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Food Retail Supply Chain Resilience and the COVID-19 Pandemic:
A Digital Twin-Based Impact Analysis and Improvement Directions
Diana Burgos, Dmitry Ivanov*
Berlin School of Economics and Law
Department of Business and Economics
Supply Chain and Operations Management Group
10825 Berlin, Germany
Phone: +49 30 85789155
e-mail: divanov@hwr-berlin.de
* Corresponding author
Abstract
In this study, we examine the impact of the COVID-19 pandemic on food retail supply chains
(SCs). Based on real-life pandemic scenarios encountered in Germany, we develop and use a
discrete-event simulation model to examine SC operations and performance dynamics with the
help of anyLogistix digital SC twin. The computational results show that food SC performance
at the upheaval times is triangulated by the pandemic intensity and associated
lockdown/shutdown governmental measures, inventory-ordering dynamics in the SC, and
customer behaviours. We observe that surges in demand and supplier shutdowns have had the
highest impact on SC operations and performance, whereas the impact of transportation
disruptions was rather low. Transportation costs have spiked because of chaotic inventory-
ordering dynamics leading to more frequent and irregular shipments. On bright side, we observe
the demand growth and utilization of online sales channels yielding higher revenues. We
propose several directions and practical implementation guidelines to improve the food SC
resilience. We stress the importance of SC digital twins and end-to-end visibility along with
resilient demand, inventory, and capacity management. The outcomes of our study can be
instructive for enhancing the resilience of food SCs in preparation for future pandemics and
pandemic-like crises.
Keywords: supply chain resilience, food supply chain, COVID-19 pandemic, digital twin,
simulation
1. Introduction
Epidemic outbreaks and associated pandemics are a specific case of supply chain (SC)
disruption as they can spread rapidly and disperse worldwide. Recent examples include SARS,
MERS, Ebola, swine flu, and most recently, the novel coronavirus (COVID-19/SARS-CoV-2)
(Choi 2020, Govindan et al. 2019, Ivanov 2020a, Paul and Venkateswaran 2020, Queiroz et al.
2020, Chowdhury et al. 2021, El Baz and Ruel 2021, Sodhi et al. 2021). In this study, we analyse
the pandemic impacts on food retail SCs in Germany.
The novel coronavirus, first reported in Wuhan, China, in December 2019, expanded rapidly
worldwide and was first detected in Germany at the end of January 2020. A pandemic was then
declared in March 2020. The COVID-19 outbreak has profoundly impacted human lives and
economic activities worldwide, affecting global SCs across many industries by reducing access
to markets and materials, leading to significant operational and financial impacts (Dolgui et al.
2020b, Veselovská 2020, Choi 2021, Ivanov 2021b, Nagurney 2021).
Among others, SCs in the food industry have experienced unprecedented shocks during the
COVID-19 pandemic (Chowdhury et al. 2020, Loske 2020, Singh et al. 2020). The crisis has
also affected many different sectors in Germany, leading to drastic falls or spikes in demand.
Hence, Germany’s Gross Domestic Product (GDP) decreased by 6% during 2020 compared to
the previous year (IMF 2020). Retail workers have been significantly affected – many shops
have been closed because of pandemic mitigation measures, and as other industries have slowed
down, consumption of certain goods has declined. On the other hand, food retailers and grocery
store workers have seen a surge in demand as people in confinement buy food and other
necessities, often stocking up for long periods of isolation (Chinn et al. 2020, ILO 2020). In this
way, as the COVID-19 pandemic has advanced, particular attention has been given to food
retailers. They have had to adapt quickly and respond to the crisis given their critical role in
providing daily-life essentials (Ivanov 2020b, Ivanov and Dolgui 2020a).
Some of the most visible implications of the pandemic in the food retail industry have included
panic buying, changes in food purchasing patterns, food deliveries and digital services, frontline
hygiene and preventive measures, logistics and organization of distribution in stores, and
supply-side issues due to labour shortages and disruptions to transportation and supply
networks (ILO 2020, PwC 2020, Rathore et al. 2020, Ivanov 2021c, Paul and Chowdhury
2021). Limits on people’s mobility have reduced seasonal workers’ availability for planting and
harvesting in many countries. For example, in the wake of the COVID-19 pandemic, many
producers/suppliers could not harvest fruits in the UK primarily because of labour shortages,
which led to large-scale food loss and waste (The Guardian, 2020).
Moreover, COVID-19 has led to disruptions in food processing industries, which have been
affected by social distancing rules and other measures aiming to contain the spread of the virus,
thus reducing operations’ efficiency. Similarly, bottlenecks in transport and logistics have
disrupted the movement of products – which are transported using three main modes of
transportation: bulk (ships and barges), containers (by boat, rail, or truck) and other road
transport, and air freight – along SCs (OECD, 2020). At the same time, according to Chinn et
al. (2020), food retail in Germany was able to seize new post-crisis opportunities. Thus, the
food retail industry has not only faced structural challenges during the COVID-19 crisis, it has
also grown during the crisis.
Despite the ongoing research on disruptions management associated with the COVID-19
pandemic, little is known about the impacts of the pandemic on food SCs, the reasons for these
impacts, and the most promising directions for SC recovery. Moreover, the partial or even full
shutdown of whole industry sectors and regions represents a novel and underexplored setting
in SC resilience literature (Ivanov and Das 2020). While some research has examined the initial
impact of the pandemic on food SCs (Chowdhury et al. 2020, Loske 2020, Singh et al. 2020),
the existing literature lacks overarching insights based on real-life pandemic scenario analysis
over a longer time scale that includes several pandemic waves and the associated disruptions
and recovery phases (Hosseini et al. 2019, Aldrighetti et al. 2021, Ghadge et al. 2021, Ivanov
2021a). We have developed this study to close these research gaps.
