2019

He, Qian

Wageningen University & Research

2019-10-28

Agribusiness Digitalization

Wageningen University - Social Sciences

MSc Thesis Chair Group Business Management & Organisation

The Future of Agribusiness

Digitalization

Digital technologies application in fresh agricultural

products supply chain traceability

Month + year: 03/2019-10/2019

MSc program: Management, Economics and Consumer Studies

Name of student: Qian HE-951027314120

Specialisation: MME

Name of Supervisor(s): Jos Bijman, Jacques Trienekens

Thesis code: MST-80436

Date of submission: 28th October2019

1

Executive Summary

This research is developing a future view for the digitalization of agribusiness through

analyzing the impact of digital technologies on supply chain traceability. The future of

agriculture would be data-driven, knowledge-based smart farming in digital technology

application models. Delphi research has been conducted to explore how emerging digital

technologies can improve fresh agricultural product (FAP) supply chain traceability. Here we

focused on the technology of the Internet of Things (IoT), Blockchain, and Artifical Intelligence

(AI). The expert panel consists of 7 experts with related expertise, results collected through

two rounds of questionnaires. The first-round questionnaire mainly focused on the

traceability challenges and the potential level of digital technologies in terms of the

traceability performance indicators, i.e., selectivity, accuracy, timeliness, and costs. The

opinions elaborate by improvement percentage. Based on the analysis of first-round

responses, the focuses of this research transfer to the traceability challenges in the second-

round questionnaire, which includes information inaccuracy, information incompleteness, and

information untimely. The opinions collected through four points Likert-scale to scenarios,

which established based on the bell pepper case. The results show that IoT and AI are

recognized to a higher degree, and there is a controversy about the application of Blockchain.

But due to the expert panel composition, the effectiveness of AI still needs more discussion.

Keywords: Traceability, Digital technology, IoT, Blockchain, AI.

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Table of Contents

Executive Summary ........................................................................................................................... 1

Chapter 1. Introduction ................................................................................................................... 3

Chapter 2. Literature Review .......................................................................................................... 5

2.1 Traceability in FAP Supply chain ................................................................................... 5

2.1.1 FAP Supply Chain ................................................................................................... 5

2.1.2 Traceability................................................................................................................ 6

2.2 Traceability Challenges Based on the Indicators ..................................................... 8

2.3 Digital technologies ......................................................................................................... 10

2.3.1 Internet of Thing (IoT) ......................................................................................... 11

2.3.2 Blockchain ............................................................................................................... 17

2.3.3 Artificial Intelligence (AI) .................................................................................... 19

Chapter 3. Method and Material ................................................................................................ 21

Chapter 4. Results ............................................................................................................................ 26

4.1 First-Round Information Collection ........................................................................... 26

4.1.1 Current Traceability Challenges ....................................................................... 26

4.1.2 Digital Technologies Improvement in FAP Traceability ........................... 27

4.2 Bell Pepper Case Scenarios ........................................................................................... 30

4.3 Second-round Result ...................................................................................................... 33

Chapter 5. Discussion and Conclusion ..................................................................................... 37

5.1 Research Method Discussion ....................................................................................... 37

5.2 Research Result Discussion ........................................................................................... 38

5.3 Research Limitations and Further Research ............................................................. 40

5.4 Conclusion .......................................................................................................................... 40

References ......................................................................................................................................... 41

Appendix ............................................................................................................................................ 47

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Chapter 1. Introduction

Agricultural products are closely related to people's lives, and safety monitoring and

sustainable development have always been a topic of concern. With the development of the

economy and the improvement of life quality, customers have a higher expectation for the

agricultural products' quality information (Vizza et al., 2018). At the same time, agricultural

companies desire to monitor the source and flow status of products in real-time, thus

achieving better supply chain management and reducing waste (Seifert & Kirci, 2017; Vizza

et al., 2018), and increasing customer trust (Wognum, Bremmers, Trienekens, van der Vorst,

& Bloemhof, 2011). Therefore, for agricultural products supply chain management, the

establishment of supply chain traceability is important (Shambulingappa & Pavankumar,

2017). There is a need for information transparency to improve the agriculture supply chain's

overall performance (Luthra, Mangla, Garg, & Kumar, 2018). In the past, there was a lack of

appropriate information technology support (Shambulingappa & Pavankumar, 2017).

However, the development of digital technologies provides a good foundation for better

traceability in the agricultural products supply chain.

Digitalization is changing the way that economic actors engage in trade and the way of

organizing and managing international trade transactions, in particular, improving the trade

chain (OECD, 2017a; OECD, 2017b). The development of digitalization has brought many

opportunities and challenges to commercial trade, causing direct and indirect impacts on

changing the market structure, promoting innovation, and creating new business models

(OECD, 2019). The impacts of digitalization on agribusiness and agricultural products supply

chain are more broad, as these sectors are becoming increasingly data-intensive (OECD,

2019). Xin & Zazueta (2016) proposed that the future of agriculture would be data-driven,

knowledge-based smart farming in digital technology application models. Digital technology

is an opportunity that cannot be ignored in the trend of modern agriculture (Schiefer, 2004).

Large amounts of information required during processing and trading, for instance, quantity,

quality, sustainability, and social concerns, as well as the logistic transparency from farm to

fork (OECD, 2019).

However, the ideal information exchanging is hindered due to the discontinuity of agricultural

products supply chain information and poor information reliability, which results in high losses

and low efficiency throughout the supply chain (Sun, Yang, Liu, & Guo, 2015). This is especially

true for fresh products (Yu & Nagurney, 2013), vegetables and fruits, due to the perishable

nature, there is a continued deterioration on agri-food quality throughout supply chain (Yu

& Nagurney, 2013), which is a big challenge for the efficiency and effectiveness of information

exchange (Sun et al., 2015). The establishment of agricultural supply chain traceability is the

basis for the development of the digital agricultural (Shambulingappa & Pavankumar, 2017).

Therefore, it is time to bring digital technology to the agricultural products chain. But the vast

literature on digital technology in agricultural products largely ignores the role of the supply

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chain. In other words, the role of technologies in the agricultural products supply chain is

relatively less of a concern (Swinnen & Kuijpers, 2019). So in this research, we explored the

influence of digital technologies in agricultural products supply chain traceability. Due to the

variety of agricultural products, we mainly focused on fresh agricultural products (FAP) supply

chain, which particularly means fresh vegetables and fruits -the fresh products that are sold

directly to the market without processing required. Thus, the research question has been

formulated as: How can the application of digital technologies improve the traceability

of the fresh agricultural products supply chain? In order to answer this question, a few sub-

questions have developed as well.

1. What is the current situation and what are the traceability challenges in the fresh

agricultural products supply chain?

2. What could new digital technologies be used for improving traceability in the fresh

agricultural supply chain?

3. How can those digital technologies address traceability challenges?

Internet of Things, Blockchain, and AI as the emerging digital technologies have been

researched since some researchers have pointed that they will achieve some disruptive

changes, e.g., Craig (2018) and Dewey, Hill, & Plasencia (2018).

This research is structured as follows: in Chapter 2, literature research was conducted. The

FAP supply chain traceability challenges have researched in terms of traceability

characteristics. Then IoT, Blockchain, and AI as digital technologies have been introduced.

This research is trying to address the traceability challenges by those digital technologies, so

Delphi research has been used to gain the insightful opinions from experts, the detailed

methods and materials are described in Chapter 3, and Chapter 4 shows the results. Chapter

5 discussed the difference between literature and research results and addressed the research

limitation as well as the conclusion.

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Chapter 2. Literature Review

2.1 Traceability in FAP Supply chain

2.1.1 FAP Supply Chain

Supply chain system is a network of all individuals, organizations, resources, activities, and

technologies involved in the product creation and sales process, from the supplier to the

manufacturer and ultimately to the user (Mentzer et al., 2001). There is usually more than one

participant at each stage, sometimes by many suppliers and distributors (Wognum et al.,

2011). It has been defined by Mentzer et al. (2001) as:

“a set of three or more entities (organizations or individuals) directly involved in the

upstream and downstream flows of products, services, finances, and/or information from

a source to a customer.”

FAP is perishable over time (Yu & Nagurney, 2013). They rely heavily on storage conditions

and require timely control of diseases. At the same time, it is necessary to consider the quality

assessment of the product when it reaches the end customer, such as the relevance of

products, the distance between production and sales, the volatility of the market and price,

etc. (Ramundo, Taisch, & Terzi, 2016). Understanding the key features of agricultural products

business is important for building the FAP supply chains. The composition of supply chain

varies from product to product, such as meat, dairy, fruits and vegetables, and grain/bread

products. Here, we focus on FAP, which means fresh vegetables and fruits. We simply put that

the FAP supply chain consists of six actors from raw material supplement to consumption, as

shown in Figure1. Since every operation in the food chain can range from raw materials to

final products and up to consumers (Vizza et al., 2018). The direction of the arrows indicates

the direction of the product flow, information flow, and financial flow.

Figure 1: The fresh agricultural product supply chain

Supply of Material

Farming Handling Wholesaler Retailer Consumption

Product logistics

Finanacial Flow

Information Flow

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2.1.2 Traceability

In the most of research, traceability has been defined as the ability to trace the product’s

history by collecting all information related to product flows in the supply chain in a strictly

formal manner (Dabbene, Gay, & Tortia, 2014). Here we also take tracking into consideration;

it represents a valuable guarantee for the end-users and for companies to avoid

counterfeiting (Probst, Frideres, & Pedersen, 2015). Thus, traceability allows tracing and

tracking of product paths; masters the information and status of product flow. It is already

mandatory for food sectors. In 2002, the EU accepted the General Food Law (GFL-

178/2002/EC) and added the traceability requirements for packaging materials two years later.

Article 14 shows that unsafe foods are not allowed to enter the market. It asked for

transparency through traceability. Article 18 requires members in the food chain to be able

to identify material flows. In addition, all foods must be labeled (e.g., barcodes, electronic

devices) to inform consumers of the food source and to ensure that the food chain is traceable

at each stage (Wognum et al., 2011).

The EU General Food Law (GFL) Regulation contains clear requirements for traceability, stating

in Article 18 (Wognum et al., 2011):

“The traceability of food, feed, food-producing animals, and any other substance

intended to be, or expected to be, incorporated into a food or feed shall be established

at all stages of production, processing, and distribution.”

