Traceability implementation in food supply chain: A grey-DEMATEL approach

Available at www.sciencedirect.com INFORMATION PROCESSING IN AGRICULTURE 6 (2019) 335–348 journal homepage: www.elsevier.com/locate/inpa

Traceability implementation in food supply chain: A grey-DEMATEL approach Abid Haleem, Shahbaz Khan *, Mohd Imran Khan Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi 110025, India

A R T I C L E I N F O

A B S T R A C T

Article history:

Numerous incidents of food adulteration, fraudulence and foodborne disease outbreaks

Received 29 August 2018

have shaken the consumer confidence towards the food they consume. These incidents

Received in revised form

compel the Food Supply Chain (FSC) partners to implement an appropriate traceability sys-

18 December 2018

tem in their respective supply chains to sustain the consumer confidence. The objective of

Accepted 16 January 2019

this research is to identify the drivers (major factors) which play a significant role in the suc-

Available online 23 January 2019

cessful implementation of the traceability system in FSC and evaluate the causal relationships developing therein. Twelve drivers are identified towards implementation of the

Keywords:

traceability system in FSC through literature review and supported with expert’s opinion.

Drivers

The grey-based DEMATEL approach is identified to evaluate these relationships among

Decision making trial and

the drivers according to their net effect. Further, these drivers ranked based on the promi-

evaluation laboratory (DEMATEL)

nence and effect score. The finding of this research shows that the drivers are clustered into

Food safety and quality

two groups namely: influential (cause) and influenced (effect) group. Four drivers belong to

Food Supply Chain (FSC)

the influential group, and remaining eight are from the influenced group. The most influen-

Traceability

tial driver is the ‘‘food safety and quality” which provide a significant effect on the implementation of a traceability system. This research can be a building block to develop a framework to implement the traceability system within FSC and assist the policymakers, and practitioners to identify and evaluate drivers related to the implementation of traceability system in FSC. This paper also provides a useful insight & support to the practitioners and managers in decision making for traceability implementation related issues. Ó 2019 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).

1.

Introduction

Better documentation requirements for food products has been an essential issue for the last several years and has helped to protect consumers against product adulteration & food hazards. Recently, in India, the milk scam revealed the

contamination of milk with saturated fatty oil, and the powder for increasing the milk sales [1]. After this incident, Indian authorities investigated and exposed that many milk manufacturers are diluting or contaminating milk by unappetizing agents such as hydrogen peroxide, detergent and urea. A similar incident of adulteration happened in Spain where they detected different animal DNA in candy products [2] and identify some problem regarding the antibiotic in honey [3]. Currently, this type of contamination is a big problem for developing countries and these incidents create an environment to implement the traceability system in food industries

* Corresponding author. E-mail addresses: [email protected] (A. Haleem), shahbaz. [email protected] (S. Khan), [email protected] (M.I. Khan). Peer review under responsibility of China Agricultural University. https://doi.org/10.1016/j.inpa.2019.01.003 2214-3173 Ó 2019 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Several legislations at national and international level undertake consumer welfare and have also helped efficient implementation of traceability to control food scandals and to ensure the safety of the food [4]. These incidents draw the consumer attention towards the food safety and food integrity which increased the awareness among consumers as to implement traceability. This enhanced consumer awareness increases the interest of the consumer about the food origin, processes, and other properties regarding the food product, and put pressure on the public/private companies to introduce the traceability in their Food Supply Chain (FSC). Traceability system further provides information regarding the organic production method, absence of genetically modified organism (GMO), religious requirements, sustainable and environmental information [5]. The information collected through the traceability system is utilised for the devolving the brand and gaining some certifications such as ‘certified organic food’, ‘GMOfree food’, and ‘Halal certified food’ etc. Apart from these benefits, traceability systems are also essential because it impacts the firm effectiveness and performance, food integrity, protects the food adulteration, maintains their position in the global food market [6]. Several authors have studies traceability in the context of FSC and have developed a comprehensive understanding of traceability and related framework [7–10]. A considerable amount of research is dealing with the implementation aspect of a traceability system. However, traceability implementations are limited to the specific sector/industry such; agro-food supply chain, fish supply chain; bulk grain supply chain and Halal supply chain [7,8,10–12]. Thus, there is a necessity for a systematic study to cater the implementation aspect of the traceability system more generically. The implementation of traceability in the food supply chain is challenging, due to the presence of many barriers. It is not surprising that these barriers hinder the implementation of traceability, despite this, there were indeed other factors (drivers) in the system that initiate and advances the implementation of a traceability system. Thus, identification and systematic investigation of these drivers is a need before traceability implementation. An understanding of the drivers and its causal relationships also support the food industries to develop strategies and assess their initiatives for the implementation of a traceability system. Therefore, there is a requirement to accurately identify the various drivers and their relationships for implementing the traceability system in FSC. In this context, this research tries to develop the understanding of the drivers for the effective implementation of the traceability system in FSC. The primary objective is as follows:
To identify the drivers of traceability implementation in FSC;
To evaluate the identified drivers to understand their interrelationships using a grey based DEMATEL approach;
To prioritise the identified drivers of implementation of traceability system in FSC.

