carbon footprint due to the need for evaporation and drying, which are
highly energy demanding. Specically, the carbon footprint was
measured at 1.824 kg CO
2
e per kg for milk powders and 2.474 kg CO
2
e
per kg of whey powder respectively (Finnegan et al., 2017).
According to a full LCA study for the milk supply chain from “farm to
fork”, to the total GHG emissions were estimated to be 2.05 kg CO
2
e per
kg of milk (Thoma et al., 2013). This study provided detailed numbers
for the emissions per different GHG emissions source including fuels and
electricity. For the needs of the present study, the focus was turned to the
energy-derived emissions (fuel and electricity), which were processed to
be effectively presented in Fig. 5. This gure presents the carbon foot-
print resulting from energy usage throughout the milk supply chain,
including energy for feed production, activities on farm, product
manufacturing and packaging, product distribution and storage in retail
and consumption energy use. Of the total 2.05 kg CO
2
e GHG emissions
released from “farm-to-fork” per kg of milk, around 0.76 kg CO
2
e result
from energy usage, while the share of emissions per energy source is
comprised of 51.4% of fuel and 48.6% of electricity (Thoma et al.,
2013). From the carton of milk illustration in Fig. 5 that shows the
emissions contribution in the four milk supply chain stages, it is clear
that the cold chain stage has the highest GHG contribution of energy use.
The table provided in Fig. 5 presents the fuel and electricity share
expressed in CO
2
e emissions per supply chain stage: farm,
manufacturing, cold chain and consumption use. It can be concluded
that the farm and the cold chain stage is responsible for substantially
increasing the overall fuel use in the dairy chain. The emissions from the
remaining supply chain stages are mainly derived from electricity usage,
while manufacturing and consumption supply chain stages have the
largest share of electricity-derived emissions.
4. Energy mitigation strategies for the dairy industry
The energy use in the dairy supply chain shows that each supply
chain stage differs in terms of processes and equipment used, energy
Fig. 5. Infographic providing information for the energy use throughout the
“farm-to-fork” supply chain of drinking milk.
[1] Information adapted from the LCA study (Thoma et al., 2013).
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mix, emission production and share in total chain energy utility. For this
reason, the energy mitigation strategies for each stage have a different
focus. Herein, are presented energy-efcient practices and several con-
siderations for innovative technologies, which can address some of the
energy consumption challenges within each stage of the supply chain.
The environmental impact of food products can be reduced by adjusting
farming, manufacturing, distribution and consumption patterns (Roy
et al., 2009). According to LCA studies on dairy products, the most
popular steps include reduction of fuel and electricity use within the
dairy product life cycle, utilisation of energy-efcient process equip-
ment, use of renewable energy resources, and the optimisation of lo-
gistics (Djekic et al., 2014; Üçtu˘
g, 2019). Mitigation practices should be
prioritised according to the energy impact of an operation along the
supply chain. The payback period should be considered and the benets
to the stakeholders and enterprises should be identied to enable
effective adoption.
4.1. Mitigation strategy for the dairy farms
The ever-increasing installation of agricultural technology and
automation allows for close monitoring of milk production and feeding
processes. Moreover, animal tracking allows for the improvement of
cattle, goat and sheep welfare which can lead to increased milk yield and
early identication of diseases (Hansen et al., 2018). Automation in
farms, also ensures improved hygiene which is an important parameter
in dairy’s supply chain food safety. Although those technologies are
highly recommended, the installation of equipment with new technol-
ogy automation may require additional energy input (Todde et al.,
2017). Automation in farms should be coupled with mitigation practices
that can reduce emissions and energy use in farms.
To mitigate the carbon emissions caused by energy use, the energy
reduction practices for dairy farms can be categorised into carbon
removal and emissions reduction practices. The former category is
associated with on-farm energy production and land manage practices
that enable soil carbon storage or sequestration (McEvoy, 2019), while
the latter category, aims to improve the energy efciency of the farm
equipment.
In the former category of carbon removal practices, some of the most
promising practices as indicated from the literature is biogas production
from the digestion of dairy manure and other co-substrates from farm
waste (Gebrezgabher et al., 2012). However, the capital costs for
anaerobic digestion coupled with technical expertise for operation and
maintenance would drive the case for offsite waste treatment and biogas
production, and/or utilising small modular waste to biogas technologies
on-site that are managed via cloud and digital technologies by an
external waste management provider (Fisher et al., 2020). Moreover,
electricity for farm use can also be produced from photovoltaic (PV)
system installation or wind turbines on-site. PV systems and wind tur-
bines can produce electricity with daylight and in the presence of wind
respectively, but such electricity production may not be aligned with
electricity demand due to load shifting which may be addressed with the
installation of batteries to store the surplus energy. However, batteries
may not always be necessary since warm water may act as energy
reservoir for the needs of a farm’s water heating (Breen et al., 2020). It is
important to note, the electricity mix from the grid is completely
exogenous to farms (Verge et al., 2013). Decisions on the national
electricity mix are extremely complex, and way beyond the impact of the
energy used by the whole dairy value chain (Aghajanzadeh & Therkel-
sen, 2019). Finally, the implementation of grassland practices is pro-
posed in order to increase the carbon uptake by sequestering
atmospheric carbon dioxide (FAO & GDP, 2018).