In addition to comply with food safety regulations (Badia-Melis, Mishra, & Ruiz-García, 2015;

Costa et al., 2013), every chain actor could benefit from the traceability system if they have

appropriate knowledge about the products/logistic information (Shambulingappa &

Pavankumar, 2017). For consumers, knowing the origin can increase their trust in the products

(Wognum et al., 2011). The non-compliant products could be extracted from the market to

prevent consumers from paying for defects in the production process, so the consumers could

have a complete overview on the products they have purchased (Vizza et al., 2018).

For agricultural companies, getting information about the product flow could help them to

improve their understanding of product quality (Vizza et al., 2018), and to make an effective

recall when necessary (Wognum et al., 2011). A traceable information sharing platform is

encouraged to implement for the industry to save the time of information seeking for

consumers and promote consumer supervision in the food industry (Liu, Li, Steele, & Fang,

2018).

For intermediaries, the transparency of information greatly increases the effectiveness of

inventory management and reduces waste in logistics and storage processes (Wognum et al.,

2011). For example, recording product in and out for each batch product can help to

implement the First In First Out policy, thereby to reduce inventory time and to reduce the

corruption (Seifert & Kirci, 2017).

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Besides, traceability also plays an important role in food safety (Liu, 2015), and promotes

investigations into the causes of food safety problems (Badia-Melis et al., 2015). In addition

to improving the safety of food supplies, the use of traceability systems also reduces the

allocation cost, reduces the recall costs, and expands the product sales (Badia-Melis et al.,

2015).

Thus, the functions of traceability based on the literature research could be list as 1) improve

inventory management efficiency (Bevilacqua, Ciarapica, & Giacchetta, 2009; Costa et al., 2013;

Tian, 2016); 2) reduce food waste (Badia-Melis et al., 2015; Tian, 2016); 3) monitor product

quality (Badia-Melis et al., 2015; Bevilacqua et al., 2009; Costa et al., 2013); 4) forecast

marketing demand and expend sales (Badia-Melis et al., 2015; Bevilacqua et al., 2009; Costa

et al., 2013); 5) give consumers the requested information (Badia-Melis et al., 2015; Bevilacqua

et al., 2009; Costa et al., 2013; Tian, 2016).

In order to achieve those functions, both product information and process information are

required. Product information means the information carried by the product itself, such as the

variety, color, size, quality, etc. Process information is the information generated with the

movement and processing of the product, such as the storage and transport temperature,

humidity, time, etc. As Figure 2 shows, take the warehouse as an example; the products are

coming from upstream, such as farmer or sorting center, and flow to retailers. But the

regarding information is required from all aspects, such as the demand from upstream, the

transport/storage information from downstream, and environment background such as

regulation, etc. More detail required information examples (but not complete) for the

traceability system are shown in Table 1. A complete traceability information chain needs to

be shaped by agents, jobs, and flows (Van Der Vorst, Tromp, & Van Der Zee, 2005). Agents

mean the chain entities; all chain activities are defined as jobs, and the flows include product

flow and information flow.

Figure 2: Information flow for warehouse

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Table 1: Traceability information

Agents Traceability information References

Farmers farming methods, seed varieties, date, and place

of planting, chemical applications, harvesting, etc.

(Hu, Zhang, Moga, &

Neculita, 2013)

(Tian, 2016)

Transporters variety, mode of transport, packaging materials,

delivery lead-time and frequency, inventory

control policy, and order batch size,

storage/shipping temperature and humidity,

transport time, distance, etc.

(Hu et al., 2013),

(Rong, Akkerman, &

Grunow, 2011),

(Van Der Vorst et al.,

2005)

Vendors sales information, including weight, batch,

variety, storage temperature and humidity,

distribution, etc.

(Hu et al., 2013)

(Rong et al., 2011)

(Tian, 2016)

Inspectors whether the production complies with food

safety regulations, including pesticide residues,

heavy metal content, moisture content, etc.

(Hu et al., 2013)

Consumers desired appearance, decay rate, smell, value

range, and other aspects

(Hu et al., 2013)

Key performance indicators of traceability

Vizza et al. (2018) presented the characteristics of traceability are selectivity, timeliness,

accuracy, and costs: Selectivity means the ability to find the problematic product. If a recall is

needed, then one wants to recall only those products that need to be withdrawn. Timeliness

represents the time to retrieve traceability information from each stage of the production and

logistic process in order to recall or deal with the problematic product. Accuracy is the number

of errors during information capturing or product withdrawing. There are two types of error:

one is that an uncontaminated product is recalled, the other is that a contaminated product

is taken out of the market. The latter has a greater impact on food safety because it means

being irresponsible to the health of consumers. Cost involves the total cost in the traceability

system, including the cost of its implementation and management and the cost of information

collection and product recalls.

Therefore, a good traceability system means that when the products need to be withdrawn

or recalled, the products should be searched in a timely, accurate and effective manner. The

data from the production process to the final consumer is recorded and stored, so as to

reduce the time of inquiry and operation.

2.2 Traceability Challenges Based on the Indicators

In order to show how digital technologies can add value to the FAP supply chain, we first

need to look at the challenges. Looking more deeply at the supply chain network, large and

diverse perishable goods are transported over long distances and delivered frequently. The

agricultural products like fruits and vegetables are greatly affected by the process from the

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distribution centers to customers, such as delays, temperature changes and other

environmental factors. Potential security vulnerabilities, integration issues and other barriers

are introduced at every stage of the supply chain, making supply chain traceability a challenge

(Phil, 2017). Here we describe the traceability challenges in terms of the four performance

indicators: selectivity, timeliness, accuracy, and costs.

Selectivity challenges

One key challenge in the agricultural products industry is information management,

regardless of a particular actor or throughout the whole supply chain. There are a large

number of stakeholders involved in the agricultural supply chain. The business relationships

between them are constantly changing due to each member has different interests (Luthra et

al., 2018). In most cases, companies will pay more attention to their own business than the

entire supply chain (Wognum et al., 2011). Therefore, the flow of information is very poor

(Luthra et al., 2018). But the degree of chain traceability does not depend on an individual

company. The efficiency of tracing and tracking methods depends on the agreement between

the group of companies, i.e., the lack of transparency in one node affects the entire chain

(Dabbene et al., 2014).

In fact, many food manufacturers have good electronic traceability systems internally. But due

to the diversity and proprietary of their internal systems, the exchange of information between

links in the supply chain is difficult and time-consuming. While many companies have already

arranged cross-company traceability, it is still difficult to recognize all possible potential

traceability due to the complexity of the supply chain components, for example, multiple

alternative suppliers. Moreover, the link between physical and administrative product flows is

often lacking. Therefore, existing tracking systems are not complete. It is difficult for the

companies to benefit directly from the traceability system because the results of the traceback

are not very accurate, and it is difficult for the retailers to distinguish the batch of the products

(Wognum et al., 2011).

Timeliness challenges

Fresh produce is prone to deterioration over time (OECD, 2019). What’s worse is, in many

cases, it does not show any sign of spoiling until it is too late, which causes the shipments to

be rejected at the end of the new supply chain. This usually causes the supplier, producer, or

grower to bear the cost of the loss. This can be predicted in advance through production,

logistics data and timely stop loss. Therefore, monitoring the analysis of perishable goods is

an important challenge. Besides, information registration and retrieval being limited by a lack

of uniform standards for information coding and management (Pizzuti, Mirabelli, Sanz-Bobi,

& Goméz-Gonzaléz, 2014).

Accuracy challenges

Tracking and tracing the fruit and vegetable in supply chain depends on unique identification.

Although some applications appear in the fruit and vegetable industry, systematic

applications for tracing and tracking are still in the research and development phase because

of a large amount of data gathered from RFID or other identification technologies. The open

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system for rapid information identifying is still not mature. Some private systems exist;

however, the information is proprietary, but not open. The industry is reluctant to form a truly

open system across all parties in the fruit and vegetable industry (Schuster, 2008)

Besides, many participants of the supply chain have the technical infrastructure available, but

they do not count accordingly. Product registration is easy to implement, but it takes a lot of

time to process large amounts of data, and it faces a high risk of inaccuracies due to human

error, such as data storage errors and possible missing files (Vizza et al., 2018). Thus affecting

the process and the credibility of the company itself or the producer taking part in the process.

Due to the high complexity of the supply chain, Goggin & Murphy (2018) indicate that full

traceability is not possible for stakeholders who have access to existing systems only. An

individual company usually cannot control the entire supply chain, and each participant needs

to implement a solution that connects the supply chain. If the solution is flexible and efficient

enough, it needs all participants to work together (Phil, 2017).

Costs challenges

The other challenge in supply chain management is how to maintain the visibility of the

warehouse level. To reduce uncertainty in the supply chain, members of the supply chain

network need to get the information they want. Otherwise, this uncertainty will encourage

companies to adopt safe inventory or other non-ideal management methods to ensure their

supply needs (Shambulingappa & Pavankumar, 2017). This can result in food waste and

increased product logistics costs.

Information transmission can be achieved through the use of RFID in the food supply chain

and the use of NFC when the product reaches the end consumer; however, it still cannot meet

all the requirements of the heterogeneous food in the supply chain. Some new technologies

(DNA barcodes, chemometrics...) can make up for gaps, such as disease spread or historical

control, but these technologies are in the testing phase or require a lot of resource

deployment, and this investment is unaffordable for many chain parties (Badia-Melis et al.,

2015). The characteristics of the agricultural products supply chain are one of the reasons why

farmers are hard to accept technology investments: technology investments as a part of

production costs usually occur at the beginning, and payments occur in the final stages. The

return on technology investment is difficult to guarantee (Swinnen & Kuijpers, 2019). The true

value of using an electronic traceability system is not really realized, and the cost of installing

technology and operating systems outweighs the benefits (Badia-Melis et al., 2015).

Traceability costs and added value are not clear, and consumers seem reluctant to pay more

for better traceability (Wognum et al., 2011).

2.3 Digital technologies

Since the beginning of 21st century, the digital paradigm has provided a new way of thinking

for corporate innovation (Bucci, Bentivoglio, & Calitatea, 2018), which offers innovation

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benefits for the economic business (Zambon, Cecchini, Egidi, Saporito, & Colantoni, 2019).