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This article seeks to evaluate the inter-relationship among the identified drivers for the implementation of the traceability system in FSC. To evaluate the interrelationship among the drivers, we have identified an Multi Criteria Decision Making (MCDM) method named as ‘grey-DEMATEL’. It can explore the inter-relationships among drivers; however, DEMATEL is not well equipped to deal with the uncertainty and potential vagueness (due to the linguistic expert’s input) in the data. Thus, Grey set theory is integrated with DEMATEL as to help to deal the uncertainty and vagueness. In this manner, Grey-DEMATEL is adapted to evaluate the inter-relationship among of the drivers. The remaining study is as follows: literature review is provided in Section 2. The next Section 3 identifies the driver of traceability implementation in FSC. Section 4 introduced the research methodology. The causal relationship among the drivers is established in Section 5. The obtained results are reported in Section 6. Discussion of result and implication are provided in Sections 7 and 8 respectively. Section 9 highlights the conclusion, limitation and future scope of the study.

2.

Literature review

In the resent year, traceability has become a popular concept in the supply chain, regardless of the production regime and the type of product [7,13]. Traceability provides a set of information about the source of raw material, process, and location of the product along the supply chain. It also acts as a tracking and communication tool to ensure information accessibility along the supply chain. Traceability is defined contextually by authors, and a generic definition of traceability is given by Olsen and Borit [14], ‘‘The ability to access any or all information relating to that which is under consideration, throughout its entire lifecycle, using recorded identifications”. Traceability is a tool to find all information (such as origin, process, handling and associated activities) regarding the product during the supply chain stages and again at a later point. The main aim of a traceability system is to find the history of the product [15].

2.1.

Traceability implementation in FSC

Literature highlights that traceability implementation is a challenging task [7,16]. The complex nature of the food processing and capturing the massive volume of the information has made the traceability implementation more difficult. These complexities required advanced technologies and methods to capture high-quality data about the product and the production process [17]. Thus, successful implementation of the traceability system required an ICT infrastructure, human resources training and management support. Several studies reported the implementation of a traceability system in the different type of supply chain. Min Aung and Seok Chang [18] pointed out the requirements of traceability regarding safety and quality in the FSC. Major issues in the implementation of a traceability system in the FSC is identified by the Dabbene et al. [19]. Further, they suggest that the design of a traceability system requires a thorough rethinking and reorganising of the whole food supply chain.