Regarding the latter category, of carbon emissions reduction prac-
tices related to energy use, the literature focuses on energy-conserving
technologies to reduce on-farm energy consumption and carbon emis-
sions. Several energy and cost conserving practices for dairy farms were
recently reviewed and demonstrated promising energy savings
potential. Some of the most common practices where technologies aim
to reduce on-farm electricity consumption, including pre-cooling milk
through a plate cooler, improving hot water tank insulation, and
switching to energy-efcient lighting, etc. (Shine et al., 2020). In any
emissions reduction practices the impact on water utility should be
considered beforehand.
Finally, as regards the growth of the sector, increased organic
farming is recommended since it is less energy consuming compared to
conventional farming per kg of milk produced (Shine et al., 2020). It can
be concluded that with proper mitigation actions, dairy farming can not
only become net-zero but even net-positive, complementing other sec-
tors such as the UK Water Industry operation of municipal wastewater
treatment processes that have moved signicantly from energy negative
to even net-positive (Water UK, 2020).
4.2. Mitigation strategy for dairy manufacturing
Today, manufacturing companies are under pressure to improve
sustainability throughout their systems (Fisher et al., 2021). Dairy plants
appear in great variety both in terms of scale and products manufac-
tured, and thus, net-zero mitigation actions may differ in each type and
scale of dairy plant respectively. The manufacturing stage is highly
energy-demanding due to numerous processes taking place many of
which require high temperature, for processes such as pasteurisation,
evaporation and drying. Dairy manufacturing plants can substantially
reduce their carbon footprint by switching from conventional energy use
to clean forms of energy. Fossil fuels should be replaced by biofuels
while the electricity use is preferable to be derived from cleaner re-
sources such as from wind, solar, hydroelectric, geothermal or nuclear
energy (Rad & Lewis, 2014). On-site electricity production should be
considered where local generation of electricity is feasible. Broader
mitigation could be achieved more generally by a shift in the overall grid
energy mix towards renewable sources alongside nuclear energy.
Some of the common practices followed by dairy manufacturers that
can substantially reduce the energy use, is the installation of heating and
cooling regenerators and energy-efcient equipment, the insulation of
heating and cooling processing equipment, optimising combustion ef-
ciencies in steam and hot water boilers and xing steam leakages (Rad
& Lewis, 2014). There is an increasing focus on emerging technologies to
reduce energy use in dairy production (Martins et al., 2019). The
non-conventional technologies to replace conventional heat treatment,
such as pasteurisation, are categorised into thermal and non-thermal
treatment processes. Microwave (MW) and radio frequency (RF) are
two of the most promising non-conventional thermal treatment pro-
cessing methods. Both MW and RF use electromagnetic energy to pro-
vide instantaneous volumetric heating, overcoming heat transfer
limitations to achieve higher heating rates (Martins et al., 2019). Given
that the capital cost of such technologies is signicantly higher, the
denition of the value proposition is key for successful application. The
fact that MW and RF are promising technologies for improved product
quality is also a common benet, however as yet, very few bulk heating
MW and RF applications have achieved commercial success (Kingman,
2018).
Non-thermal techniques proposed in the literature to replace heat
treatment processes are ultrasound (US), high-pressure processing
(HPP) and Pulsed Electric eld (PEF). The efciency of US in milk
pasteurisation has been evaluated and shows promising results in terms
of food safety and energy efciency (Kotsanopoulos & Arvanitoyannis,
2015). HPP is increasingly used in the food industry for value-added
products (Chawla et al., 2011), whilst non-thermal techniques provide
the advantage of efcient preservation of milk nutrients using less en-
ergy consumption compared to conventional treatment (Martins et al.,
2019). Again, the value proposition scenarios for non-thermal processes
must be critically evaluated to corroborate such claims at industrially
relevant scales.
Waste management plays an important role in energy savings and
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sustainability (Kazancoglu et al., 2018). Dairy manufacturing plants
produce huge volumes of waste streams. Circular economy thinking,
building on the waste hierarchy, provides opportunities to reduce dairy
industry waste or valorise it through reusing, recycling and recovery
operations (Fisher et al., 2021). Key waste streams in dairy
manufacturing include whey, however avoidable wastes such as leaks,
spillages, spoilage and equipment cleaning discharges are important to
consider (Zero Waste Scotland, 2020). Cleaning-in-Place (CIP) technol-
ogies have a critical part to play in improving cleaning efciency,
reducing not only energy use but also overall water used and therefore
waste generated. In fact, the installation of an energy-efcient CIP sys-
tem can reduce cleaning cost by approximately 35% and cleaning energy
use by 40% (Marriott et al., 2018).