We examine the impact of the COVID-19 pandemic on the food retail supply chains. Based on
real-life scenarios encountered in German food supply chains during the COVID-19 pandemic,
we have developed and used a discrete-event simulation model to examine the impact of the
pandemic on supply chain operations and performance. Previous research has provided strong
and substantial insights about how to evaluate, measure, and improve resilience in SCs using
simulations (Wilson 2007, Carvalho et al. 2012, Ghadge et al. 2013, Schmitt et al. 2017,
Macdonald et al. 2018, Currie et al. 2020, Ivanov and Dolgui 2021, Llaguno et al. 2021). For
example, Aguila and ElMaraghy (2020) have suggested an evaluation framework for SC
resilience using system dynamics simulation. In the first phase, their SC simulation model is
constructed and used to determine the SC performance in different scenarios. Then, potential
disruptions and possible mitigation strategies and configurations are identified. Finally, the
disruptions that have the most significant impact on the SC are proposed to build scenarios for
analysis. However, neither this nor other works have captured the real-life pandemic scenarios
in the food SC over a long period of time or considered two pandemic waves – distinct and
substantial contributions made by our study.
Our study makes substantial contributions by uncovering the impact of the COVID-19
disruptions in a food retailer’s SC and providing guidelines for recovery actions. Our research
outcomes can be instructive for developing SC actions to respond to COVID-19 disruptions
and improve SC resilience in the food retail industry. Moreover, we provide generalized
recommendations for SC stabilization and recovery that address our two research questions
(RQ):
RQ1: How and why has the COVID-19 pandemic outbreak impacted food retail SC
performance?
The purpose of this question is to analyse the impact of different COVID-19 disruption
scenarios and identify concrete impacts on the operations and performance in food retailers’
SCs.
RQ2: How can food retail SC resilience be improved?
This question aims to identify SC actions in response to a pandemic that would increase food
retailers’ SC resilience.
The rest of this paper is organized as follows: In Section 2, the underlying case study, data
sources, and simulation model are presented. Section 3 structures the pandemic scenarios for
analysis. Our computational results are described in Section 4. In Section 5, we discuss
implications of the simulations and offer guidelines for resilience-enhancement in future.
Finally, in Section 6, we conclude by summarizing the study’s major outcomes and delineating
some future research directions.
2. Case study, data, digital twin, and simulation model
2.1. Case study and data
We used anyLogistix SC simulation and optimization software to study a multi-stage SC for a
retail company in Germany. This software has been frequently and successfully used for SC
resilience analysis (Ivanov 2017, Ivanov 2018, Aldrighetti et al. 2020, Dolgui et al. 2020a,
Ivanov 2021b). Without loss of generality, we restricted ourselves to the consideration of ten
product categories and 28 supermarket locations in five different countries (Germany, Austria,
the Czech Republic, Italy, and Hungary). We selected product categories that experienced
significant changes due to new consumer trends during the COVID-19 pandemic – Fresh Fruits,
Fresh Vegetables, Fresh Meat, Fish & Sea Food, Rice, Pasta, Convenience Food, Frozen Meals
(ready-to-eat), Wheat Flour, and Confectionary Food. Product demand was calculated by
multiplying the annual per capita consumption for each product category by the retail
company’s market share (16.2%). The selling prices for each product were taken from Statista
data regarding the average price for each product category in 2020.
Next, we created a sample of three suppliers per product category (30 suppliers in total) by
analysing supermarket data and manually identifying supplier locations. Three distribution
centre (DC) locations were selected, one in east, one in west, and one in south Germany. In
total, the retail company operates 19 DCs in Germany. Figure 1 shows the SC design.
Figure 1. Supply chain design (interface from anyLogistix Professional)
The suppliers produce and ship their products via trucks to the DCs. We assumed that the costs
of shipping the products are already included in the final price agreed with the retailer.
Inventory spending corresponds to the expenses for replenishing the inventory. This is the sum
of the initial inventory purchase costs and the replenishment costs. This cost is calculated by
anyLogistix automatically for suppliers by multiplying initial stock units by the costs for the
corresponding product. These costs were calculated based on the company’s financial report in
2019. Analysis of the company’s statement of income revealed that the purchasing costs
represent 75.9% of the total sales. Consequently, product purchasing costs were calculated as a
percentage of each product’s price, using this proportion.
2.2. Digital twin and simulation model
Digital SC twins are defined as ‘computerized models that represent the network state for any
given moment in time’ (Ivanov and Dolgui 2020b). They can be used for building end-to-end
SC visibility, enhancing resilience, and assessing contingency plans. Cavalcante et al. (2019)
point out that digital SC twins display the ‘physical SC based on actual transportation,
inventory, demand, and capacity data’. Therefore, they can be utilized by decision-makers for
planning, monitoring, and supervising. Hence, digital SC twins can improve SC visibility and,
as a result, SC resilience. Ivanov and Dolgui (2020b) and Ghadge et al. (2021) state that there
is an urgent need to visualize SC networks because of the increasing number of SC disruptions.
Digital SC twins enable real-time transparency about important logistics data such as financial
key performance indicators (KPIs), inventory level, stock level, service level, capacity, and
transportation data. They are powerful data-driven tools and firms’ control towers.
Performance-based simulation models help create efficient contingency plans to prevent or
recover from disruptions by simulating and creating what-if scenarios that predict the future
impact. Digital twins not only visualize SCs and associated risks but also offer supplier
performance and risk analysis along with forecasts of SC interruptions and risks. In addition,
they can allow to establish and examine detailed backup routes, including estimated time of
arrival (ETA) calculations. During disruptions, digital twins utilize real-time data to calculate
the impact of the disruption, build alternative SC networks and perform KPI analysis to get
real-time data about inventory levels, service level, financial parameters and demand (Ivanov,
2020a).
With the help of anyLogistix supply chain simulation and optimization software, a digital SC
can be designed. Figure 2 shows the structure of a digital SC twin created in this study for
disruption analysis. The digital SC twin encompasses three major perspectives– the network,
the flows, and the parameters. The supply chain network can be designed using different
location objects, such as customers, DCs, factories, and suppliers. The flows in the network can
be flexibly arranged to represent the specifics of different supply chains. The flows are
associated with some design (i.e., maximum) capacities in production, warehouses, and
transportation and controlled by the associated production, inventory, sourcing, and shipment
policies. These policies can be flexibly adapted to the specifics of the SC and its management
rules. Finally, different operational parameters such as demand, lead time, and control policies’
thresholds (e.g., re-order point, target inventory, and minimum vehicle load) can be defined.
With that functionality, a digital model of a physical SC (i.e., a digital SC) can be created and
used for optimization and simulations to analyse SC operations and performance dynamics
under disruptions.