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Karlsen et al. [20], evaluated the role of the traceability implementation based on the cost and benefits of seafood products. Karlsen et al. [8], reviewed the traceability literature and pointed out the lack of the framework to implement the traceability system in the FSC. The major factor(s) that affect or/and require in the implementation of the traceability are studies by some authors. Sohal [21] studied the implementation of the traceability system in automobile companies and identified six critical factors namely top management understand ComputerIntegrated Manufacturing (CIM); understand and communicate the benefits of traceability implementation; develop relationships with other FSC partners; employees training and long-term plan for CIM. Senneset, Fora˚s, and Fremme [22] identified the eight critical criteria for the implementation of traceability in a seafood supply chain. The important criteria are chain traceability; internal traceability; internal traceability software system; electronic/automatic recording technology; and use standardised identification for traceable units. Karlsen et al. [12], identified the four major critical criteria for the implementation of the traceability system in the fish supply chain and evaluated the traceability implementation against this criterion through a case study. The four major identified criteria are motivation, identification of benefits, investments, and development of optimal solutions. Duan et al. [23], identified the six critical success factors for successful implementation of traceability systems in FSC in China. The identified CSFs are laws, regulations, and standards; consumer knowledge and support; government support; top management support; effective management and communication with other FSC partners; and information & system quality. Bosona and Gebresenbet [24] reviewed the traceability implementation of highlights the five-driving force of the traceability implementation of the traceability system in the agricultural supply chain. The five forces are regulatory, safety and quality, social, economic and technological concerns. In a recent study, Faisal and Talib [9] identified the fifteen drivers of the implementation of traceability system in the agro-food supply chain and established the structural relationship among them using the ISM. The major drivers are food safety, certification, regulatory framework and welfare.

2.2.

Requirement of the traceability in Indian FSC

Indian economy is significantly depending on the agriculture sector both concerning contribution to gross value added (GVA), which was 17.32% during 2016–17 and the source of employment [25]. Despite being a large sector, however, the food retail sectors in the domestic market is highly unorganised, and segmented. The 40% of the total food produced in India is being wasted at various stages in the supply chain as per the UN’s Food and Agriculture Organization (UNFAO) report [26]. There is various reason behind the wastage of food in the present FSC system such as lack of storage space, improper postharvest management, inadequate transportation facilities, inefficient distribution, lack of infrastructural facilities, lack of refrigerated transport, lack of awareness, stock management inefficiencies, corruption, natural calamities and information regarding the production [27].

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Apart from the food wastage, food safety is also a major concern in India. The principal cause behind the food safety is the discrepancy and uncertainty in the food safety monitoring system, for example, milk adulterants, the problem of antibiotic in honey [3] contaminated meat [28]. Thus, there is a requirement to implement the effective traceability system in the FSC that can help in providing information about the product origin, ingredient and processing method. Some incentives are taken from India, such as food safety and standards authority of India provide the suggestion and guideline to develop an efficient traceability system to identification/ removal of unsafe food and preventing customers from harmful food in the market.

3. Drivers of implementation of traceability in food supply chain In the ancient age, traceability of the FSC used to be undertaken through marking the animal bodies for the animal identification [29]. In the era of technology, the traceability is managed using the advanced traceable technology such as RFID, and DNA barcoding. In the last three decades, the consumer focus has shifted towards the safety and quality; food integrity, clear labelling and transparency of the food product. This changed behaviour of the consumer puts pressure on the food supply chain partners to implement traceability systems. The traceability implementation refers as to the integration of the system (i.e. traceability system) with the supply chain, which is capable of collecting, processing and transforming information about the product in a standardised way and exchange it among different actors in the supply chain. In practice, most traceability systems are computerised, and they are implemented through extensive use of information and communications technologies (ICTs). Traceability system should provide the precise, effective and efficient information at the various link in a chain. However, to implement the traceability system in the FSC requires motivation and driving force. This paper deals with the analysis of the drivers which are significant towards implementation of the traceability system in FSC. In our case, drivers could be defined as ‘‘the resources, processes, conditions, and benefits that are vital for the implementation of a traceability system in the FSC.” Fig. 1 shows, twelve drivers of food traceability implementation and their benefits as identified through systematic literature review integrated with expert’s opinion. Table 1 shows twelve identified drivers along with their brief description and supporting references.

4.