The digital technology applications are expected to bring a remarkable improvement in

agricultural products supply chain in terms of effectiveness and efficiency (Vizza et al., 2018).

The data management system development for traceability in the food chain has gained

significant importance (Hu et al., 2013). From a technical point of view, it can be said that the

equipment used to identify and track products has now reached a good industrial level,

providing new and effective opportunities for food supply chain management (Dabbene et

al., 2014).

There are many traceability systems and standards that have been developed to support

supply chain automation activities, such as barcode, radio frequency identification (RFID),

Quick Response (QR) code, Electronic Product Code (EPC), etc. (Biswas, Muthukkumarasamy,

& Tan, 2017). Some emerging technologies are considered to have the potential to improve

global trade. According to the report of the WUR China Office (2019), digital technologies

that have a high impact on agriculture include IoT, Automation, AI, Big Data. The technologies

of the medium-impact categories include Blockchain, Global Navigation Satellite Systems

(GNSS), and Virtual Reality. Many scholars have analyzed emerging technologies. Craig (2018)

and Dewey, Hill, & Plasencia (2018) pointed out that the rise of emerging technologies;

Blockchains, AI and 5G IoT may achieve some disruptive changes. So in this research, we

mainly focus on the technology of IoT, AI, and Blockchain. Whilst each of the three

technologies (i.e., IoT, AI, and blockchain) can add value to supply chain management in

isolation(Dewey et al., 2018). However, the combination could deliver a smarter, more

intelligent, more autonomous supply chain operations, especially when the supply chain

evolves into a network; and expects to deliver higher quality services while achieving efficiency

gains (Craig, 2018).

2.3.1 Internet of Thing (IoT)

What is IoT

Internet of Things combines the concepts ‘Internet’ and ‘Thing ’ (Verdouw, Wolfert, &

Tekinerdogan, 2016), which is defined by Ben-Daya et al. (2017) as:

“The Internet of Things is a network of physical objects that are digitally connected to

sense, monitor and interact within a company and between the companies and its supply

chain enabling agility, visibility, tracking and information sharing to facilitate timely

planning, control and coordination of the supply chain processes.”

As Internet connectivity becomes the norm in business applications, IoT is the next stage of

the Internet, where physical things are communicating. In IoT, every “thing” is uniquely

identifiable, equipped with sensors and connected to the Internet in real-time. (Verdouw et

al., 2016).

This concept was first proposed by the MIT Auto-ID Center, indicating that objects can be

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tracked by Radio Frequency IDentification (RFID) tags via the internet (Schoenberger, 2002;

Verdouw et al., 2016). Later, the possibility of using radio frequency identification tags has

been proposed by Kevin Ashton to track products in 1997 (Ben-Daya et al., 2017). Then a

new concept of the wireless sensor network (WSN) appeared to sense, track and monitor

objects (Ben-Daya et al., 2017). Nowadays, the network is enriched by GPS devices,

smartphones, cloud computing to support the concept of IoT (Ben-Daya et al., 2017).

How IoT works

In the product tracing process, data can be passed from one actor to the next, with each actor

recording the source and direction of a particular product ("forward and backward"). The IoT

system supports end-to-end visibility and real-time tracking by capturing and sharing

traceability data throughout the supply chain. In addition, by recording data on product

functions, production methods and environmental conditions, this intelligent perception of

supply chain conditions help achieve high product quality, sustainability and waste reduction

(Verdouw et al., 2016).

The IoT architecture has been subdivided into four main essential layers by Ben-Daya et al.,

(2017) and Xu et al., (2014): a) Sensing layer, integrated different type of ‘things’, mainly using

RFID objects, sensors, actuators; b) Networking layer, support information to transfer through

network, such as fixed and mobile networks; c) Service layer, integrate services and

applications through middleware technology; d) Interface layer, display information to users

and allow interaction with the system.

Here IoT is introduced according to five key IoT technologies defined by (Ben-Daya et al.,

2017; Lee & Lee, 2015). The predominant technologies are RFID and sensors (Cortés, Boza,

Pérez, & Cuenca, 2015).

1) RFID: allow information identifying, tracking and transmitting. (RFID and sensors are the

main enablers of information technology; they will be introduced in detail in the following

chapters as the important components of IoT technology. As an emerging hot topic, 5G

technology will be briefly introduced as a support technology for IoT.)

2) WSN: it is a sensor network that monitors and tracks the status of different devices, such

as position, motion or temperature. They can also collaborate and communicate with

RFID tags.

3) Middleware: it belongs to the service layer that allows software developers to

communicate with heterogeneous devices such as sensors, actuators or RFID tags.

4) Cloud computing: it is an Internet-based computing platform that can share and access

different computing resources, such as computers, software, storage, etc. Cloud

computing is critical for IoT deployments because of the sheer volume of data generated

by IoT devices that require high-speed processing calculators to analyze them in real-

time and efficient decisions. They play a similar role as middleware software, connecting

IoT devices and IoT applications. Transfer data from IoT devices to business intelligence

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software to provide real-time information to decision-makers.

5) IoT applications: they can support the interaction between devices and devices or humans

and devices. As an interface between users and devices, IoT applications can present data

in an intuitive way, discover problem and propose solutions.

Current stage & challenges

IoT is expected to provide promising solutions that can change the operations and roles of

many existing systems (Xu et al., 2014). But the Internet of Things is still in its infancy in the

agriculture and food field. Applications are often decentralized and lack seamless integration,

especially the more advanced solutions are in the experimental phase of development

(Verdouw et al., 2016).

Based on the review of Liu (2015), the main individual identification technologies at present

are the identification methods based on bar code, proteins or lipids, infrared spectrometry,

the Global Positioning System (GPS) and Geographical Information System (GIS) technology,

deoxyribonucleic Acid (DNA) fingerprint technology, radio frequency identification

technology and iris recognition, etc. Among them, the bar code method in the food

traceability system has been widely used.

As one of the IoT predominant technologies, RFID is also facing a challenge in international

trading. The higher the frequency of the RFID means that the function may be more powerful

and therefore, have a larger range. However, it also means more interference. The United

States and Europe have more choices of frequency types. Japan and China are not allowed

to transmit in the 860 MHz and 915 MHz bands (Ruiz-Garcia & Lunadei, 2011). This means

that some cross-international tracking is not possible in this band. What’s more, even though

RFID technology has many advantages, it is still not the first choice for most companies

because it will bring additional costs to the company. The balance between the company’s

profits and product safety requirements is the main driver for technologies such as RFID

(Badia-Melis et al., 2015).

Advantages

The visibility and efficiency of the supply chain is an essential part of a profitable agribusiness.

Introducing a flexible, complete Internet of Things (IoT) helps optimize highly complex

agricultural supply chains (Phil, 2017). On the one hand, sensors can be used to monitor food

quality to better meet relevant food safety and sustainability requirements. On the other hand,

unpredictable supply changes (such as delays or accidents) will be quickly identified and

reported by real-time sensors in order to take timely action, or based on current and historical

conditions warning potential incidents and maybe completely avoid based on predictive

models (Phil, 2017; C. N. Verdouw, Robbemond, Verwaart, Wolfert, & Beulens, 2018). Besides,

the application of IoT technology will largely help to achieve traceability of food, if needed,

end consumers will know where their food comes from through a more transparent supply

chain (Phil, 2017). With the supporting technique of 5G, the communication of machine-to-

machine over wireless network will be faster and more efficient. It will greatly enhance and

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realize IoT in agriculture and many other fields (Dewey et al., 2018).

Examples

IoT is considered to reduce supply chain management cost (Gu & Jing, 2011; Qu, Jing, Wang,

Li, & Liang, 2012), and improve the efficiency of supply chain management (Gu & Jing, 2011;

Qu et al., 2012; Shambulingappa & Pavankumar, 2017). It also helps the FAP circulation, to

record the products’ moving track, which could help people to recall the problematic product

immediately (Liu, 2015). Meanwhile, a vast amount of data generated by sensors can be used

in building information networks for decision making (Shambulingappa & Pavankumar, 2017).

An example in the cucumber value chain, the date needs to be put in for enterprise

management includes foundation geographical data, producing environmental data, additive

in process, main processing/distribution process technology data and quality analysis testing

results data (Qu et al., 2012). It is feasible to achieve through integrating cloud computing

tools with IoT devices (Shambulingappa & Pavankumar, 2017).

Another example is for the aquatic product, Yan et al., (2013) designed and developed a

traceability platform for Tilapia supply chains based on radio frequency identification (RFID)

and electronic product code (EPC) Internet of Things. The object name service (ONS) and

electronic product code information service (EPCIS) of the platform realized the whole process

of tracking and tracing of Tilapia from breeding, processing, distribution to sales. The study

realizes product information inquiry at anytime, anywhere through traceability codes. It is

supported by the equipment like RFID tags, readers, sensors, etc. and a global traceability

network built by wireless communication technology, using a unified EPC coding

management solution to uniquely identify products on the traceability network. The traceable

subject includes business, government and consumers.

Meng, Cui, Wang, & Li (2015) investigate the experiment on the feasibility of mobile queries

for food traceability. Through algorithm analysis of QR code, operation of image

preprocessing and decoding techniques, to form the real-time query algorithm system. They

are achieving traceability queries by using the mobile terminal. There are more examples of

IoT technology application have been presented in Table 2 based on the Internet of Food

2020 project.

Data management

The terminal data is saved in the database system. For data in the server database, system

administrators or the users with specific permissions have access to add, delete, modify, and

query information. Since authorized users have the right to modify the data, miss operations

(possibly) may occur, for example, incorrectly deleting valuable data, which will cause

irreparable damage. Therefore, in the system design of Hu & Tao (2015), all modifications or

deletions performed through the network do not actually change the database, but add

“modified” or “deleted” identifiers. This way, when the mistake occurs, a higher-level data

manager is reported, which helps recover from erroneous data deletion and improves the

security of the data throughout the system. In addition, the server's architectural design uses

15

two storage mechanisms to back up data to further ensure data security. A dedicated

database server allows data to be stored on a separate physical server, improving the security

of the lifecycle network and the stability of the data by separating the data access loads and

other web application loads. By setting up a backup of the data server, data disaster recovery

can be guaranteed and the risk of loss of valuable data in server storage due to natural

disasters and other anomalies can be reduced (Y. Hu & Tao, 2015).