Figure 2. Digital supply chain design for disruption analysis using anyLogistix
The simulation model structure developed in the anyLogistix digital twin is shown in Figure 3.
Figure 3. Simulation model design
The simulation model was based on production-inventory control with five main components
in the control loop (Fig. 3): demand, lead time, continuous inventory control with a re-order
point and a target stock setting, production control, and transportation control. It is assumed in
this simulation that the DCs follow (s,S) inventory control policy. For this simulation, the re-
order point(s) per product per DC equals the daily demand per product at each DC. Similarly,
the target inventory (S) corresponds to twice the value of the re-order point. Inventory carrying
costs for DCs are 12% of the total costs. It is assumed that suppliers have limited inventory.
Therefore, an (s,S) inventory policy with the same replenishment logic for the DCs was
applied.The Expected Lead Time (ELT) for each order is 1 day. In other words, if the order is
delivered within one day, it is considered on-time delivery; if later, delayed delivery. The
delayed deliveries have a negative impact on the ELT service level (i.e., on-time delivery),
which is defined as a ratio of on-time delivered orders to the total number of orders. The
facilities have some processing costs, which can be classified as inbound and outbound costs.
These costs correspond to the expenses incurred in receiving shipments from a supplying site
and from sending shipments to a receiving site, respectively.
The pandemic is modelled by setting some disruption and recovery events for supply and
capacities along with surges in demand. The results of the simulations are evaluated through
the following KPIs (following studies by Ivanov 2017, Ivanov 2018, Singh et al. 2020, Dolgui
2020a):
Financial indicators: Statistics related to this group provide detailed information on
generated revenue and incurred expenses during the simulation experiment for the specified
scenario. They include profit, revenue, and costs (inventory carrying costs, transportation
costs, inventory spending, inbound processing costs, outbound processing costs, and other
costs).
Expected lead time: It displays statistics on the delivery time of every ordered product item.
It is updated each time a shipment containing the order is delivered (all delivered orders are
considered whether they are on time or delayed).
Average daily available inventory: It shows statistics on the daily integral mean of the
available volume of products in stock.
Demand (product backlog): It illustrates the quantity of processed products for incomplete
orders (orders that currently lack the required number of products).
Fulfillment (late orders): It shows statistics on the number of orders that failed to arrive
within the specified ELT (e.g., the orders that are still being delivered after the ELT has
expired).
ELT service level by orders: It shows the service level based on the ratio of on-time orders
to the overall number of outgoing orders.
We validated the model in several ways. First, we tested the model on the ideal (i.e., business-
as-usual) scenario (see Fig. 5). Second, we compared the results of our simulations with
statistical data for German retail in 2020, which confirmed the trends identified in our
experiments (e.g., growing demand and increasing transportation costs). Third, we visually
checked the dynamics of material flows through the simulations jointly with two retail SC
managers, who confirmed the correlation between our simulation and performance dynamics
and real-life settings. Fourth, we performed a set of variation experiments with different
parameters (e.g., re-order point and demand). They confirmed the model’s sensitivity. The
sensitivity analysis did not reveal any interesting or novel managerial implications, so we have
omitted the presentation of these test computations and focussed on the managerial implications
of our experimental results. Finally, we used the output data analysis in the log files and
replication tests for the validity proof. We selected the timing of disruptions to avoid the ‘noise’
at the start of the simulation experiment.
3. COVID-19 pandemic scenarios
Our analysis aimed to investigate the impact of COVID-19 on German food retail SC
performance. Therefore, different disruptions resulting from policies adopted to contain the
spread of COVID-19 were examined. For analysis purposes, the timeline of the COVID-19
outbreak in Germany was used.
The first coronavirus case was reported in Germany in January 2020. By mid-February 2020,
Germany’s COVID-19 cases had been contained. However, on 25 and 26 February, multiple
cases were detected in the country, and the virus began spreading (WHO, 2020). To contain the
spread of the coronavirus, numerous restrictions to public life – including rules for reduced
contact in daily life, the closing of non-essential businesses, and temporary border controls –
were imposed.
In May 2020, an initial lifting of border controls, a gradual relaxation of the containment
measures, and a return to public life started to occur. However, a second wave of increased
coronavirus cases began around 20 October. A partial lockdown was imposed on 1 November
and continued until the end of the year.
We created the following timeline as a guideline as we developed the simulation experiments
(Deutschland.de, 2020):
27 January 2020 – First case of COVID-19 in Germany
25 February 2020 – Spreading of COVID-19 in Germany: multiple cases were detected
13 March 2020 – First restrictions on public life were imposed
24 March 2020 – Initial lockdown measures started (including border controls)
15 April 2020 – Initial release of lockdown measures after a decrease in case numbers
13 May 2020 – Initial release of border controls
20 May 2020 – Gradual relaxation of social distancing and a slow return to public life
20 October 2020 – Increase in number of COVID-19 cases and the announcement of new
measures to control the spread of COVID-19
1 November 2020 – New restrictive measures in public life begin (borders remain open)
23 November 2020 – Extension of measures until 20 December
2 December 2020 – Extension of measures until 10 January 2021
3.1. Scenario 0. Disruption-free scenario
This simulation analyses the SC performance of the food retail industry in a scenario free of
COVID-19 disruptions. The results serve as a basis for comparing and analysing the food retail
industry’s SC performance during COVID-19 disruption events.
3.2. Scenario 1. Increase in demand
Four fixed periods that are distinct in demand are simulated (Table 1).
Table 1. Experiment description: Demand levels
Event
Period
Coefficient
Normal demand
01/01/2020 – 14/03/2020
1
Demand increase 75%
15/03/2020 – 31/05/2020
1.75
Demand increase 10%
01/06/2020 – 31/10/2020
1.1
Demand increase 35%
01/11/2020 – 31/12/2020
1.35
The logic of this experiment is as follows: Normal demand (i.e., 100%) corresponds to a
business-as-usual scenario. This period corresponds to the beginning of 2020, when COVID-
19 was not yet widespread in Germany. The demand increase by 75% corresponds to the first
lockdown period. It starts when the first restrictions on public life were imposed and ends when
social distancing restrictions were relaxed and a slow return to public life began (following the
timeline described above). During this period, panic buying increased the demand enormously
in different product categories, which is reflected in our scenarios (Paul and Chowdhury 2021).