For valorisation, biotechnological approaches have been shown to be
applicable for the production of biopharmaceutical products, whey-
derived food products and bioplastics (Ahmad et al., 2019). Moreover,
there are opportunities for energy generation by producing biofuels
from dairy plant waste. For example, dairy waste can be used as sub-
strate for ethanol production using yeast, and high-strength efuent
streams can be used for methane recovery via anaerobic digestion.
Ethanol and methane can then be utilised by the manufacturing plant as
a supplementary fuel supply (Ahmad et al., 2019; Rad & Lewis, 2014). In
addition, electricity can also be produced from waste streams via bio-
electrochemical processes by employing microbes as catalysts (Fisher
et al., 2021).
4.3. Mitigation strategy for the cold chain
The cold chain requires signicant changes to improve energy sus-
tainability. The use of energy-efcient and carbon-free refrigeration
technologies in all cold chain stages can boost the efciency of the cold
chain resulting in less CO
2
e emissions (James & James, 2010). Cold
chain logistics plays a crucial part in product’s food safety in the supply
chain and research focus is turned on new technologies such as the
Internet of Things (IoT) for product monitoring (Shashi et al., 2018).
Traceability in the cold chain brings multiple benets to the cold chain’s
functionality while it can aid energy use reduction. The contribution of
logistics in the energy utility of the dairy supply chain is important since
dairy production is largely centralised, meaning that the transportation
distances for distribution are signicant (Ladha-Sabur et al., 2019).
Thus, as the sector grows, a move to de-centralisation comprising
shorter distribution routes could be considered. Minimizing the distance
by optimising the distribution routes will reduce fuel consumption and
refrigeration needs. One model proposed aims to optimise the energy
demand of refrigerated distribution routes by minimizing transportation
routes, whilst accounting for ambient temperature variations (Accorsi
et al., 2017). More specically, since high-ambient temperature requires
increased energy for refrigeration, they suggested the consideration of
the weather conditions during transportation, as well as trafc conges-
tion during the day (Accorsi et al., 2017). Additionally, the energy ef-
ciency of refrigerated trucks can also be improved, by re-designing the
diesel-fuelled compressors and installing better insulation (Accorsi et al.,
2017). Regarding the retail stage, products are recommended to be kept
in fridges with enclosed doors, as they can offer better refrigeration and
energy savings up to 68% when compared to current standards of
open-door fridges (de Frias et al., 2020).
Consideration should be given in replacing fresh dairy products with
their non-refrigerated processed dairy alternatives which will not spoil
when stored at ambient temperature for up to 6–9 months (Guzm´
an--
Luna et al., 2021). This will substantially decrease the energy load on the
cold chain, however their processing is more intensive and thus more
energy is required in manufacturing. These products may be more
environmentally sustainable options, but this can only be proved
through full LCA studies. For instance, UHT (Ultra High Temperature)
pasteurised milk has been reported that it may have a lower energy
consumption (Djekic et al., 2014) but also reported as having a higher
energy consumption (Nicol, 2004) compared to regular pasteurised milk
along their life-cycle. It is worth mentioning that UHT milk is more
common in warmer climates, and this is because of the high energy cost
associated with refrigeration (Mercier et al., 2017). This implies that
with climate change and increased temperature, countries that currently
prefer fresh dairy products may need to swich to long shelf-life products.
However, the preference in dairy products is very dependent on con-
sumer behaviours in a specic region and do not change immediately
because a less energy intensive product alternative becomes available
(Macdiarmid, Douglas, & Campbell, 2016).
4.4. Mitigation strategy for consumption use
To improve sustainability of the dairy supply chain, waste produc-
tion and energy use at the domestic stage should be minimised. Both
goals can be achieved by increasing public perception and their envi-
ronmental awareness and adapting their consumption behaviour. Con-
sumers should be encouraged to make use of low carbon footprint
transportation, use energy-efcient appliances such as fridges in the
household and minimise all types of consumer waste. Also, smart fridges
are suggested which have recently been introduced to the market and
are able to track the shelf-life of products preventing food wastage
(Kumar & Chimmani, 2019). Moreover, to enhance sensitivity about a
product’s energy consumption, one effective approach is to place a fuel
economy label on the products indicating the energy requirements of the
product along the supply chain, in a familiar style to the consumers unit
such as equivalent light-bulb minutes (Camilleri et al., 2019).
Overall, consumers, have a great impact on the energy demand of the
food and dairy supply chain, though it can be considered more chal-
lenging to raise environmental awareness of consumers, compared to
industrial stakeholders involved in the other stages of the chain who are
conscious of their corporate images and government environmental in-
centives. Thus, positive changes in consumer behaviour could contribute
signicantly to the overall net-zero carbon target.