16

Table 2: Summary of IoF 2020 in Fruits and Vegetables (IoF 2020, 2019).

Projects Challenges IoT technologies Impacts Expected Results

Table

Grapes

1. High quality demand in

color, size and brix.

2. Vulnerable to pests.

3. Short shelf-life.

4. Easily damaged in

logistic.

1. Ground and aerial robotic

devices

2. Fixed sensors and mobile

sensors mounted on a tractor

1. Lower the use of water at the plant level.

2. Increase yield by decision support system

with weather data and soil sensor data

3. Decrease transport time, reducing spoilage

1. Yield +15%

2. Crop value +10%

3. Water usage -20%

4. Shelf life +20%

5. Harvest rejection -20%

6. Post-harvest rejection -10%

Fruit

Logistics

1. Millions of Returnable

Transport Items need to be

handled within and

between companies every

day with low efficiency.

1. RFID chips 1. Make resource planning, transport

coordination and logistics from farm to fork

much more efficient.

1. Workload for product flow documentation -25%

2. Higher transparency

3. Less (food) waste

4. Improved food quality and safety

5. Lower CO2 emissions

Digital

Ecosystem

Utilization

1. Only a fraction of the

plant protection products

applied successfully tackles

pests or insects, while the

rest unnecessarily pollutes

the environment.

1. IoT devices

2. Cloud computing

3. Analytics technologies

1. Farm productivity increases based on the

synergized parameters such as air humidity,

temperature and other weather conditions,

contributing to food security.

2. The tailored information to farmer needs

lowers resource costs by early warning

1. Irrigation water -10%

2. Use of pesticides -10%

3. Increased total factor productivity of farms

4. Reduced costs -10%

5. Improved consumer trust

6. Boosted farm sustainability

7. Strengthened data privacy and security

Chain-

Integrated

Greenhouse

Production

1. Climate change

2. Arable land scarcity

3. The needs on

productivity, freshwater and

resource use are increasing

1. Information and

communication technologies

2. Physical and virtual sensors

3. Control loops

4. Networks, models and

optimization techniques

1. Develop a decision support system

2. Standardized information will increase

interoperability along the production chain,

with easier quality and safety management

3. Improved products and processes and a

lower environmental impact

1. Improved transparency in food quality

2. Reduced costs and inputs

3. Web-based traceability and decision support

system

4. Quality standards and certification

17

2.3.2 Blockchain

What is blockchain

Blockchain as new technology has drawn much attention from researchers in many domains.

It has been defined by Tapscott & Tapscott, (2016) as:

“The blockchain is an incorruptible digital ledger of economic transactions that can be

programmed to record not just financial transactions but virtually everything of value.”

Some scholars name blockchain as a distributed data structure, system or database (Ølnes,

2016; Zhao, Fan, & Yan, 2016), while others consider it as a decentralized network (Kosba,

Miller, Shi, Wen, & Papamanthou, 2016). It initially applied to support virtual currency- Bitcoin

(Nian & Chuen, 2015); this peer-to-peer system could be used for any form of transactions

without intermediaries (Biswas et al., 2017). Future has been investigated in some other

contexts, such as smart contracts (González, Ramos, De Paz, & Corchado, 2015; Kosba et al.,

2016) and manufacturing supply chain (Abeyratne & Monfared, 2016). Jahanbin, Wingreen,

& Sharma (2019) claim that blockchain technology increases trust through transparency and

traceability in any transaction of data, commodities and financial. A majority of academic

literatures state that blockchain technology will successfully deal with trust-related issues and

may ultimately solve the basic challenges of sharing economic activities (Glaser, 2017). Dewey

et al., (2018) introduced blockchain technology is considered to be the basic shared database

technology that supports virtual currency transactions, and it will play a greater role in

business and law, even change the rules of the trading game in the next coming years.

How blockchain works

According to Biswas et al. (2017), blockchain is a distributed, decentralized ledger, which

records contracts, sales and agreements. The security of blockchain technology depends on

a powerful encryption scheme that validates each transaction block and links them together.

The attacker must compromise 51% of the system to exceed the hash function of the target

network. Therefore, tampering with transactions stored in blockchains is computationally

impractical. In this system, a transaction is represented in the form of a block, including block

number, previous block and transaction details, and the block is broadcast to each participant

in the network. Other participants, known as miners, will verify the block, and if more than 50%

of the miners verified the block, the transaction would be approved and added to the chain.

Then the transaction can proceed (Biswas et al., 2017). The simplified blockchain transaction

is shown in Figure 3.

18

Figure 3: Simplified Blockchain transaction (source: Biswas et al., 2017).

Current stage & challenges

Blockchain has been identified as one of the top ten strategic technology, which is seen as

widespread applications in monetary remittances, tracking the origin of products and more

areas (Panetta, 2017). But the knowledge of blockchain is still in the early lifecycle for supply

chain management (Hald & Kinra, 2019). The current implementation of Blockchain only

processes public data for simplicity, and only limited command-line based instructions are

provided in the current system to store chain information. In the future, a more advanced

application programming interface and graphical user interface will need to be developed to

easily store and retrieve information in the chain (Biswas et al., 2017). Current triers involve

Walmart, Fonterra, Alibaba, NZ Post and IBM amongst others (pma, 2019).

Another challenge of Blockchain is the transaction speed for applications such as bitcoin

(Biswas et al., 2017). In the current configuration, the Bitcoin blockchain can process up to

seven transactions per second. Although the size of the block will linearly affect this

processing ability, even 60-80 transactions could be processed per second with 20MB block

size. In contrast, Visa, Inc. has the ability to handle up to 50,000 transactions per second. So

with the low transaction processing capability, Bitcoin could be as digital gold, but it is not a

substitute for PayPal (Easley, O’Hara, & Basu, 2019).

Advantages

As a digital innovation, blockchain provides unique technical qualities such as invariance,

autonomy, and undeniability (irreversibility), which could contribute to supply chain reliability,

transparency and efficiency (Treiblmaier, 2018). Kshetri (2018) demonstrates the impact of

blockchain on supply chain performance, such as cost, quality, speed, reliability, risk reduction,

19

sustainability and flexibility. Based on the analysis of secondary case data, there is a strong

relationship between blockchain and increased transparency and accountability (Hald & Kinra,

2019). When all records are stored in the supply chain memory, they could be retrieved by all

participants at any time. The past transactions become irrefutable, enabling measures to

reduce corruption and fraud and increasing trust in data (Hald & Kinra, 2019). It indicates the

use of blockchain technology in the supply chain enables increased supply chain traceability

and visibility (Hald & Kinra, 2019). Each chain participant could see the progress of goods as

they move through the chain, it will give each participant an idea of where a particular good

or container is in transit (Hald & Kinra, 2019).

Example of Blockchain in the wine supply chain

Figure 4 represents the data flow in simplicity wine supply chain with the application of

blockchain. The gray rectangles indicate that the entities are a part of the collective decision-

making process. Assume some information need to be kept private in the wine supply chain

traceability system. A pair of public and private key need to be generated in the system. The

public key will be shared with all participants so that the block can be authenticated by miners.

In the traceability system, a key feature is that every individual bottle wine could be traced

back. A unique ID will provide the consumer with complete data flow and related information.

Since all detail information on the wine sold is recorded in the blockchain, it is not possible to

sell the same item twice. Therefore, it is impossible to make wine counterfeiting (Biswas et al.,

2017).

Figure 4: Data flow among wine supply chain entities (source: Biswas et al., 2017).

2.3.3 Artificial Intelligence (AI)

What is AI

Artificial intelligence is called using computers to reason, identity, learn or understand

certain behavioral phenomena from experience, acquire and retain knowledge, and develop

various forms of inference to solve problems in decision-making (Luger, 2005; Min, 2010). In

short, the main goal of artificial intelligence is to understand the patterns of human behavior

and to design computer systems that mimic human behavior, create and solve problems (Min,

2010). The behavior of managing assets is modeled and a feedback loop is built to help

20

improve the system – for example, correlating the decisions being applied with other data

points (such as weather or market data) to obtain a more reliable, more standardized view of

the environment. The actions taken are automated, making the entire system “smarter” (Craig,

2018).

Advantages

Due to the digitization of information, a large amount of information/data needs to be

processed, and AI technology makes an efficient analysis of large amounts of data easier (Min,

2010). Analysis of supply chain information is a big challenge. There is a big uncertainty in the

supply chain, and everything is changing. This is especially true for fresh products, which are

constantly updated due to the perishability of the food. Besides, uncontrollable factors like

the weather can also have an incalculable impact on the product. Therefore, the formation of

AI technology will greatly improve predictability based on accurate analysis of large amounts

of data (Min, 2010). At the same time, artificial intelligence is expected to allow the intelligent

implementation of larger scenarios, such as intelligent monitoring and intelligent sorting. In

the report of Min (2010), the application of AI in supply chain decision-making is evaluated,

in terms of inventory control and planning, transportation networking design, purchasing and

supply management, demand planning and forecasting, order-picking problems, customer

relationship management and e-synchronized supply chain management.

MH&L (2019) has raised an interview regarding AI/ML (Machine Learning). Their research

result shows that 80% of the respondents believe AI/ML is the most influential technology of

the year, because of its wide applicability and its ability to solve the complex business

problems. It is wildly believed that AI has the following primary applications: 1) Increasing

inventory and pricing accuracy for Retail, 2) Improving demand forecasting for Manufacturing,

3) Optimizing distribution network for Logistics. Hence, it can be extended that the value of

AI technology mainly focuses on data analysis and prediction, which is after the product

already has realized its digitalization. For example, the supplier provides all information on

the web for the people who have access (using identity-code/RFID) and the information

needs have a reading format for the computer to read automatically (information should

available as Linked date for machine-reading). Due to the correlation with IoT applications, it

introduced simply here.

21

Chapter 3. Method and Material

Delphi research method

In order to explore the future view on how digital technology could improve supply chain

traceability and get the experts consensus. Delphi research method has been applied in this

research, which is a method that the researcher holds multi-rounds of interview or

questionnaire to gain insights opinions from experts with certain expertise (Dalkey & Helmer,

1963). It has been widely and successfully used to summarize expert opinions on future

developments and events over the past 60 years (von der Gracht, 2012). In order to achieve

a common expectation for the future of a certain field, interactive predictions between

relevant experts are often required.