Next, a period of 10% demand increase is introduced. This period corresponds to the time when
coronavirus control measures were relaxed. Finally, the third period of demand increase (of
35%) corresponds to Germany’s second lockdown period, which impacted the demand to a
lesser extent than the first lockdown. The objective of this scenario is to simulate the impact of
increasing demand over the whole year. The results are analysed using the KPIs explained in
Section 3.0.
3.3. Scenario 2. Shutdown at suppliers’ factories
The pandemic has caused temporary shutdowns of factories, which in turn has resulted in a
sharp decline in production. Limits on people’s mobility have reduced seasonal workers’
availability for planting and harvesting in many countries. In addition, food processing
industries have been affected by social distancing rules and other measures aiming to contain
the virus’s spread, which have reduced operations’ efficiency (OECD, 2020). Thus, we simulate
a shutdown at three factories (i.e., suppliers) to analyse the impact of their closure on food
retailers. Table 2 shows the experimental setup for the event simulation and the period when
the shutdown occurs.
Table 2. Experiment description: Supplier’s factories shut down
Event type
Shutdown period
Shutdown factory: Frozen Meals supplier
01/04/2020 – 15/04/2020
Shutdown factory: Fresh Fruits supplier
16/03/2020 – 27/03/2020
Shutdown factory: Fresh Vegetables supplier
16/03/2020 – 27/03/2020
The shutdown period for each site is consistent with the German government’s period of
restrictions to contain the coronavirus spread (explained in the timeline above). The objective
of this scenario is to simulate the impact of closing the factories of two major suppliers during
a fixed period of 10 days in March and one supplier’s factory during a 15-day fixed period in
April. The results are analysed using the KPIs explained in Section 3.
3.4. Scenario 3. Bottlenecks in transport
Bottlenecks due to increased border controls are simulated in this experiment. Although no
border closures for the transportation of goods were imposed, traffic jams at the border and long
queues occurred because of the temporary border controls during the first lockdown in
Germany. This limited the normal flow of transports into the country. Bottlenecks in transport
due to border control delays are simulated for six different periods. This scenario simulates an
interruption of material flows over a period of 2 days in each experiment. Figure 4 shows the
interrupted paths with their respective durations.
Figure 4. Experiment description: Bottlenecks in transport
The disruption period for each route is consistent with the period during which the German
government’s restrictions to stop the coronavirus spread were in effect, as explained in the
timeline above. The purpose of this scenario is to simulate the impact of closing paths from
suppliers to DCs and from DCs to customers during short periods of 2 days in March and April.
The results are analysed using the KPIs explained in Section 3.0.
3.5. Scenario 4. COVID-19: Increase in demand, shutdown of suppliers’ factories, and
transport bottlenecks
This scenario simulates the impact of COVID-19 on the food retail industry SC by combining
scenarios 1, 2, and 3. This simulation aims to reflect the overall food SC performance.
4. Results and analysis
4.1. Impact of COVID-19 in Germany’s food retail SC performance
This section aims to respond to RQ1: How and why has the COVID-19 pandemic outbreak
impacted food retail SC performance? This section presents the simulation results and analysis
of the simulation experiments. First, the performance of a disruption-free scenario will be
described. Subsequently, the disruption scenarios will be evaluated using four simulation
experiments. Additionally, a cross-comparison analysis of the scenarios and generalizations of
the COVID-19 impact in the food SC will be provided.
4.1.1. Scenario 0. Disruption-free scenario
This section describes the SC performance of the food retail industry in Germany in a
pandemic-free scenario. Figure 5 shows the simulation results.
Figure 5. Experiment results: Disruption-free scenario
Regarding Financial Performance, Figure 5 displays detailed information on generated revenue
and incurred expenses during the initial scenario simulation experiment. Revenue includes the
income generated from selling products to customers. Total costs include inventory carrying
costs, transportation costs, processing costs (inbound and outbound), inventory spending, and
other costs. Profits are calculated by subtracting total costs from revenues.
The disruption-free scenario shows profitability, leading to outstanding performance. Total
costs add up to USD 2.750bn and represent 90% of the revenue (USD 3.057bn). Inventory
spending is the most elevated cost in the simulated SC, representing 84.6% of the total costs.
Given that companies in the retail industry do not usually produce, but rather purchase,
inventory, inventory spending typically represents the highest costs in their SC. Inventory
carrying costs represent 12.8% of the total costs. These costs include the expenses for storing
each product item at each DC. As many of the products are perishable, they rotate quickly –
typically on a daily basis. Thus, DCs do not accumulate excessive stock, leading to relatively
low inventory costs.
Regarding Lead Time, Figure 5 shows a histogram of the daily lead time, representing the
delivery time for each product item ordered. The x-axis shows the lead-time in days, and the y-
axis indicates the number of occurrences of orders with a particular lead-time. Products are
delivered within a time interval of 0 and 12 hours, but most of the products are delivered within
a 2-hour timeframe. Thus, the customer’s ELT of 1 day is met for all product categories. This
case suggests that lead times in the food retail industry are usually short because of recurring
orders and high inventory rotation in DCs.
With respect to the Average Daily Available Inventory, the simulation outcome shows the
inventory levels in DCs for all product categories. The min-max policy (s,S) allows ordering
quantities up to level S when reaching the re-order point s. This policy prevents excessive
inventory levels and shortages and considers some situational demand fluctuations. From the
results, it is evident that there is enough stock availability from day 1 in all scenarios, which
allows for high flexibility to match demand. Small fluctuations with low amplitude can also be
seen for all products. Moreover, inventory levels do not fall to the 0 level during the year, which
means that the inventory policy allows the retailer to satisfy demand across the SC.
Demand satisfaction can be measured in terms of Product Backlog, which indicates the number
of processed products for orders that lack the required number of products. In Figure 5, a
backlog of 0 can be observed, meaning that all orders are completely delivered.
Fulfilment (Late Orders) shows the number of orders that fail to arrive within the specified
ELT. As the ELT is one day, every order delivered within a frame greater than 1 is considered
a late delivery. In Figure 5, it can be seen that all products are delivered on time because the
number of late orders is 0. Therefore, the number of on-time orders equals the total number of
orders, 102,280.