5. Challenges in the implementation of energy mitigation for the
dairy industry
Moving towards net-zero carbon emissions in the dairy sector is a
continuous process of reconstruction that is time-consuming. This is not
only attributable to the actual time required for the mitigation practices
to be applied but primarily because industry and society have to develop
environmental awareness and place the environment at the top of their
priorities. The dairy sector can substantially reduce their carbon emis-
sions by proper resource management spanning energy to waste. How-
ever, due to the interconnectivity between the stages, this cannot be
necessarily met on a stage-by-stage basis but rather from a holistic
perspective. More specically, some supply chain stages can become
carbon net-positive, which means that their actions go beyond achieving
net-zero carbon emissions. This can be achieved through appropriate
energy management and by investing in bioenergy production
(Gebrezgabher et al., 2012). This way, even if it is unrealistic to expect
the carbon emissions of some stages to be reduced to zero there is the
potential of the overall supply chain’s carbon emissions to substantially
decrease, and move towards net-zero carbon levels. Nevertheless, this is
a multistep process that requires an in-depth analysis of the carbon
emission performance of each step of the supply chain and evaluation of
the contribution of each stage to the entire chain. The major challenge
arising from meeting the net-zero carbon target under a holistic
perspective is how all sectors can unite and work altogether within that
scope.
Another emerging signicant challenge for the dairy sector is climate
change. Climate change will affect all supply chain stages, causing the
whole sector to be at risk. At the farm stage, adverse climatic conditions
might lead to lower crop-yield caused by reduced land productivity,
while the increased ambient temperature and humidity levels may cause
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Trends in Food Science & Technology xxx (xxxx) xxx
9
heat stress in cows. This could be addressed by developing new stan-
dards for the animal’s living conditions and installing cooling systems
(Harrison et al., 2017). At the manufacturing stage, the possible devel-
opment of heat-resistant foodborne pathogens in raw milk arriving from
farms, may need to be addressed by intensifying the standard heat
treatments (Feliciano et al., 2020; van Asselt et al., 2017). However,
under the existing processing technologies, more intensive heat treat-
ment to address the arising food safety concerns would incur increased
energy demand (Augustin et al., 2013). As regards the cold chain and
consumption stage, food products may be required to be stored at lower
refrigeration temperature than usual to prohibit the microbial growth to
dangerous-for-consumption levels (James & James, 2010). This, in
addition with the expected higher heat loads in the cold-chain systems
due to increased ambient temperature will increase energy for product
refrigeration. Fig. 6 illustrates the main climate change impacts per
supply chain stage, and presents adaptation actions to address climate
change impact, while outlining the main energy mitigation practices
that can be put in place to address the expected growth in energy de-
mand. The dairy sector not only has to adapt to climate change but also
to respond to the expected increased global food demand arising from
population growth. This will inevitably lead to a respective substantial
increase of energy demand across the supply chain if energy mitigation
practices are not applied throughout the entire sector.
6. The role of supply chain energy modelling towards net-zero
carbon emissions
Today, environmental sustainability is a top priority in the agenda of
every dairy company and should always be considered along with the
social and economic impacts of dairy production. To simulate complex
systems within the dairy supply chain and assist the decision-making
process towards more sustainable decisions, computational models
have signicant role to play. A computational model, or model as it is
referring to in the following section, is the representation of a systems
through mathematical expressions, equations and algorithms (Calder
et al., 2018).
Modelling plays a growing role in many industrial sectors allowing
the assessment of parameters such as the cost, the process efciency for
individual unit operations (such as a processing device for dairy
manufacturing) or systems (e.g. a wastewater treatment plant) (Fisher
et al., 2021). A step further is the development of models which incor-
porate numerous or all components of a supply chain. Such models will
allow the quantication and optimisation of parameters of interest (e.g.
energy utilisation or carbon footprint) from a holistic viewpoint.
Focusing on the improvement of energy management of the dairy supply
chain, the supply chain energy models (SCEMs) are proposed as an
efcient means to organise such broad systems and assess their energy
use. A SCEM, is a synthesis of individual models that quantify the energy
use for each operation in sequence taking place from farm-to-fork along
a supply chain, to create a holistic energy model. The individual energy
models should be combined in order to full the mass balances along the
chain, while accounting for product losses and waste. A SCEM will
require quantitative and qualitative characteristics for each supply chain
stage as inputs. For example, for the farm stage inputs may be the type of
farming, and the number of cows. For the manufacturing stage, inputs
may be the production scale and type of products produced. For the cold
chain, the type of trucks and the transportation distance. Finally, for the
retail outlet the temperature of refrigeration and the days of storage. The
SCEMs can provide as output the supply chain energy use, the energy
mix and the product embodied energy.