Besides, the advantages of Delphi research reflected in (a) flexible methods for quantitative

and qualitative data sources; (b) affordable, questionnaires can be easily disseminated to

participants through traditional or electronic ways; (c) no generalizable sample, but seeking

advice from a sample of individuals with specific expertise; (d) Delphi research does not

require highly specialized techniques and knowledge to design complex experiments (Brady,

2015). It is also carried out in the field of information technology (IT) to improve the efficiency

and effectiveness of technology infrastructure and communications in key sectors such as

human services (Brady, 2015).

The key features of the Delphi method introduced by Skulmoski, Hartman, & Krahn (2007)

are anonymous participation and cyclical feedback. On the one hand, the participants should

freely express their opinions without considering social pressures. It is important that the

experts don’t know each other. In this way, they will present different ideas more freely and

openly instead of criticizing each other's ideas or succumbing to an expert. On the other hand,

participants can refine their views from round to round based on research progress. This is a

good way to get a more comprehensive understanding of other’s feedback. It also helps to

reach a consensus on this topic as much as possible.

How Delphi works

Questionnaire is a typical data collection tool for Delphi research. It is used to solicit and

receive experts’ opinions on a topic with multi-rounds. It avoids some of the shortcomings of

face-to-face group discussions. For example, the impact of differences in social identities on

the response, such as values or the history of each other (Bolger & Systems, 1994). In general,

questions are developed by the researcher beginning with open or semi-open issues, to

formulate the understanding of the problem based on literature. With the progress of

research, the problems become more structured in subsequent improvements to validate the

previous consensus and ultimately determine the model (Birdsall, 2003). The number of

rounds depends on the purpose of the research, according to the suggestion from Atherton

(1976), for most research, two or three iterations are sufficient. If the sample is heterogeneous

and groups consensus is desirable, then three or more rounds may be needed. Otherwise,

22

fewer than three rounds are sufficient to reach the consensus or to conclude the result. As

the number of rounds increase, the effort required by participants increase, and the response

rate will decrease (Skulmoski et al., 2007).

In this research, we implemented two rounds of questionnaire. Semi-structured questions set

for the first-round data collection. The purpose was to get general opinions from experts on

the digital technology application in traceability. The second-round questionnaire aims to get

a consensus on how digital technologies address the current traceability challenges. The

implementation of the questionnaire follows the process of the Delphi research method. The

basic process framework of the Delphi research method includes four steps. The research

framework is shown in Figure 5.

Figure 5: Research framework based on Delphi research method

Research Process

1.1: Expert panel selection and connection.

Judgmental sampling and snowball sampling have been applied for the expert panel selection.

Judgmental sampling is used when the sample needs to fulfill a certain requirement, and

respondents are selected based on considerations of ease and predetermined criteria

(Explorable.com, 2018). In this study, experts in the field of ‘digital technology’ and ‘supply

23

chain traceability’ are needed. The experts from Wageningen University and Wageningen

Research are mainly contacted in this phase. This is because the experts in WUR are easier to

reach geographically and they are more focus on the agricultural aspects. Therefore, in the

preliminary selection stage of sampling, the potential respondents are searched by expertise

through WE@WUR, the result shows in Table 3.

Table 3: Expected respondents search result through WE@WUR

Search terms Search result Expected number of respondents

Blockchain technology 5 3

Artificial Intelligence 10 (1 duplicate) 8

Traceability 2 0

Digitalization 7 (2 duplication) 3

Tracking and Tracing 10 (1 duplicate) 4

Total 34 18

After having the list of relevant experts, potential respondents were further screened through

the introduction of their relevant research directions. For example, experts in livestock, plant

science, and environmental science were out of the picture. Thus, there are 18 expected

respondents in total at this stage. They were connected by email firstly, but just a few replies

have been received and only 1 expert was willing to participants. (some are during holiday)

Then these unanswered experts were tried to contact by calling, and 4 of them accepted the

appointment of face-to-face research explanation (all agreed to participant). Eventually, there

are 5 respondents from WUR participated in the first-round questionnaire.

Meanwhile, snowball sampling was used to get more participants. According to the

endorsement from relevant experts, 13 experts involved in the ‘IoF2020 project’ have been

contacted by email. Among them, 2 experts have replied. Therefore, the expert group

contains 7 people in total. All of them are working in the area of agricultural research, among,

3 of them are doing research on blockchain, 2 on AI, and 2 on IoT. The background

information of this expert panel is shown in Table 4.

Table 4: Background information of respondents in the first-round questionnaire

Respondents Organization Expertise

1 WUR Blockchain, Chain management

2 IoF 2020 Future Intelligence, Internet of Things, ICT

3 IoF 2020 Information technology, Internet of Things

4 WUR Information management, Artificial intelligence

5 WUR Blockchain, Big Data

6 WUR Computers and internet, Machine learning/AI, Big data

7 WUR Information technology, Supply chain, Blockchain

1.2: Developing the initial questionnaire

Questionnaire has been conducted in this research. In the first-round questionnaire, a

research description was firstly set before the questions to make the research purpose and

24

method clear to everyone. The questionnaire obtains two parts, and respondents first need

to answer the current traceability challenges of fresh agricultural product supply chain in

terms of the four traceability performance indicators (selectivity, accuracy, timeliness, and cost)

(Chapter 2.1). This question aims to facilitate the respondents obtaining a better

understanding of the current stage of FAP supply chain traceability. Then they need to provide

their opinions on the improvement level of digital technologies (i.e., IoT, Blockchain, and AI)

in fresh agricultural product supply chain, still based on the four performance indicators. Here

the percentage degree level has been used. (which means to what extent you think digital

technology could improve supply chain traceability compared with the current stage.) The

questionnaire is shown in Appendix 1.

1.3 &1.4: Responses collection and Result analysis

Both improvement percentages and explanations were collected from 7 experts. Since the

respondents gave basically the same reason for the performance increase in terms of

selectivity and accuracy, during the result integration process, selectivity and accuracy were

integrated into one category. For each performance category, the average improvement level

was calculated for each technology. To better present the result, Figure 6 to Figure 8 show

the summary of the percentages experts given. Explanation are filtered by the relevance with

topic firstly, then they are summarized and attached with the second-round questionnaire,

the same or similar opinions are merged. Since some explanations were mentioned along

with all those four performance indicators, they are list in the result as general reasons for

each digital technology.

Research adjustment

The questionnaire was planned to conduct three rounds. With the advancement of the

research process, the range of the respondents’ degree level difference would be reduced to

10% or less, until the consensus reached.

In practice, the second-round questionnaire was sent with the same questions as first-round

and attached with the integrated responses. However, there was no response received within

two weeks. According to the reply, we found that it is difficult for respondents to imagine and

quantify the possible impact of these digital techniques on supply chain traceability. Because

it depends largely on certain products and specific conditions, for example, the scope of the

application of technology, the degree of implementation of the company. Through some

interview talking with respondents and meeting with supervisors, I was suggested starting

with the traceability challenge issue to visualize the questionnaire. Therefore, the

questionnaire was adjusted based on a bell pepper case study and the questions were

adjusted to the agreed level with scenarios (Chapter 4.2). The detail process is illustrated

below (2.1-2.4). The third-round questionnaire desist since the time limitation and a small

number of responses.

2.1: Experts supplement

During the second-round data collection, there are 2 respondents from the first-round

dropped unfortunately, to future verify the scenarios, more experts were needed. Thus, the

25

experts who did not respond during the first-round questionnaire were contacted again by

emails, 2 experts from the ‘IoF 2020’ project participated. In total, there were 7 experts

participated in the second-round questionnaire as shown in Table 5.

Table 5: Background information of respondents in the second-round questionnaire

Respondents Organization Expertise

1 WUR Blockchain, Chain management

2 IoF 2020 Future Intelligence, Internet of Things, ICT

3 IoF 2020 Information technology, Internet of Things

4 WUR Information management, Artificial intelligence

5 WUR Blockchain, Big Data

6* IoF 2020 Internet of Things

7* IoF 2020 Smart Farming, Internet of Things, Big Data

*New respondents

2.2: Questionnaire modify

Three digital technology application scenarios raised according to the bell pepper case. The

scenarios provided a future possibility to help respondents consider the questions concretely.

Besides, three traceability challenge categories summarized from first-round responses. Thus,

the four performance indicators replaced by those three challenges (i.e., information

inaccuracy, information incompleteness, and information untimely) in the second-round

questionnaire. Meanwhile, some challenges examples were given here to help respondents

have a better understanding. Moreover, instead of asking the improvement percentage, a

four-point Likert scale was used in the second-round questionnaire (fully disagree, disagree,

agree and fully agree scaled from 1 to 4, respectively). The respondents only need to give the

level of recognition on the scenarios and present the opinions to the scenarios.

2.3 &2.4: Responses collection and Result analysis

For the second-round responses, the opinion consistency was analyzed by the agreed level

of recognition they gave. A summary of the recognition level from 7 experts shows in Figure

9 to Figure 11. Besides, the average recognition level on each challenge was calculated for

each technology.

26

Chapter 4. Results

4.1 First-Round Information Collection

The first-round responses collected from 7 experts, 5 of them are from Wageningen

University & Research and 2 of them are from IoF 2020 project (Table 4). The result consists

of two parts and presents as below; one part lists the comments for current traceability

challenges in the fresh agricultural supply chain; another explains how and to what extent the

digital technologies (i.e., IoT, Blockchain, and AI) would improve the traceability in the fresh

agricultural product supply chain. Both of those two parts are discussed based on the

traceability performance indicators, selectivity/accuracy, timeliness, and costs.

4.1.1 Current Traceability Challenges

Selectivity /Accuracy:

➢ Hard for single product identification, only select the whole batch, not packed products

even harder

➢ ETO Floricode standard can manage on the single product level, but some commercial

sensitive data are not sharing

➢ Separate systems between chain actors are difficult for data exchanging

➢ Hard to identify what has happened along the value chain, difficult to know whether the

product is contaminated

➢ Incorrect data input

➢ Inconsistent terminology

Timeliness:

➢ No access record permission

➢ Information recorded on paper

➢ Separate information storage system

➢ Hard to record the waste product information

Costs:

➢ Registration and data extraction cost

➢ Expensive for entire information system changing

➢ Hard to share the cost in the uncomplete chain, not all actors are willing to work together

➢ Risk of commercially trend in traceability

27

4.1.2 Digital Technologies Improvement in FAP

Traceability

The improvement percentage and the explanation present along with the three digital

technologies; IoT, Blockchain, and AI. The improvement percentage result has been

summarized in Table 6. To better visualize the data collection result, Figure 6 to Figure 8

summarized the improvement percentage for each traceability performance indicator.