Finally, the ELT Service Level analyses the ratio of on-time orders to the overall number of
outgoing orders. Hence, late deliveries have a negative impact on ELT service. Results show a
service level of 1, which means that customers receive 100% of their orders without delays and
within the expected lead time.
4.1.2. Scenario 1. Increase in demand
Scenario 1 shows the impact on the food SC performance after an increase in demand resulting
from the COVID-19 pandemic outbreak. Figure 6 shows the results of this scenario for each
KPI.
Figure 6. Experiment results: Increase in demand
This scenario reveals that an increase in demand positively influences the revenues because a
higher volume of products is sold, which increases the profits. Additionally, inventory levels
decrease because of demand growth. Consequently, lead time increases, as well as the number
of late orders, reducing the ELT service level. An increase in the product backlog can also be
seen because of a rise in the number of incomplete orders.
In detail, in terms of Financial Performance, the results suggest that an increase in demand in
different periods throughout the year shows a positive impact on sales as profit increases
because of increased revenues. The total costs add up to USD 2.723bn and represent around
71% of the revenue (USD 3.825bn). Transportation costs increase and represent 6.2% of the
total costs. This increase results from a growth in the number of shipping vehicles as larger
quantities are transported from the DCs to customers. Inbound and outbound costs also increase
because more goods must be processed to meet demand.
Lead Time for most orders in scenario 1 is between 0 and 10 days. Under normal conditions,
lead time is a maximum of half a day. Also, with a lower frequency, some orders can take up
to 50 days to be delivered to the customer. It can be inferred from these results that as the
demand increases, procuring the quantities required to meet it takes longer because the available
inventory is insufficient to satisfy the growing demand. Delays in delivery occur until the stock
is replenished. The results also show that the Average Daily Available Inventory dramatically
decreases because of the increased demand. Fluctuations throughout the year can be seen in the
‘Average Daily Available Inventory’ graph in Figure 6. When demand increases by 75%
(corresponding to Germany’s first lockdown), there is a sharp drop in inventory. Inventory
levels do not recover to a level similar to that in the initial scenario throughout the year. On the
other hand, during the second lockdown, a slight decrease in inventory can be seen for some
product categories, but this decrease is not as abrupt as the decrease during the first lockdown.
An increase in the demand for all products simultaneously over a relatively long period causes
delays and unattended orders.
In addition, concerning the Product Backlog, the results show that orders are not completely
delivered, and thus demand is not 100% satisfied. The ‘Demand (Product Backlog)’ graph in
Figure 6 shows a sharp increase in the product backlog during the first lockdown period, when
demand for all products increases by 75% compared to their initial level (in the disruption-free
scenario). Then, beginning on day 150 of the year, there is a decrease in the backlog as demand
decreases compared to the first lockdown period but remains 10% higher than in a disruption-
free scenario. An increase in the backlog appears again on day 300 – which corresponds to
Germany’s second lockdown period – representing an increase in demand of 35% compared to
its initial level. According to the simulation results, the accumulated backlog throughout the
year is 32.1 million kg, while the total demand for all products is 1.157 trillion kg. Therefore,
2.7% of the total demand is not satisfied after an increase in demand due to the COVID-19
pandemic.
The results also indicate that when there is an increase in lead time, the Fulfillment (Late
Orders) indicator is affected. In the ‘Fulfillment (Late Orders)’ graph in Figure 6, a growing
line over time indicates the number of orders that are not delivered on time for all products in
all customer locations. According to the results, 3,949 orders are delivered late throughout the
year, representing 3.7% of the total number of orders placed by the customers.
Similarly, Figure 6 shows that the ELT Service Level for all products is 100% from the start of
the year until the beginning of the first lockdown period in mid-March. Then, the average
service level falls to 87.2% and remains at this level throughout the rest of the year. The
increasing number of late orders is the reason for these ELT service level dynamics.
4.1.3. Scenario 2. Shutdown in suppliers’ factories
Scenario 2 shows the impact on the food SC performance after a shutdown in suppliers’
factories due to the COVID-19 pandemic outbreak. We simulated a shutdown for three different
product categories over periods of 10 and 15 days. This yielded the results depicted in Figure
7. According to these results, a temporary production shutdown at three suppliers’ factories
slightly reduces profit as total costs increase. Moreover, a reduction in inventory levels at DCs
occurs as suppliers stop delivering the products during the disruption period. Additionally, there
is a high increase in inventory when the factories are reopened. This effect can be explained by
disruption tails – that is, a destabilization of production-ordering dynamics in the post-
disruption period due to a lack of adaptability in the production-inventory control policies when
transitioning from the disruption to the recovery period (Ivanov 2019, Ivanov and Rozhkov
2020). Consequently, lead time (as well as the number of late orders) increases, which decreases
the ELT service level over an extended period. Furthermore, the number of incomplete orders
also grows, generating product backlog in some product categories.
Figure 7. Experiment results: Suppliers’ factories shutdown
In general terms, Financial Performance is not strongly affected throughout the year after
experiencing a stoppage in supply for a certain period. Profit decreases by approximately 1%
compared to a disruption-free scenario. Although revenues are not impacted, total costs are
affected by increased inventory and transportation costs. Thus, total costs add up to USD
2.753bn and represent 90% of the revenue (USD 3.057bn). Inventory levels are readjusted to
satisfy the demand because the min-max policy allows variable order quantities, thus preventing
inventory shortages.
After an outage of products, incoming quantities accumulate in DCs. Thus, stock levels grow,
causing an increase in inventory carrying costs. Also, during a supply outage, incomplete orders
are transported from DCs to customers, decreasing the vehicle capacity utilization. Then, when
products become available after the suppliers’ factories re-open, the number of trips escalates
to deliver the missing quantities. Therefore, transportation costs increase. Inbound and
outbound costs remain the same. Other administrative costs do not vary, as they are fixed costs.
The ELT service level falls to an average of 88.2% as the minimum value and improves
throughout the year, reaching a level close to 100% at the end of the year.
4.1.4. Scenario 3. Bottlenecks in transport
Scenario 3 shows the impact on the food SC performance of multiple transport bottlenecks due
to the COVID-19 pandemic outbreak. Six bottleneck events are simulated for 2-day periods in
different paths from suppliers to DCs and from DCs to customers. The results are depicted in
Figure 8.