The development of SCEMs will be critical to achieve efcient energy
management. Such models can be used for benchmarking the energy
utility across the dairy sector allowing the identication of the most
energy-demanding operations (Yakovleva et al., 2012). In this stage,
experts and engineers could consider alternative energy-saving opera-
tions across the sector to address the identied hotspots. Moreover, the
ability to acquire detailed data on energy use, will become extremely
useful as inputs in LCA studies, due to the current lack or condentiality
of accurate data related to energy consumption (Rahimifard et al.,
2010). The LCA studies will allow the estimation of the carbon footprint
of the dairy supply chain and assess mitigation plans that can move the
dairy sector towards net-zero carbon.
Supply chain energy models can prove to be benecial in predicting
possible collaborating actions between the actors of the supply chain
within the context of industrial symbiosis (Kastner et al., 2015). For
example, to achieve the net-zero carbon target through the entire supply
chain, these models can be used to test the efciency of collaborating
actions by assessing which supply chain stages are net-negative and
which are net-positive. In the future, the dairy industry will undergo
radical changes due to the emerging global changes of climate change
and population growth. For deciding whether an adjustment measure
will be effective or not, SCEMs can be used to project this measure under
future conditions and examine their stability at different timescales.
Specically, climate change effects can be simulated, by considering
Fig. 6. Climate change impacts, climate change adaptation actions and energy mitigation practices for the dairy supply chain.
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projected environmental conditions in different regions and times. For
example, global warming will result in changes in average ambient
temperatures, and thus simulations can be made to project the conse-
quences on energy usage along the dairy supply chain using different
ambient temperatures as model inputs.
An upcoming challenge for the dairy sector is that dairy production
will have to grow to meet increased food demand, which is a conse-
quence of population growth and increasing consumer desire. SCEMs
can be used by dairy companies that aim to grow their production, in
order to simulate future growth scenarios and decide which is the most
energy-efcient plan to follow. For example, one growth plan for a
company could be to build a network of small dairy production plants
distributed across a geographical region, which will reduce trans-
portation energy use and enhance the local development of the agri-
cultural sector (Gimenez-Escalante & Rahimifard, 2018; Ladha-Sabur
et al., 2019). In other cases, the best growth plan might be the expansion
and technology up-grad of an existing dairy production plant. In this
case, although the energy use for distribution will be demanding, the
energy efciency of the production plant will be improved since larger
dairies tend to require less energy per unit of product (Xu et al., 2009).
The SCEM’s will indicate which growth plan will lead to an overall
decrease in the energy consumption for the supply chain.
SCEMs can also prove to be extremely useful for unexpected situa-
tions, such as the Covid-19 pandemic which could be simulated to
observe the stability and effectiveness of any possible measure and
consider any immediate action (Rizou et al., 2020). Overall, SCEMs al-
lows close monitoring of the entire supply chain and can provide useful
information and many opportunities for optimisation in terms of sus-
tainability of the supply chain. The development of tools available to all
the supply chain members, can enhance the efciency of common sus-
tainability goals and help make decisions with greater certainty (Bran-
denburg et al., 2014).
6.1. Considerations for the development of SCEM
Any energy reduction actions to be adapted for a supply chain re-
quires decision-making through well-established planning. A compre-
hensive understanding of complex systems such as the energy
consumption in supply chains begins with a holistic quantication of the
energy use and the interactions between the supply chain stages
(Namany et al., 2019). Thus, for decision-making, trustworthy SCEMs
should be developed.
Models for planning agricultural supply chains can be categorised as
stochastic or deterministic, depending on whether uncertainty is
considered or not respectively (Ahumada & Villalobos, 2009). Models
are usually structured in a suitable way in order to be optimised. The
most commonly used optimisation approaches for stochastic models are
stochastic programming (SP), stochastic dynamic programming (SDP),
simulation (SIM), risk programming (RP) while the most common
deterministic approaches are linear programming (LP), dynamic pro-
gramming (DP), mixed integer linear programming (MILP), and goal
programming (GP) (Ahumada & Villalobos, 2009). These optimisation
approaches differ on how the objective function depends on the decision
variables. Mixed-integer linear programming has been observed as the
most common modelling method for supply chain sustainability models
(Nematollahi & Tajbakhsh, 2020). It is important to note that the supply
chain network encompasses a huge variety of parameters and may
present multiple risks. Supply chain models are mainly used to assist
with strategic decisions that are usually long-term. Not incorporating
this variability and risk in the models may lead to unsafe decisions
(Baghalian et al., 2013). Thus, accounting for uncertainty in SCEM is
highly recommended. Stochastic programming (SP) is the most
commonly used optimisation approach for problems that incorporate
strategic and operational randomness (Namany et al., 2019). SP con-
siders the probability distribution of potential outcomes which allows
the inclusion of the variability and risk of real-life problems and can
create solutions that are robust to uncertainty (Ghadge et al., 2017).