Research methodology

An extensive literature review is used to identify significant drivers towards the implementation of traceability in FSC. These drivers are finalised with the help of an expert’s panel. The expert’s panel consists of eight members: five experts are professional, working at management level (two are the production managers, one logistics manager, one is senior manager of supply chain and one is the procurement manager) and three members are from academics (one is professor in

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Information Processing in Agriculture

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Fig. 1 – Drivers of traceability system implementation in FSC and their benefits.

the reputed university and two are PhD scholar in area of FSC). All the professionals have more than ten years of experience in the domain of management of food industries. The participated experts are highly knowledgeable & skilled in decision-making and competent in the field of food supply chain planning and operations management. After the formation of the expert’s panel, the drivers are finalised through the brainstorming session. In this manner, we have finalised twelve drivers of the implementation of a traceability system in FSC. Subsequently, a technique is required to understand the causal relationship among these drivers for better insight [56]. For the accomplishment of this objective, several methods are available such as Interpretive Structural Modelling

(ISM), Total Interpretive Structural Modelling (TISM), DEMATEL and AHP. ISM and TISM provide a structural relationship among the factors (barrier) but could not quantify the strength of the relationships [57]. Whereas DEMATEL is a useful tool to visualize the structure and strength of complex relationships by matrices and a diagram. Some MCDM methods (AHP/TOPSIS) consider the drivers are independent which does not truly reflect the real situation whereas DEMATEL can capture the inter-relationships among the dependent drivers. Moreover, DEMATEL does not require extensive information and quickly determine the most critical factor (drivers) which affect other factors (drivers). The limitation of DEMATEL is the imprecise human judgments and unpredictable surrounding [58,59]. These imprecise the human judgement and

Table 1 – Drivers of traceability implementing in food-supply chain. Code

Drivers

Brief description

References

D01

Legislation

[7,30–32]

D02

Agro-terrorism threats

D03

D04

Information communication technology (ICT) systems Information quality

D05

Sustainability

D06

Welfare

D07

Certification

D08

Competitive advantage

D09

Information sharing

D10

Food safety & quality

D11

Production scheduling and optimisation

D12

Tracking of goods

Several countries have mandatory regulations to implement the traceability system in FSC. Thus, making traceability implementation a regulatory requirement of the organisation Agro-terrorism is defined as ‘‘the deliberate introduction of a disease agent, either against livestock or into the food chain, for purposes of undermining stability and generating fear”. Used traceability as a tool to respond to such agro-terrorism threat ICT system is the set of components, which is required to generate and process data effectively. ICT generate data which is used for establishing a traceability system for the product in the supply chain Information quality includes aspects such as accuracy, adequacy, credibility, and timeliness of information exchanged. This high-quality information sharing helps overcome the bullwhip effect Traceability improves the transparency in FSC which positively affect the economic (by reducing the recall), social (by increase social welfare) and environmental (reducing the food wastage) aspect of the FSC and traceability is an essential requirement to obtain the sustainability certificate Consumers are more concerned about animal treatment & welfare and show a willingness to pay for improved animal welfare. Traceability implementation provides the guarantee to the consumers for the protection of the environment and animal welfare Many certification schemes such as ‘‘organic”, ‘‘fair trade”, ‘‘sustainability” and ‘‘Halal” requires extensive product and process information, and traceability system possesses the capacity to provide such information Traceability implementation improves the product acceptability with product information as well as process information (i.e. good manufacturing practices/good agricultural practices) provides a competitive advantage to the organisation and support the consumer’s purchasing decision Traceability enables the information sharing along the FSC to enhance coordination among FSC partners and help towards mitigating the bullwhip effect Efficient traceability ensures food safety & quality and acts as a mechanism to mitigate food safety crises. Further organisations have developed and implemented systems to remove the hazardous food form the market at lower recall cost Traceability improves the coordination among members of the supply chain which further leads to efficient production scheduling with increased internal control and decreases the recording of unnecessary information Through advanced traceable technology (such as RFID, DNA barcode) supply chain partners can track their products. Advanced technologies increase the accuracy and speed of gathering information, increases inventory control, reduces the recording errors, and reduce time & cost to record information