Subsequently, for each technology, a general reference for all indicators give firstly. Then the

specific explanation for each category is following. Some experts refused to give a particular

percentage, but the explanations they provided are integrated into the below list for each

category.

Table 6: Result of the first-round collection

Se: Selectivity, Ti: Timeliness, Ac: Accuracy, Co: Costs

IoT

Figure 6: The improvement percentage summary of IoT

Respondent Se Ti Ac Co Se Ti Ac Co Se Ti Ac Co1 50% 80% 50% 80% 70% 0% 70% 70% 60% 80% 60% 80%2 50% 100% 30% 50% - - - - 50% 20% - -3 100% 100% 100% 0% 100% 0% 50% 0% 100% 100% 100% 0%4 - - - - 0% 0% 0% - - - - -5 90% 80% 80% 40% 10% 10% 30% 0% 75% 75% 75% 75%6 60% 60% 60% 60% 10% 10% 10% 10% 50% 50% 50% 50%7 - - - - - - - - - - - -

Average 70% 84% 64% 46% 38% 4% 32% 20% 67% 65% 71% 51%

IoT Blockchain AI

0%

20%

40%

60%

80%

100%

120%

S E L E C T I V I T Y T I M E L I N E S S A C C U R A C Y C O S T S

I O T

IOT

1 2 3 4 5 6 7 Average

28

General reference for all performance indicators:

➢ The sensor could monitor product condition and logistic status, achieve better planning

and logistics services in the chain

➢ It does not depend on the IoT technologies but on the information systems to process

the information and the level of utilization of the Internet of Things during all phases

Selectivity / Accuracy:

➢ Farming and autonomous driving are grown up by sensor technology and image

recognition

➢ Capture data automatically and assure the information is precision gathered and

registered in IT platforms, less human error

➢ Contaminated batches’ products will self-report their location

Timeliness:

➢ Immediate feedback helps to identify problems and provide reaction room

➢ Fast data storage and access, and friendly user software can significantly improve the

timeliness of tracing products.

➢ Real-time data exchange

Costs:

➢ Better selectivity and timeliness reduce operational cost

➢ High investment and running costs, but profitability will increase with the traceability

increasing, because the whole procedure will become much leaner and value-added,

especially in the areas of cost of information collecting and product recalling

➢ Cost reduction is not for every single actor

Blockchain

Figure 7: The improvement percentage summary of Blockchain

Selectivity / Accuracy

0%

20%

40%

60%

80%

100%

120%

S E L E C T I V I T Y T I M E L I N E S S A C C U R A C Y C O S T S

B L O C K C H A I N

BLOCKCHAIN

1 2 3 4 5 6 7 Average

29

➢ Improving tracking origin

➢ Blockchain is more about trust, to ensure not tampering information, and only deals

with storage aspects, has no impact on chain selectivity and accuracy.

➢ The entire sector has to change towards Blockchain, otherwise, it is pointless. It may not

happen. It is also very energy-intensive; it does not fit in our current perspective of

‘destroying the earth.’

➢ Precision registration may increase accuracy

Timeliness:

➢ Any other data storage solution can perform the same or better

➢ There is too much data to copy to all the nodes without running out of capacity and

memory

Costs:

➢ If there is no blockchain solution because of running out of memory and capacity, every

solution will add costs

➢ The entire sector needs to be changed toward to Blockchain

➢ High investment for companies

AI

Figure 8: The improvement percentage summary of AI

General reference for all performance indicators:

➢ Food informatics and computer vision can use AI in smart ways to detect food

issues/fraud in early stage, for example, automatic recognition of patterns in data, such

as exceptions, could help to trace errors in products or how they are handled

➢ AI may timely detect the riskier farmers (age, educational background…) crops from

countries that input material are not of the best quality. Ethical and privacy-related issues

here arise

➢ AI has not been used as a traceability tool often as such. The value of AI depends on the

0%

20%

40%

60%

80%

100%

120%

S E L E C T I V I T Y T I M E L I N E S S A C C U R A C Y C O S T S

A I

AI

1 2 3 4 5 6 7 Average

30

availability of data, and thus IoT.

➢ For the fresh chain machine learning and deep learning already generating forecasting

information. Using the training sets leads to optimization

Selectivity / Accuracy

AI can be used to make the software tracing products smarter. This can be useful in identifying

products

➢ AI will speed up the identifiability of the products in each step of the value chain

➢ AI will improve the precision of data collection

Timeliness:

➢ No direct relation between AI and timeliness in traceability

➢ AI will speed up all process

➢ Focus on the most notorious process of a certain SC thus reducing the identification

time

Costs:

➢ No direct relation between AI and costs in traceability

➢ High investment and running costs, but profitability will increase with the traceability

increasing.

4.2 Bell Pepper Case Scenarios

Case of bell pepper

Take bell pepper as an example, bell pepper is one of the most important Dutch export

agricultural products (De Rijke et al., 2016). Slowing down the quality decay and reducing the

shrinking during transport is a common goal for all chain actors, preferably in the context of

lowering the total chain cost, as this will increase sales and profits for all chain participants

(Van Der Vorst et al., 2005). Meanwhile, the quality and safety of products are primarily

concerned with the consumer. Quality assurance is also the ultimate goal of tracing and

tracking (Hu et al., 2013).

Temperature and humidity during transport and storing are essential determinants for pepper

product quality (Alfian et al., 2017; Van Der Vorst et al., 2005). According to Rong, Akkerman,

& Grunow (2011), the quality of the bell paper in storage (or transport) is reduced by the

storage time and the storage temperature. The shelf life of pepper is reduced from 3 weeks

to 2 weeks when the temperature is raised from 7.2 °C to 10 °C. The estimated shelf life and

quality degradation for peppers at different temperature levels are in Table 7.

31

Table 7: The estimated shelf life and quality degradation for peppers at different temperature levels

(Rong et al., 2011)

Temperature (°C) 2 4 6 8 10

Shelf life (days) 34 29 24 19 14

Quality degradation per day (Δq) 11 13 16 20 27

Based on the importance of temperature, accurate and real-time recording of temperature

during transportation and storage is also critical to predicting the rest shelf life of bell peppers.

For example, bell pepper can be stored 24 days in a 6°C environment under normal conditions.

If the storage temperature rises due to incorrect operation or irresistible factors, the quality

degradation of bell pepper will be accelerated, resulting in a shortage in shelf life. At this time,

if the temperature change is not detected in time, the manager may estimate the remaining

storage time of the product based on the pre-change conditions. If no timely measures are

taken, it may result in a waste of batches. Or another situation occurs, the products flow to

the supermarket for sale, and the bell pepper's shelf life will be mislabeled due to no timely

information transfer. This results in consumers to purchase products that do not match the

description and loss the trust in the brand or the retailer.

Challenges

The current traceability systems are characterized by the inability to link food chain records

(Badia-Melis et al., 2015). The biggest challenges for traceability systems proposed by Hu et

al. (2013) are information integrity and accuracy.

On the one hand, only a few ICT applications are specifically designed for traceability, for

example, barcode, RFID. However, barcodes can only carry limited data, and labels are poorly

organized in harsh environments (Badia-Melis et al., 2015). In addition to the technical

challenge, Wognum, Bremmers, Trienekens, van der Vorst, & Bloemhof (2011) point to the

managerial problem. In most cases, traceability is established to connect the existing

registration system, such as an integrated enterprise information system. However, the privacy

policies, data security, and the company's willingness to retain autonomy hinder the sharing

of information with others in the supply chain. Full traceability will not succeed if there is

insufficient information sharing between the participants in the supply chain. But due to the

competition, companies typically only pass a small amount of information or less useful

information to others, so the fully vegetable traceability is hindered (Mugadza, 2014).

On the other hand, in the process of registration, sometimes the information is still recorded

manually, in this case, a lot of time is needed to process the data, and there is a high risk of

inaccuracy due to human error, improper data storage, difficulty in quickly recovering stored

information, and possible loss of documents (Vizza et al., 2018). Hu et al. (2013) also verified

that the information found during the transport and processing of vegetables is often lost

and inaccurate.

Another point of view from Tian (2016) illustrated that the centralized agri-food supply chain

32

traceability system had been widely used. The information-based intelligent supply chain

network relies on the information supervision center to transmit and share information.

However, the problem with this model is that it results in information monopoly, asymmetry,

and opacity. This can lead to trust issues such as fraud, corruption, tampering, and forgery.

Badia-Melis et al., (2015) and Pizzuti, Mirabelli, Sanz-Bobi, & Goméz-Gonzaléz, (2014)

pointed out that the current traceability system cannot link to the chain records, also

described the record inaccuracies and errors and delays in obtaining basic data, which are

the fundamental in case of food outbreak disease; these systems should address the recall

and withdraw of non-consumable products (Badia-Melis et al., 2015). Clearly, the future

direction is to improve the ICT capabilities of the food industry and to provide information

exchange. This is, in particular, a challenge for crops and vegetables (Wognum et al., 2011)

ICT Solution (scenario)

Based on Tian's (2016) outlook on future agriculture, a bell pepper case scenario has been

created to elaborate on the digital technology applications in supply chain traceability. If

digital technologies, such as Artificial Intelligence, Blockchain, and Internet of Things could be

applied to this supply chain management process, accurate, timely and integrity information

delivery may be achieved. For instance, an associated smart sensor device is set up at each

stage to automatically collect information. This process does not require any manual

operation, which greatly reduces the errors caused by human factors. Meanwhile, each

product has an RFID tag with a unique ID, which records all information from planting to

selling. Information about the manager or operator is also recorded in these RFID tags. In the

event of food safety incident, relevant processes and personnel will be found immediately

through the tags. Combined with the application of blockchain systems, it is possible to

immediately track the problem product and find the cause, location and responsible

personnel of the problem product in time, which can greatly reduce losses and hazards.