Decision-making in a supply chain problem may depend on single or
multiple objectives (or criteria), which is called single objective
decision-making (SODM) and multi-objective decision-making (MODM)
respectively (Allaoui et al., 2018). In SODM the objective is to minimise
one important parameter of the problem, which in a SCEM will be the
total energy use of a supply chain. SODM can also be used to achieve
multiple objectives, by setting constrains to the optimisation problem.
For example, for a SCEM this can be the minimisation of the energy use,
with an upper limit of cost. Alternatively, when there is the need to
optimise a supply chain from several different aspects, such as cost, time
and energy, MODM is used. The aim of MODM being to provide the most
efcient solution and ensure transparency in the decision-making pro-
cess (Triantaphyllou, 2000).
In order to trust the solution of a SCEM and perform decision making
with condence, it is essential to test the results’ sensitivity on highly
uncertain parameters by undertaking sensitivity analysis (SA) (Bagha-
lian et al., 2013; Yakovleva et al., 2012). Sensitivity analysis helps
analyse the robustness of a model by investigating the inuence of a
model’s input parameters on the model output variables of interest.
It is not always possible to use a SCEM to determine the optimal
energy mitigation strategy since such models may have a complex
structure that is not compatible with established optimisation ap-
proaches (e.g. LP, MILP etc.) to be able to generate a unique best case. In
such cases, a scenario analysis approach can be used. Scenario analysis is
the simulation and comparison of different realisations of the supply
chain under different hypotheses. For example, several different future
climate change scenarios for the dairy supply chain can be simulated and
then compared in terms of their energy performance, their cost and
other criteria. It is then upon the decision-makers to choose which of
these scenarios is the most suitable to be implemented (GFS, 2021).
Although scenario analysis can bring out the most sustainable decisions
holistically, this does not mean that it will be the best and most prot-
able choice for each individual actor. When several actors of the supply
chain have to make a common decision, game theory can aid the
decision-making process and lead to a common solution that works
equally for all the actors of the chain (Namany et al., 2019).
Importantly, SCEMs require system-relevant data that is available,
and technological advances to be able to map the energy use along the
chain. The rapid development of a new generation of information
technology provides the ability to digitize and visualize data of food
supply chains (Han et al., 2020). Specically, cloud computing, block-
chain, Industry 4.0 and the Internet of things (IoT) are technologies that
have been increasingly adopted by some food supply chain actors
especially in manufacturing, and they offer the ability to obtain and
exchange data to improve efciency on automation, product improve-
ment and material management (Fisher et al., 2021). Such technologies
can allow the analysis and visualisation of energy use and carbon
emission into on-demand services and assist the decision-making pro-
cess in supply chain monitoring (Fisher et al., 2018). For example, IoT
allow the collection of multi-source data through sensing technology
along the supply chain by using sensors that capture and transmit data
through communication technologies (e.g. 5G or the internet). To cap-
ture the energy use along the supply chain such sensors could measure
the electricity use of processing systems, the refrigerating temperature,
the location of products along the chain and more. Such information can
help the trackability of the products’ embodied energy and carbon
emissions along the supply chain. Specically, through blockchain
technology, each product’s supply chain can be tracked and specic
information on the energy use per product can be provided through
energy labelling on the products. Also, through blockchain technology,
the most energy-efcient supply chain sequence can be revealed and
used as a prototype for future reconstruction of the supply chain.
Table 1 presents some valuable work undertaken in the eld of SCEM
and closely relevant areas that can be used as implemented examples of
SCEM. For each study presented, the food product under analysis, the
M.I. Malliaroudaki et al.
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Table 1
Application of supply chain models in individual and multiple supply chain stages for the dairy industry and related areas.
Title Type of
product
Model Inputs Model Outputs Supply chain
stages included in
model
Technical
approach
Research focus and
objective
Authors and year
Environmentally
friendly
management of
dairy supply chain
for designing a
green products’
portfolio
Dairy
product: curd
Number of
suppliers and
markets, Distances,
manufacturing
volume, product
quantities
Economic and
environmental
performances
Entire supply
chain
Single criterion
decision-making
(SCDM)
Optimisation of
prot and
environmental
impact for design of
“green” products’
portfolio of a supply
chain for curd
production
Kirilova and
Vaklieva-Bancheva
(2017)
Energy-neutral dairy
chain in the
Netherlands: An
economic
feasibility analysis
Biogas
production
50% Manure 50%
other (energy
maize, grass silage
and other co-
substrates)
Green gas, Digestate
investment costs
Dairy farm and
manufacturing
Monte-Carlo
Sensitivity analysis
A simulation model
aiming to achieve
energy-neutral
chain from dairy
farm to factory.
Gebrezgabher et al.