Figure 8. Experiment results: Bottlenecks in transport
According to the results of scenario 3, transport bottlenecks have a small impact on profit
performance. There is a minor reduction in inventory levels at DCs during the disruption as
suppliers stop delivering the products within short time periods. Therefore, the number of late
orders increases, although in minimal quantities compared to the total number of orders.
Moreover, the number of incomplete orders rises but only slightly, resulting in a very low
backlog. As a result, the service level remains close to 100% within the entire period, indicating
demand satisfaction.
In terms of Fulfillment (Late Orders), a growing line that stabilizes over time can be observed.
30 orders are delivered late throughout the year, representing approximately 0.03% of the total
number of orders placed (102,280). Finally, the ELT service level is approximately 100% during
the whole period. This number can be explained by the small number of late deliveries as most
orders were delivered on time. Thus, the demand during the year is satisfied overall despite the
transport disruptions.
4.1.5. Scenario 4. COVID-19 – A combination of all scenarios
Scenario 4 shows the impact on the food SC performance after experiencing a combination of
the three disruptive scenarios explained above: an increase in demand, a shutdown in suppliers’
factories, and transport bottlenecks. Figure 9 shows the results of the experiment through the
selected KPIs.
Figure 9. Experiment results: COVID-19 scenario
This simulation illustrates the SC performance in the food retail industry during the COVID-
19 pandemic outbreak in Germany. In this scenario, synergetic effects of adding different
negative events can be observed. Interestingly, the aggregation of events results in a positive
impact on SC financial performance. Increased revenues and decreased total costs have a
positive impact on profit. Nevertheless, delays occur, and a considerable percentage of products
are not delivered on time or are incompletely delivered (reflected in the fulfilment (late orders)
and demand (products backlog) graphs, respectively). These outcomes lead to out-of-stock
products, especially during the first lockdown period. Moreover, a decrease in inventory
followed by a considerable increase after the government relaxes the lockdown measures is
apparent in the average daily available inventory graph.
The second wave of COVID-19 impacts the industry to a lesser extent than the first wave as
inventory levels do not drop significantly and a smaller quantity of backlog is accumulated.
Furthermore, the late orders curve is flattened, which implies a smaller number of delayed
orders. The accumulated disruptions result in a drop in the service level to 80%, demonstrating
that a significant percentage of the demand is still satisfied despite the pandemic outbreak.
Therefore, in general terms, the simulation suggests that the food retail industry can benefit
from the pandemic in terms of financial performance and growth opportunities.
In detail, in terms of Financial Performance, the COVID-19 scenario has a positive impact.
The increasing demand throughout the year is the principal reason for revenue generation. Total
costs add up to USD 2.707bn and represent approximately 71% of the revenue (USD 3.816bn).
Regarding inventory costs, the min-max policy allows adjusting the inventory levels to satisfy
demand, preventing inventory shortages. Thus, inventory levels change to cope with the
multiple disruptions faced. Nevertheless, available inventory is rapidly consumed, leading to
lower stock levels at DCs. As a result, inventory carrying costs decrease by 17% in the COVID-
19 scenario compared to the disruption-free scenario.
Transportation costs increase in the COVID-19 scenario because a higher quantity is shipped
in response to higher demand during the pandemic. However, there are inefficiencies in
transport as incomplete orders are shipped from the warehouse to customers when suppliers’
factories are shut down, leading to lower vehicle capacity utilization. In addition, transportation
costs increase because there is an increase in the average number of vehicles used to transport
the requested amounts. Inbound and outbound costs also increase because increasing demand
leads to more goods needing to be processed at warehouses. Other costs do not vary as they are
fixed administrative costs.
The Lead Time graph shows that a considerable number of orders are delivered to the customer
within a range of 0 to 10 days, but under normal conditions, this takes a maximum of half a
day. Less frequently, orders may take up to 70 days to be delivered. Thus, the disruptions
resulting from the coronavirus outbreak affect the lead time, so providing customers with the
required quantities of goods within the expected time becomes a challenge for food retailers.
Moreover, Average Daily Available Inventory levels are affected by the COVID-19 disruption.
Initially, a sharp drop in inventory occurs because of the increased demand along with factory
shutdowns and bottlenecks in transport during Germany’s first lockdown. During the second
lockdown, a slight decrease in inventory can be observed; however, it is not as abrupt as that in
the first lockdown. Also, alterations in stock at the end of the period can be observed inducing
the ripple effect (Ivanov et al. 2014, Dolgui et al. 2018, Li et al. 2021) of the first lockdown.
For some product categories, inventory levels recover to a level similar to that in a disruption-
free scenario throughout the remaining part of the year.
Regarding Product Backlog, the results show that some orders are not entirely delivered
because of a lack of the required number of products, and thus demand is not 100% satisfied.
The graph starts with a sharp increase in the product backlog, which results from the 75%
increase in demand, disruptions in transport, and shutdowns in the suppliers’ factories during
the first lockdown. Then, beginning on day 150, a decrease in the backlog can be observed.
This result occurs as coronavirus regulations are softened, and the country begins a gradual
return to normality. As a result, demand decreases compared to the first lockdown period,
suppliers’ factories reopen, and transport operates under normal conditions. At the end of the
year, a slight increase in product backlog can be explained by the increased demand during
Germany’s second lockdown period.
The pandemic outbreak negatively impacted the Fulfillment (Late Orders) indicator, which
shows a growing line over the whole period beginning on day 80, which coincides with the first
lockdown. This line indicates the number of orders that fail to be delivered on time in all
customer locations. According to the results, 9,067 orders are delivered late throughout the year,
representing approximately 8% of the total number of orders (102,290). Finally, this scenario
shows that the ELT service level is 100% from the start of the year until the first lockdown
period in mid-March, when the service level falls to 80% and remains at this level throughout
the rest of the year.
4.2. Cross-comparison analysis
After evaluating each simulation scenario separately, we used a cross-comparison analysis of
all scenarios to evaluate the results and create generalizations. A summary of the computational
results is presented in Table 3.
Table 3. Summary of computational results
Performance impact
Scenario 0.
Disruption-
free
Scenario 1.
Demand
increase
Scenario 2.
Shutdown
at
supplier's
factories
Scenario 3.
Bottleneck
s in
transport
Scenario 4.