Meanwhile, through the rational use of artificial intelligence, the transportation route, storage

environment, etc. can be optimized. For example, by understanding the storage temperature

and time, the remaining shelf life of the bell pepper is inferred. Thus, managers can consider

which products should be prioritized removed from inventory. At the same time, when the

temperature and humidity exceed the safety standards, an alarm can be issued immediately.

The details of each technology application scenario are described as below:

IoT (Scenario 1)

In the production stage, information such as product name, variety, origin, planting conditions,

water and fertilizer use have been captured automatically based on precision agriculture and

stored in the individual tag. After the processing company receives the product, all the basic

information could be read by scanning the RFID. Then add the information of the relevant

processing personnel. In the transportation and storage stage, GPS and intelligent sensor

equipment are used, and the information (e.g., delivery time, storage environment) have been

gathered automatically. In the selling stage, customers receive product information by

scanning the RFID. Meanwhile, the farmers and wholesalers receive sales information. Besides,

the RFID tag has a chemical and biological sensor to monitor the quality of the pepper.

33

Contaminated products will self-report their location and be removed by the operator from

the chain. Thus, the information is available to all chain participants, thereby improving

traceability in the pepper chain.

Blockchain (Scenario 2)

By applying blockchain technology in the traceability system, accurate information transfer is

realized. All relevant information gathered on the RFID is uploaded and stored in the

blockchain system, to prevent fraud and misinformation. The information is available to all

members of the system, but only administrators have access to change. Therefore, logistics

companies can follow the products at any time without inspection procedure, and control

agencies conduct traceability management and responsibility investigation of products;

consumers have access to information about the products and chain processes.

AI (Scenario 3)

Artificial intelligence simulates optimal supply chain designs based on historical data, which

include optimized transportation route planning, optimized storage environment, and

forecast demand. Based on the AI, the product flow follows a clear plan. In addition, errors in

production or processing are discovered in time by automatically identifying data patterns.

On the one hand, focusing on the most notorious processes reduces recognition time. On

the other hand, AI rapidly reviews the potential threats to products based on product sources,

such as country of origin and farmer of origin.

4.3 Second-round Result

In this round of information collection, seven experts answered the questions and gave their

opinions. Among, five of the expert panel participated in the first-round questionnaire, and

two of them are new for this round since there are two dropped. In the below section, the

result is illustrated from two aspects, one is the responses of recognition level to the scenarios,

and the other is the responses to the scenarios. Table 8 summarized the result of the

recognition degree to the scenarios with a 4-point Likert-scale (fully disagree/1, disagree/2,

agree/3, fully agree/4) in terms of the challenges on information inaccuracy (Ac), information

incompleteness (Ac) and information untimely (Ti). For each technology, a summary figure is

shown along with the explanations (Figure 9 to Figure 11).

34

Table 8: The summary of recognition level for the second-round questionnaire

*New respondents

Ac: Information inaccuracy, Co: Information incompleteness, Ti: Information untimely

IOT

Figure 9: The recognition level summary of IoT

The results show that respondents have the highest level of recognition level for IoT

applications. The automated data collection with sensors would increase the information

accuracy, completeness and timeliness. But one expert holds that IoT (RFID) cannot change

the information completeness since the completeness starts at the beginning of the chain (a

fully disagree marked). Half of the respondents said that fully automated data collection is a

little bit unrealistic at this stage since it still depends largely on the actual implementation.

Such as the time and pattern of placing the tag. Besides, how to balance the traceability and

profitability is also a problem to consider. In addition, one respondent commented that RFID

probably is not the most common technology for consumers to read. QR-Code or NFC may

be more intuitive since mobile phones being popular.

Respondent Ac Co Ti Ac Co Ti Ac Co Ti1 4 3 4 3 3 3 2 2 22 4 4 4 2 2 3 3 3 33 4 4 4 3 1 1 4 4 44 3 3 3 1 1 1 2.5 2.5 2.55 3 1 3 4 1 3 3 3 36 3 4 2 4 3 2 3 3 -7 4 3 3 1 1 2 2 2 2

Average 3.6 3.1 3.3 2.6 1.7 2.1 2.8 2.8 2.8

AIIoT Blockchain

1

2

3

4

1 2 3 4 5 6 7 A V E R A G E

IOT

Inaccuracy Incompleteness Untimely

35

Blockchain

Figure 10: The recognition level summary of Blockchain

As shown in Figure 10, the recognition level on Blockchain is controversial. About half of the

experts (4/7) expressed their recognition on Blockchain could help with the challenge of

information inaccuracy. 5 experts disagree with Blockchain could improve information

incompleteness and four of them fully disagreed with this statement. Only 3 experts agree

that the blockchain could address the information untimely challenge. Although the

recognition levels are different between respondents, the opinion they attached to the answer

was quite consistent. Respondents indicate that Blockchain technology itself is not helpful for

information issues, it only works as a storage system and protects the data from tampering.

Especially when the data is incomplete or inaccurate during the entry phase, because the

blockchain technology can only store the input information but not the actual information.

The authenticity of the entered information cannot be verified by the blockchain. One expert

also made comments on the scenario that blockchain should combine with smart contracts.

AI

Figure 11: The recognition level summary of AI

As can be seen from the recognition level result, respondents do not have an undoubted

attitude towards the scenario description of AI, except respondent 3 with fully agree. Experts

comment on the scenario that AI serves as decision support and provides early warnings

1

2

3

4

1 2 3 4 5 6 7 A V E R A G E

BLOCKCHAIN

Inaccuracy Incompleteness Untimely

1

2

3

4

1 2 3 4 5 6 7 A V E R A G E

AI

Inaccuracy Incompleteness Untimely

36

when the practices deviate from the expected results, but it does not help to improve the raw

input data. One expert also points out that the anticipates are starting from the training

dataset may with wrong or missing information.

37

Chapter 5. Discussion and Conclusion

In this chapter, we discuss the benefits and the limitations of the Delphi method that we used,

and we critically look at the result of two rounds of questionnaire information collection. Then

we furthermore discuss the research limitation and the future research direction.

5.1 Research Method Discussion

Initially, we used the Delphi research method to explore the impact of digital technologies in

supply chain traceability, since it is widely and successfully applied to summarize experts’

opinions on future developments (Dalkey & Helmer, 1963; von der Gracht, 2012). According

to Skulmoski et al. (2007), if the group is homogeneous, then a sample between 10 and 15

people could provide a sufficient result. However, if disparate groups are involved, a larger

amount of sample may need, and hundreds of people may be involved. But heterogeneous

groups can greatly increase the difficulty and complexity of collecting data, reaching

consensus, analyzing and verifying results. For this research, 10-15 participants were expected,

but the expert panel only consists of 7 experts. One of the reasons is that this research was

conducted during the summer holiday, not so many people are available with time. Besides,

the questionnaire initially designed broadly and less attractive. It is understandable that

experts were hard to image a certain situation. So in the second round questionnaire, three

scenarios were developed based on the bell pepper specific case, and Likert-scale has been

used to collect the agreed level. It is much easier for the respondents to image the situation,

and the 4 points Likert-scale was forcing respondents to give their opinion on agree or

disagree.

Due to this research is conducted during the summer holiday, the time availability is not

consistent for respondents. So we chose to apply questionnaire as it has advantages as (a)

flexible methods for quantitative and qualitative data sources; (b) affordable, questionnaires

can be easily disseminated to participants through traditional or electronic ways; (c) no

generalizable sample, but seeking advice from a sample of individuals with specific expertise

(Brady, 2015). Besides, the results are not very obvious if we only look at the numbers. But

through the explanations they gave, the opinions are relatively consistent. However, there are

differences in the angle of the problem understanding, so that the numbers given are quite

different. This problem is mainly due to that the setting of the first-round questionnaire was

not clear enough. But for the researcher, the questionnaire needs to be set as simple as

possible to get more participants. Therefore, for the further design of similar research,

empirical research should be changed to the form of an interview, which is conducive to better

obtaining the concentration of the respondents on the research questions.

38

5.2 Research Result Discussion

Traceability challenges

In the first-round questionnaire, we collected opinions from experts on traceability challenges.

As we can see from the result, the challenges focus more on managerial problems. In the

seven responses, the data-sharing problem between chain actors has been proposed four

times, which results in information incompleteness through the chain. The reasons shown in

the result may be due to commercial or profit conflicts. Another remarkable challenge that

has been summarized from the result is information inaccuracy, such as incorrect data input

or inconsistent terminology. Tracking and tracing the product in the supply chain depend on

unique identification (Schuster, 2008). The availability of technology is a necessary condition

for achieving the traceability system, but equally important is the application level and the

overall management of the entire chain. If each chain actor has its system, which not link to

each other, then even the perfect technology will not help to achieve the traceability system.

Dabbene et al. (2014) described the efficiency of tracing and tracking systems depends on

the agreement between the chain group, in other words, the lack of transparency in any node

affects the entire chain. For instance, if half of the orders and invoices in the supply chain still

base on the paper exchange, any gains expected from the deployment of new technology

will be offset by inefficiencies in the manual paper process. Therefore, the ability to trace and

track individual products not only depends on the availability of digital technologies, but also

on the information exchange level between internal data management systems and supply

chain actors (Hu et al., 2013). Thus, technologies that can increase the transparency and

reliability of information exchange may help mitigate those challenges.

IoT

The results of the first round of information collection indicated that the Internet of Things

was initially considered to contribute a 50%-100% improvement in traceability without

considering costs. The results of the second round of the questionnaire also showed the

recognition of the development of IoT. Especially for the challenge of information inaccurate,

the average recognition rate for Scenario 1 is as high as 3.6. This result is consistent with the

expectations of the literature study. A flexible and complete introduction to the Internet of

Things helps to optimize highly complex agricultural supply chains (Phil, 2017). Dewey et al.

(2018) also noted that machine-to-machine communication over a wireless network would

be faster and more efficient. Through the application of sensors and the interconnection of

networks, IoT could help to monitor the product status and the logistic status. Therefore, the

Internet of Things is expected to make a more significant contribution to traceability.