(2012)
Environmental
impact of future
milk supply chains
in Sweden: a
scenario study
Dairy
product: Milk
Material ow,
Chemical
composition,
Physical properties
Contamination by
heavy metals
Net energy and
environmental
impacts
Entire supply
chain
Life cycle
assessment (LCA)
Scenario analysis
Environmental
impact analysis of
future supply chains
for dairy products, a
scenario technique
was chosen.
Sonesson and Berlin
(2003)
Dairy waste-to-
energy incentive
policy design using
Stackelberg-game-
based modelling
and optimisation
Dairy manure
and waste
Farm’s
characteristics,
weight of manure
minimizing total
government
intervention and
minimizing its unit
cost on generating a
target amount of
bioelectricity.
Dairy farm,
biogas production
plant
Game theory:
Single-leader-
multiple-follower
Stackelberg game
Two conicting
objectives,
minimizing total
government
intervention, and
minimizing its unit
cost on generating a
target amount of
bioelectricity.
Zhao and You
(2019)
Selecting new
product designs
and processing
technologies under
uncertainty: Two-
stage stochastic
model and
application to a
food supply chain
Dairy
product: Milk
powder
production
technologies
Cost of raw
material,
transportation
cost, demand,
waste fraction,
technologies used
Selection of new
product designs and
processing
technologies in a
supply chain
context.
Manufacturing:
Processing
technologies’
impact on the
entire supply
chain
Two-stage
stochastic mixed
integer linear
programming
(MILP) model
An assessment of
new product
technologies in a
supply chain
context Which may
lead to extensive
energy savings in
production.
Stefansdottir and
Grunow (2018)
An optimisation
approach for
managing fresh
food quality
throughout the
supply chain
Fresh food Transportation
Distances, storage
temperature,
transportation
time, Shelf life
Quality indicators,
Transportation cost
Manufacturing
and cold chain
Mixed-integer
linear
programming
(MILP)
Modelling the
production and
distribution in a
food supply chain
using food quality
as an indicator,
which is strongly
related to
temperature
control.
Rong et al. (2011)
A case analysis of a
sustainable food
supply chain
distribution
system—A multi-
objective approach
Dairy
product: milk
Characteristics of
two processing
plants with twenty-
two drop off points
Carbon footprint
measured in CO
2
emissions and costs
Cold chain:
Distribution
Multi-objective
decision-making
(MODM) Multi-
objective
optimisation using
Pareto fronts. A
multi-attribute
decision-making
approach
Minimises CO
2
emissions from
transportation and
total costs in the
distribution chain.
Design of a
capacitated
distribution
network.
Validi et al. (2014)
On the sustainable
perishable food
supply chain
network design: A
dairy products case
to achieve
sustainable
development goals
Perishable
products:
milk products
Number of
products shipped,
type of product,
distances of routes
etc
Total supply chain’s
present value,
Vehicles’ fuel
consumption, social
inuence.
Cold chain:
Distribution
Multi-objective
mixed integer
programming
(MOMIP) -Multi-
objective decision-
making (MODM)
Optimisation using
goal programming
(GP) Uncertainty
analysis
Optimisation the
cost, the energy
consumption, and
the trafc
congestion for
multiple products
with different
properties,
including
perishability,
weight, and price.
Jouzdani and
Govindan (2020)
Chilled or frozen?
Decision strategies
for sustainable
food supply chains
Perishable
food products
Storage
temperature and
storage time
Optimal
combination of
energy use and
quality degradation
of food
Cold chain:
Distribution and
storage of frozen
food
Multi-Objective
Decision Making
(MODM)
Cold chain
optimisation by
introducing energy
as a key factor.
Zanoni and
Zavanella (2012)
(continued on next page)
M.I. Malliaroudaki et al.
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12
inputs and outputs of the model, the supply chain stages modelled, the
technical approach used and a short description of the research focus,
and the main objective of the study are provided. The models are or-
dered starting from those accounting for the entire supply chain or
multiple stages and then follow the supply chain sequence starting from
farm, then manufacturing and then cold chain.
Most of the reviewed articles summarised in Table 1 focus on the
dairy sector, while only a few present models in the general category of
perishable food products (Accorsi et al., 2017; Rong et al., 2011; Zanoni
& Zavanella, 2012). Several papers present models that deal with the
entire supply chain (Kirilova & Vaklieva-Bancheva, 2017; Rong et al.,
2011; Sonesson & Berlin, 2003) as they recognise the importance of
optimising the variables of interest such as carbon footprint, sustain-
ability, or energy use holistically, throughout the entire supply chain. In
addition, in some papers the developed models aim to minimise the
energy use, while some of them aim to optimise the cost as well use
multi-objective methods (Accorsi et al., 2017; Jouzdani & Govindan,
2020; Zhao & You, 2019). Last but not least, some studies addressed
sustainability (Kirilova & Vaklieva-Bancheva, 2017; Sonesson & Berlin,
2003; Validi et al., 2014) by optimising CO
2
emissions, while others
aimed to optimise food quality (Rong et al., 2011). The most relevant
study to the idea of net-zero carbon aimed to achieve an energy-neutral
dairy supply chain developed a model which estimated the biogas from
dairy farm manure required to create a self-sufcient dairy supply chain
(Gebrezgabher et al., 2012). Also, they carried out a sensitivity analysis
on their model to check for the robustness of their model in terms of cost
and revenue. From Table 1 it can be seen that a range of different
modelling methods have been used and within the area of SCEM in the
food, and specically dairy sector, no preferred modelling method has
been identied by the research community.