[8,33–35,66]

[9,30,40]

[8,31,41,42]

[9,43,44]

[8,42,45]

[9,48,49] [8,46,50,51]

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[10,46,47]

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[12,36–39]

[47,52,53]

[37,40,54,55]

339

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unpredictable surrounding can be eliminated using grey theory. The significant advantage of the grey-DEMATEL is to deal with limited data under uncertainty and can apply with small samples/number of experts [60,61,67]. Thus, a combined greyDEMATEL based approach is used for evaluating the causal relationship of these drivers. Moreover, some recent studies also show the competence of the grey-DEMATEL to evaluate the interrelations among the criteria/factors such as external barriers to remanufacturing [68], drivers for sustainable consumption and production [69] modelling of critical success factors for sustainability initiatives [70] and selection of 3PL provider [71] etc. The upcoming section discuss the greyDEMATEL approach in detail.

Step 3: The total relation matrix ‘‘T” is determined by using Eq. (5) T ¼ N  ðI  NÞ1

ð5Þ

where ‘‘I” is the identity matrix. Step 4: Determine the causal parameters using the Eqs. (6) and (7): Ri ¼

n X

tij

8i

ð6Þ

tij

8j

ð7Þ

j¼1

Cj ¼

4.1.

6 ( 2 0 1 9 ) 3 3 5 –3 4 8

n X i¼1

Grey DEMATEL

The theory of grey set is combined with a DEMATEL approach to deal with the ambiguities caused from human judgments and further, enhance the accuracy of the observations [58,62]. The steps of this methodology are [63]: Step 1: The initial step of grey-DEMATEL is to develop a fuzzy direct-relation matrix. To develop the fuzzy-direct relationship a linguistic scale is defined. In our case, we use a five-point scale as shown in Table 2. The initial direct relationship matrix is developed using the evaluation of criteria c = {ci|i = 1, 2, . . ., n} by H experts regarding pair-wise relations. Their corresponding grey number replaces the element of this matrix (Please see Table 2). Hence, the H number of Z1 ,Z2 ; Z3 . . ..,ZH direct-relation grey matrix is obtained from the H-experts. The element of

The Ri represents the direct and indirect influence of the criteria (in this case driver) ‘‘i” over the other criteria (driver), and the Cj represent the influence received by ‘‘j” by the other drivers. Step 5: The prominence (Pi) and net effect (Ei) of the criteria is determined using expressions (8) and (9): Pi ¼ Ri þ Cj ji ¼ j

ð8Þ

Ei ¼ Ri  Cj ji ¼ j

ð9Þ

The causal relationship graph is developed by the neteffect value (shown in Figs. 2 and 3). The positive value of the Ei shows the net effect (cause) of the criteria (driver) on the system and negative value represent the net effect received on the criteria by the system.

direct- relation grey matrix is represented as ‘‘Zkij ” (i.e. criteria ‘‘i” influence ‘‘j” by expert k). The overall grey relation matrix is developed by combining all grey direct-relation matrices using the Eq. (1) XH ðZH Þ=H ð1Þ Z¼ i¼0 Step 2: The overall grey relation matrix is converted in the normalised grey direct-relation matrix N using Eqs. (2)–(4). 

s ¼ ½s; s  ¼

max

1 Pn

06i6n

i; j ¼ 1; 2; 3 . . . n

j¼0  Zij

N ¼ s  Z

ð2Þ ð3Þ



nij ¼ ½s zij ; s zij 

ð4Þ

Table 2 – Linguistics terms and their corresponding grey scales. Linguistics term

Grey number

No influence (N) Very low influence (VL) Low influence (L) High influence (H) Very high influence (VH)

[0, [0, [1, [2, [3,

0] 1] 2] 3] 4]