COVID-19
Revenue (USD)
3,057,962.06
0
3,825,920.2
20
3,057,962.0
60
3,045,062.8
31
3,816,148.1
98
Total costs (USD)
2,750,695.74
9
2,723,818.5
89
2,753,020.0
95
2,759,776.6
09
2,707,307.6
03
Profit (USD)
307,266.310
1,102,101.6
30
304,941.96
4
285,286.22
2
1,108,840.5
94
Mean lead time (days)
0.10
1.03
0.86
0.10
3.112
Average daily available inventory
(kg)
11,858.269
10,748.007
12,018.571
11,996.012
10,579.705
Total shipped vehicles
39,697
47,089
40,764
39,766
49,387
Average number of vehicles used
6,008
7,391
6,217
6,037
7,935
Total demand by customer (kg)
918,006.548
1157,252.1
25
918,006.58
4
918,006.58
4
1157,252.1
25
Total product backlog (kg)
0
32,194,785.
25
0
0
30,885,163.
00
Backlog % of total demand by
customer
0.00%
2.78%
0.00%
0.00%
2.67%
Demand placed (orders) by
customers
102,280
102,280
102,280
102,280
102,280
Fulfillment (late orders)
0
3,949
2,733
30
9,067
Late orders % of total orders by
customer
0.00
0.04
0.03
0.00
0.08
ELT service level (%)
1.00
0.87
0.98
1.00
0.80
The overview in Table 3 allows for analysing the effects of the COVID-19 outbreak in the food
SC for the selected KPIs. It serves as a guide for addressing the main SC issues in Germany’s
food retail industry resulting from the pandemic.
Analysis of the results from the different scenarios yields several important observations. As
the overall demand increases (scenario 1) at different levels during the selected period,
inventory levels decrease. Also, the number of total shipping vehicles rises as more orders need
to be dispatched. At the same time, longer lead times can be observed, leading to an increase in
the late-orders ratio, which in turn reduces the service level. Backlogs due to incomplete orders
also occur, but they are low when compared to the total demand. From a financial point of view,
an increase in demand positively impacts revenues, leading to growing profits.
A shutdown in three suppliers’ factories (scenario 2) for 10 or 15 days generates increased
inventory levels and a higher number of shipping vehicles used when inventory accumulates at
DCs after the factories are reopened. This growth in the number of shipping vehicles used leads
to decreased transport efficiency because some vehicles are not fully utilized during the
shutdown. Longer lead times can be observed, leading to an increased late-orders ratio and a
reduced service level. Backlogs due to incomplete orders are evident.
Multiple short-time bottlenecks in transport (scenario 3) have a small impact on the KPIs
selected. The inventory levels and the number of shipping vehicles used slightly increase. The
mean lead time remains low, within the expected lead time of 1 day, and the number of late
orders rises, although in a small quantity compared to the total demand. This leads to a high
service level, close to 100%. Backlogs due to incomplete orders occur in small quantities such
that the approximate value of backlogs is 0. Profits slightly decrease because of increased total
costs and declined revenues. A combination of the disruptions mentioned above is simulated in
the COVID-19 scenario, providing insights regarding the synergetic effects. Since disruptions
coincide along the simulation period, a more substantial impact on SC performance and
operations can be observed. Inventory levels decrease because of a rapid demand increase at
the beginning of the first coronavirus wave, which is intensified when products stop being
received because of production stoppages and transport barriers. In turn, the inventory decrease
leads to higher lead times and unattended orders, generating an increase in the mean lead time
and late orders rate, and entailing an overall reduced ELT service level.
5. Discussion and Implications
This section aims to respond to RQ2: How can food retail SC resilience be improved? In
particular, we discuss what companies should do to increase resilience during a pandemic and
how these measures can be implemented.
The COVID-19 pandemic has brought both challenges and opportunities to the food retail
industry in Germany. Customers have modified their shopping behaviour and increased their
home consumption. Thus, the demand for food has seen a rapid and unprecedented growth that
has impacted Germany’s food SC. Nevertheless, the increased demand has introduced
significant pressure on the SC, creating many immediate challenges. It has caused alterations
in inventory levels, which have led to simultaneous surpluses for producers and shortages for
consumers. Hence, food retailers experienced a sharp reduction in inventory, increased product
backlog, and late orders during the first lockdown period. Some product categories presented
delays and did not arrive within the expected lead times. In short, empty shelves in supermarkets
were seen at the beginning of the COVID-19 outbreak in Germany.
Furthermore, food processing plants experienced shutdowns or were forced to operate at
reduced capacity because of Germany’s measures to contain the coronavirus’s spread during
the first lockdown. Transport bottlenecks have disrupted the movement of goods along the food
SC. Although the simulation carried out was limited to land transport by truck, the effects of an
interruption in the road that connects suppliers with DCs and DCs with customers was
evidenced. Additionally, on-time deliveries and service levels also decreased. In sum, this
section’s results, combined with the examined literature review, identify the following SC
issues in Germany’s food retail SC due to the COVID-19 pandemic: (1) change in the quantity
demanded, (2) change in the demand patterns and market composition, (3) suppliers’ output
reduction due to partial or total capacity shutdown, (4) inventory shortages and surpluses at
DCs, (5) transport and logistics backlogs, (6) adoption of new distribution channels (e.g., a shift
to online sales), (7) capacity constraints at DCs, (8) increased lead times, (9) increased number
of non-fulfilled orders, and (10) increased hygienic regulations and traceability requirements.
When these changes and challenges occur, the food retail industry should adjust its SC to
increase its resilience. In this section, we discuss some directions and actions that food retail
companies can take to increase their SC resilience, and how these measures can be implemented
in practice (Fig. 10).
Figure 10. Post-disruption framework to increase SC resilience in the food retail industry
In the context of the COVID-19 outbreak, the first step we recommend is that companies in the
food retail industry evaluate the impact of COVID-19 disruptions on their business. Companies
must assess and address the effect of disruptions in their SCs by carrying out a rapid evaluation
of their current situation and those of their most important partners. During this phase, we also
recommend that companies identify potential worst-case scenarios that may emerge from the
outbreak and analyse real-time reports to develop measures to stabilize these situations.