Regarding the modelling methods applied in the reviewed models; 4
out of 10 studies followed a multi-objective decision making approach
(Jouzdani & Govindan, 2020; Validi et al., 2014; Zanoni & Zavanella,
2012; Zhao & You, 2019) and 3 out of 10 studies have used
mixed-integer linear programming (MILP) for optimisation (Accorsi
et al., 2017; Rong et al., 2011; Stefansdottir & Grunow, 2018). Inter-
estingly, a multi-objective problem where both expenditure and envi-
ronmental impact needed to be optimised, was constructed as a
single-objective optimisation problem by using cost as a common mea-
sure to rank both the expenditure and the environmental performance
(Kirilova & Vaklieva-Bancheva, 2017). All in all, research is growing in
the SCEM area with the majority of papers published within the last
decade (2011–2021). The papers presented in Table 1 can inspire re-
searchers to develop SCEMs, which can signicantly contribute towards
the net-zero carbon target.
7. Conclusion
All sectors around the globe will have to reach net-zero carbon
emissions levels by 2050–2070 according to the Paris Agreement.
Companies and stakeholders who adjust to the net-zero target sooner
rather than later, can signicantly reduce the downside risks during net-
zero adaptation, while leading on environmental sustainability. With
the dairy sector as a signicant energy intensive food sector and
alongside the requirements of environmental sustainability, reduction of
energy use is essential. The “net-zero carbon” is the ultimate target,
however it is extremely challenging for sectors such as the dairy in-
dustry, where zeroing the overall net carbon emissions is almost
impossible to implement directly. This is because the current technology
and practices is unable to tackle the huge source of emissions derived
from bovine enteric fermentation. Although net-zero carbon is an
important target, not all supply chain stages in the dairy sector can reach
net-zero carbon levels. The dairy industry will more efciently move
towards net-zero carbon via collaborative actions between the four dairy
supply chain stages (farm, manufacturing, cold chain and domestic use)
within the context of industrial symbiosis.
To provide a holistic overview of the opportunities for moving to-
wards net-zero carbon levels through energy mitigation, this paper
rstly allocates the energy use along the entire dairy supply chain and
subsequently presents energy mitigation actions. The increasingly
alarming effects of climate change and global population growth will
make the net-zero carbon challenge even more difcult since energy
demand will increase even further. Nevertheless, mitigation actions
without validation pose considerable uncertainty for the industry and
the stakeholders/enterprises. In order to address those risks and make
the reconstruction process more efcient, this paper proposes the
development of supply chain energy models. Such models will be able to
project the energy use across the dairy supply chain and indicate the
most energy demanding operations, enabling the decision-makers to
prioritise the energy conservation and net-zero mitigation actions.
Furthermore, they can project future climate change scenarios of the
food and dairy supply chain as well as the short- and long-term sus-
tainability of the supply chain. Supply chain energy models will become
essential to the industry since they will be able to indicate, with a great
degree of certainty, which are the optimal decisions on a nancial basis
and with regards to environmental sustainability. Overall, the devel-
opment of modelling tools able to simulate energy demand, assess en-
ergy reduction practices and project the future operating conditions
under various climate change scenarios can substantially contribute to
industrial and environmental sustainability and improvement of the
dairy and any other food sectors.
Acknowledgments
This project is part of the PROTECT ITN (http://www.protect-itn.eu/
) which is funded under the European Union’s Horizon 2020 research
and innovation programme under the Marie Skłodowska-Curie grant
agreement No. 813329.
Table 1 (continued )
Title Type of
product
Model Inputs Model Outputs Supply chain
stages included in
model
Technical
approach
Research focus and
objective
Authors and year
A climate driven
decision-support
model for the
distribution of
perishable
products
Perishable
food products
Product’s prole,
Vehicles features,
Network and
Nodes, Packaging
characteristic,
Weather forecast
or historical
climate proles
Optimal
temperature at the
warehouses,
Optimal routes
management,
Energy-effective
operations, Proper
packaging solutions,
Delivery scheduling
according to the
optimal weather
conditions
Cold chain Mixed-integer
linear
programming
(MILP)
Modelling the
refrigerated
distribution of
perishable products
which incorporates
climate
considerations in
the management of
cold chain
operations.
Accorsi et al. (2017)
M.I. Malliaroudaki et al.
Trends in Food Science & Technology xxx (xxxx) xxx
13
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