Blockchain

Through the literature study, Blockchain is expected to play a role to the challenge of data-

sharing since Blockchains are considered to have unique characteristics such as invariance,

autonomy and non-repudiation (irreversibility), which may help to improve the reliability,

transparency and efficiency of the supply chain (Treiblmaier, 2018). Hald & Kinra (2019) also

pointed out that there is a strong relationship between Blockchain and increased transparency

39

and accountability. Meanwhile, the historical transactions are recorded in the blocks and

become irrefutable and unmissable, but due to the slow transaction speed, the timeliness

issue may not be improved by blockchain technology(Biswas et al., 2017). For this point, the

research results indicate consistency with literature studies. The performance of the

Blockchain is not optimistic, and the average improvement is considered only by 4%. However,

for the other results of the two rounds of data collection, there is general controversy about

the contribution of blockchain to traceability. Some respondents believe that the blockchain

itself is just an information storage system, and its value depends on the accuracy of the input

information. Thus, any data storage system can perform the same or better. On the contrary,

another part of the respondents believes that the accuracy of traceability will be greatly

improved because the information cannot be tamper, which helps the product’s origin

tracking. Although these two views seem to be the opposite, I think it just because the

considerations are from different angles. The contribution of Blockchain technology depends

on the application scope and form. If the corresponding law is sufficient, all information

entered in the blockchain is reviewed. Then, the blockchain may play an important role in

supply chain traceability. As shown in the literature study: blockchain should combine with

smart contracts (González et al., 2015; Kosba et al., 2016)

AI

In the results of the first-round questionnaire, respondents' attitudes toward AI were basically

the same as the attitudes toward the IoT. Most respondents believe that the application of AI

will contribute more than 50% to traceability. For example, smarter product tracking, saving

time, etc. In particular, the ability to predict risk is affirmative. The same opinion was proposed

by Min (2010) as well: artificial intelligence makes large amounts of data processing easier

and more efficient, and based on data analysis, artificial intelligence technology will

significantly improve predictability and optimize supply chain management. However, in the

second round of surveys, due to the replacement of the expert panel members, there was

only one expert from the AI field, and most of the respondents indicated that they do not

have enough knowledge of this field, so the results were relatively neutral. From my point of

view, the realization of agricultural digitization is accompanied by a large amount of data

generation. Therefore, the role of artificial intelligence in data processing cannot be ignored.

But the impact may be not obvious for the product traceable. The main impact is reflected in

the process of forecasting and optimizing product flows.

All in all, innovation in digital technology will bring benefits to the entire industry to some

extent. Each digital technology can add value to supply chain management in isolation

(Dewey et al., 2018). However, the interconnection and integration of technologies can

provide a smarter, faster and more autonomous service (Craig, 2018). For example, blockchain

technology can ensure data verifiability, but accurate and complete data entry may require

help from the IoT sensor network. AI will also help to improve the data accuracy and

predictability through the intelligently analyzes on large amounts of data. Moreover, the

functions of traceability could be achieved better. For instance, more efficient inventory

management and forecasting of marketing demand could be achieved by AI. A sensor could

40

monitor product quality and the information can be shared with other chain parties by

Blockchain. Integrating the Internet of Things, artificial intelligence and Blockchain into

existing supply chain management will undoubtedly challenge the existing systems. However,

the expectations they bring cannot be underestimated, such as flexibility, security,

performance (Dewey et al., 2018).

5.3 Research Limitations and Further Research

This research aims to develop a future view on agribusiness digitalization by exploring the

impacts of digital technology applications in the supply chain. But here, we mainly discussed

the issue of traceability, and only the challenges on information accuracy, integrity and timely

have been researched. Therefore, the findings primarily directed at these challenges. Besides,

this research focused on the impacts on the applications of IoT, Blockchain and AI, but digital

technology is not only limited to them. Thus, a more specific tech analysis could be conducted

in future studies.

5.4 Conclusion

The supply chain traceability performances are reflected in selectivity/accuracy, timeliness and

cost. The traceability challenges can be summarized from the research result as information

incompleteness, information inaccuracy and information untimely. IoT, Blockchain and AI as

digital technologies have been elaborated in this research. We could conclude this research

as below:

⚫ The application of IoT would improve the fresh agricultural product supply chain

traceability performance largely and address the traceability challenges well, especially

for information accuracy.

⚫ The application of Blockchains in fresh agricultural product supply chain management

has not been widely recognized. For the supply chain traceability performance,

Blockchain may contribute to the selectivity/accuracy on a small scale, but the

improvement of timeliness is almost negligible.

⚫ The application of AI would improve the fresh agricultural product supply chain

traceability performance to a large extent as well both in selectivity/accuracy and

timeliness. The ability to address traceability challenges is also basically recognized.

41

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Appendix

Appendix 1: Questionnaire- Round 1

This research will study the impacts of digital technologies on traceability in fresh agricultural

product supply chains. We include the supply chain stages from farmer to consumer. The

digital technologies investigated include Blockchain, Internet of Things (IoT) the and Artificial

Intelligence (AI). Four traceability performance indicators have been chosen; selectivity,

timeliness, accuracy, and costs. These indicators are defined as follows:

Selectivity means the ability to find a problematic product. If a recall is needed, then one

wants to recall only those products that need to be withdrawn.

Timeliness represents the time to retrieve and collect traceability information from each stage

of the production and logistic process in order to recall or deal with the problematic product.

Accuracy is the number of errors during information capture or product withdraw. There are

two types of error: one is that an uncontaminated product is recalled. The other is that a

contaminated product is taken out of the market.

Cost involves the total cost in the traceability system including the cost of its implementation

and management, and the cost of information collecting and product recalls.

In the questions below, the percentage means the level of traceability improvement in terms

of the four indicators.

Q1. What are current traceability challenges in fresh Horticulture product supply chain,

in terms of

Selectivity

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Timeliness:

_______________________________________________________________________________________________

______________________________________________________________________________________________

Accuracy:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Costs:

_______________________________________________________________________________________________

48

_______________________________________________________________________________________________

Q2. How and to what extent will IoT improve the traceability in fresh agricultural

product supply chain?

Improvement on Selectivity: 0%------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Improvement on Timeliness: 0%------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Improvement on Accuracy: 0%------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Improvement on Costs: 0%--------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Q3. How and to what extent will Blockchain improve the traceability in fresh agricultural

product supply chain?

Improvement on Selectivity: 0%------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Improvement on Timeliness: 0%------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

49

Improvement on Accuracy: 0%--------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Improvement on Costs: 0%--------------------------------------------------100%

Explanation:

______________________________________________________________________________________________

_______________________________________________________________________________________________

Q4. How and to what extent will AI improve the traceability in fresh agricultural product

supply chain?

Improvement on Selectivity: 0%------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Improvement on Timeliness: 0%------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Improvement on Accuracy: 0%--------------------------------------------------100%

Explanation:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Improvement on Costs: 0%--------------------------------------------------100%

Explanation:

______________________________________________________________________________________________

_______________________________________________________________________________________________

50

Appendix 2: questionnaire- Round 2

The current traceability challenges in the fresh Bell Pepper supply chain

1. Information Inaccuracy

Examples: (a) information is still recorded manually in the process of registration; (b)

information is lost during transport and processing; (c) terminology is inconsistent during

registration.

2. Information Incompleteness

Examples: (a) barcodes can only carry limited data; (b) labels are poorly organized in harsh

environments; (c) due to the competition, companies typically only pass a small amount of

information; (d) separate information systems are used within the chain.

3. Information Untimely

Examples: (a) the storage environment has changed but the manager/company does not have

timely information about the change; (b) the information have been recorded on paper and

(c) cannot be checked in time.

_______________________________________________________________________________________________

Traceability is the way to track and trace products from the farmer to the consumer,

and all the handling processes and stages in between.

ICT solutions -Scenarios with fresh Bell Pepper

IoT could improve the fresh products supply chain traceability:

Each pepper has an RFID tag with a unique ID, which records all information from planting

to selling. In the production stage, the data (e.g., farming method, chemical usage, harvest

time) have been captured automatically based on precision agriculture and stored in the

individual tag. In the transportation and storage stage, GPS and intelligent sensor

equipment are used, and the information (e.g., delivery time, storage environment) have

been gathered automatically. In the selling stage, customers receive the product

information by scanning the RFID. Meanwhile, the farmers and wholesalers receive the sales

information. Besides, the RFID tag has a chemical and biological sensor to monitor the

quality of the pepper. Contaminated products will self-report their location and be removed

by the operator from the chain. Thus, the information is available to all chain participants,

thereby improving traceability in the pepper chain.

To what extent do you agree that IoT applications will improve fresh product supply

chain traceability? (please tick the agree level)

Fully

disagree

Disagree Agree Fully

agree

For Information Inaccuracy

For Information Incompleteness

For Information Untimely

51

Please feel free to share your opinion on this scenario:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

Blockchain could improve the fresh produce supply chain traceability:

All relevant information gathered on the RFID is uploaded and stored in the blockchain

system, to prevent fraud and misinformation. The information is available to all members of

the system, but only administrators have access to change. Therefore, logistics companies

can follow the products at any time without inspection procedure; and control agencies

conduct traceability management and responsibility investigation of products; consumers

have access to information about the products and chain processes.

To what extent do you agree that Blockchain applications will improve fresh product

supply chain traceability? (please tick the agree level)

Fully

disagree

Disagree Agree Fully

agree

For Information Inaccuracy

For Information Incompleteness

For Information Untimely

Please feel free to share your opinion on this scenario:

_______________________________________________________________________________________________

_______________________________________________________________________________________________

AI could improve the fresh produce supply chain traceability/trackability

Artificial intelligence simulates optimal supply chain designs based on historical data, which

include optimized transportation route planning, optimized storage environment, and

forecast demand. Based on the AI, the product flow follows a clear plan. In addition, errors in

production or processing are discovered in time by automatically identifying data patterns.

On the one hand, focusing on the most notorious processes reduces recognition time. On

the other hand, AI rapidly reviews the potential threats to products based on product sources,

such as country of origin and farmer of origin.

To what extent do you agree that AI applications will improve fresh product supply chain

traceability? (please tick the agree level)

Fully

disagree

Disagree Agree Fully

agree

For Information Inaccuracy

For Information Incompleteness

For Information Untimely

Please feel free to share your opinion on this scenario:

_______________________________________________________________________________________________

_______________________________________________________________________________________________