The second step we recommend aims to respond to SC challenges resulting from COVID-19
disruptions. Food retailers should increase communication and collaboration across their SCs
to design alternative plans and supply allocations, aiming to minimize the disruption’s impact
on SC operations and performance. Thus, contact with key suppliers to make decisions that
could prevent stockouts or other potential problems for the end customer is essential. Food
retailers should also enhance end-to-end visibility to enable them to better understand
disruptions and conduct specific actions based on existing priorities. Visibility should extend
beyond tier 1 suppliers along the entire SC. Through visibility, access to the real status of
inventory at suppliers’ locations, production schedules, and shipment status can be gained,
which may help food retailers respond accordingly. End-to-end visibility can be achieved by
utilizing a variety of available digital technologies, such as big data analytics, blockchain, and
collaborative SC platforms (Cavalcante et al. 2019, Dubey et al. 2019, Ivanov et al. 2019,
Lohmer et al. 2020, Wamba and Queiroz 2020, Dubey et al. 2021). Furthermore, end-to-end
visibility can enable mapping of the SC beyond the first or second tiers (e.g., using digital twins)
(Ivanov and Dolgui 2020b, Frazzon et al. 2021).
Securing additional stock and redefining inventory strategies is another recommendation for
companies in the food retail industry. Along with alternative supply sources and logistic
transport options, food retail companies can maintain the required inventory levels and respond
quickly to COVID-19 disruptions such as demand changes, transport disruptions, or factory
shutdowns. Additionally, expanding DCs’ capacity or outsourcing DCs can help respond to
pandemic challenges related to increasing demand and an accelerated shift to online sales.
Finally, food retailers should restructure operations to be in line with essential SC priorities in
the presence of disruptions.
As the third step, we recommend that food retailers leverage opportunities from the COVID-19
outbreak. Food retail is one of the few sectors in Germany that faces new opportunities as a
result of the pandemic. Although the food retail SC may suffer short-term challenges, the food
retail industry has the potential to grow during the crisis. Companies in this industry should
benefit from demand increase and assess the market by collecting data on new customer
segments, capturing customers’ evolving preferences. This would help food retailers adjust
their SCs to respond to changes in demand patterns, improve operations, and increase their
market share and revenues. We also suggest that food retailers transform their operations model
and SC to adapt to permanent changes in the industry such as the shift to home deliveries, online
sales, and increasing digital payments. As a result, companies must decide on product
investment, channel selection, store composition, and payment systems to respond to these
changes.
In addition, food retailers can seize new opportunities through digitalization. Building a Food
Retail Industry 4.0, which completely digitalizes (e.g., through cloud-based services) the entire
food SC, will become necessary in a post-pandemic environment. Artificial intelligence,
blockchain and T&T technologies, robotics and automation, and smart data for predictive
analytics can be implemented to increase resilience and grow opportunities in the industry by
increasing productivity and reducing costs (Brintrup et al. 2020, Winkelhaus and Grosse 2020,
Fürstenhans et al. 2021). Digital technologies can also help address the safety and hygienic
concerns fueling the ‘contactless concept’.
In sum, to grow and take advantage of new opportunities, food retailers must seek meaningful
partnerships that can become a critical component of their SCs to build redundancies, thus
enhancing customer experience and ensuring long-term business stability.
6. Conclusion
The research on disruptions management and resilience in light of the COVID-19 outbreak has
become an essential field in SC management. Post-disruption recovery analysis amid the
COVID-19 pandemic outbreak is relevant for organizations seeking to respond to disruptions
and create new growth opportunities.
We contribute to the existing literature on this topic by examining the COVID-19 pandemic’s
impact on food retail SCs with the help of a discrete-event simulation methodology and
secondary data support. We examined the impact of multiple scenarios of the COVID-19
disruptions (i.e., temporary bottlenecks in transport, shutdown in suppliers’ factories, and
increasing demand) to determine (i) the overall impact on food retailers’ SC performance and
(ii) SC actions that increase resilience in response to the identified problems. Our simulation
results showed how the COVID-19 pandemic impacted the food retail SC operations and
performance but also created growth opportunities. Although food retailers’ SCs have
experienced adverse effects from the pandemic – specifically in terms of demand backlogs and
delayed orders, long lead times, decreased service levels, and increased total costs –
opportunities have also arisen because of the increased demand.
A cross-comparison analysis of the examined scenarios suggested a positive relationship
between the duration of the disruption and its SC impact. Moreover, this analysis provided
insights regarding the events’ synergetic effects and the impact of the sequence of disruptions
during the pandemic on the SC performance.
We have suggested potential improvements and SC actions in response to the identified
challenges. Furthermore, we have created a framework containing structured recommendations
for stabilization and recovery in a post-disruption environment, which can be used as a
guideline for the main SC actors in the food retail business. This framework identified the
following five main directions for food SC resilience improvement: digitalization, inventory
management, SC flexibility, SC collaboration, and end-to-end SC visibility.
As for the limitations of this study, it should be noted that the simulations were performed using
data from certain secondary sources, which may lead to misleading generalizations and generate
inaccuracy. Another limitation is related to the restricted timeline available for observing the
effects of implementing potential improvements in the disruption scenarios. Our improvement
suggestions have been developed from a qualitative point of view and require quantitative
validation in the post-pandemic future. In addition, the study has limitations due to reduced
complexity because our analyses of the scenarios were confined to a limited number of variables
and SC locations. Finally, as COVID-19 is an outgoing event, restrictions on data access should
be noted.
Nevertheless, our study suggests a number of directions for further research. One exciting
research path would investigate SC policies to address and improve control of the ripple effect
in case of pandemic outbreaks. Responding to COVID-19 disruptions and creating a long-term
recovery strategy is becoming a priority for companies facing enormous challenges in their SC
due to the pandemic outbreak. Hence, another interesting future research avenue would create
generic actions to recover from the pandemic through digital technologies that enhance end-to-
end visibility along the SC. For the specific case of food retailers, it would be interesting to
analyse how the use of robotics and automation at distribution centres can help the transition to
online sales, which is one of the biggest challenges the industry faces because of the pandemic.
Another promising research path would analyse how predictive analytics can help food retailers
be prepared for new customer patterns and market composition, adjusting their SC accordingly.
Finally, research should be conducted on the next steps to enhance SC resilience in a post-
pandemic environment in the food retail industry.
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