5. Evaluation of drivers of the traceability system in FSC The stepwise procedure of grey-DEMATEL achieves the causal relationship among the identified driver. A group of eight experts, from the academia and industry, provided feedback regarding the drivers. After finalising of the drivers, an introduction of grey-DEMATEL approach is given to the experts and was asked to fill the response in the direct relationship matrix using the linguistic scale. These direct relationship matrix (using a linguistic scale) filled by the experts and one matrix (filled by expert 1) is shown in Table 3. This matrix is further converted to the grey relationship matrix using the equivalent grey number. Table 4 shows the overall grey direct relationship matrix (Z) is developed using Eq. (1). This overall grey relationship matrix (Z) is used to develop the normalised direct relationship matrix (N) using the Eqs. (2)–(4). This normalised direct relationship matrix is shown in Table 5. Further, the total relation matrix (T) is determined using Eq. (5) and is shown through Table 6. The summation of the row (Ri, as a 12  1 vector) of total relationship matrix (T) is determined using Eq. (6) and represented by ‘‘R” in Table 7. Similarly, the summation of column (Cj, is as 1  12 vector) calculated and represented by ‘‘C” in Table 7. The value of  Ri shows the net effects of the driver

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Fig. 2 – The causal relationship among the drivers to implement the traceability in a food supply chain (for lower bound values).

Fig. 3 – The causal relationship among the drivers to implement the traceability in a food supply chain (for upper bound values).

Table 3 – The direct-relation matrix for drivers (Expert 1).

D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

D01

D02

D03

D04

D05

D06

D07

D08

D09

D10

D11

D12

0 H N L VL VL VL N N VH VL VL

H 0 VL VL L L H L L VH N N

H L 0 H VL L L VL H H H H

L H H 0 H VL H L L H L L

H H L H 0 H H VL L L VL H

H VH L VL VL 0 H VL L VH N N

H H L L VL VL 0 N VL L VL VL

H VH H H VH H H 0 H H VH H

VL L H L L VL L VL 0 H L H

H L VL L N VL N N N 0 N N

L N H L L N N N H N 0 H

H VL VH H VL N L VL H N H 0

342

Table 4 – Overall grey direct-relationship matrix (Z). D03

D04

D05

D06

D07

D08

D09

D10

D11

D12

[0, [2, [0, [1, [0, [0, [0, [0, [0, [3, [0, [0,

[2, 3] [0, 0] [0, 1] [0, 1] [0.875, 1.875] [1, 2] [2, 3] [1, 2] [1, 2] [3, 4] [0, 0] [0, 0]

[2, 3] [1, 2] [0, 0] [2, 3] [0, 1] [1, 2] [1, 2] [0.125, 1.125] [2, 3] [2, 3] [2, 3] [2, 3]

[1, 2] [1.875, 2.875] [2, 3] [0, 0] [2, 3] [0, 1] [2, 3] [1, 2] [1, 2] [2.125, 3.125] [1, 2] [0.875, 1.875]

[2, 3] [2, 3] [1, 2] [2, 3] [0, 0] [2, 3] [2, 3] [0, 1] [0.875, 1.875] [1, 2] [0.125, 1.125] [2, 3]

[2, 3] [2.75, 3.75] [1, 2] [0, 1] [0, 1] [0, 0] [2, 3] [0, 1] [1, 2] [2.625, 3.625] [0, 0] [0, 0]

[2, 3] [2.125, 3.125] [1, 2] [1, 2] [0.125, 1.125] [0, 1] [0, 0] [0, 0] [0, 1] [1, 2] [0, 1] [0, 1]

[2, 3] [2.875, 3.875] [2, 3] [2, 3] [3, 4] [2, 3] [2, 3] [0, 0] [2, 3] [2.125, 3.125] [3, 4] [2, 3]

[0, 1] [1, 2] [2.125, [0.875, [0.875, [0.125, [1, 2] [0, 1] [0, 0] [2, 3] [1, 2] [2, 3]

[2, 3] [1, 2] [0.125, 1.125] [1, 2] [0, 0] [0, 0.875] [0, 0] [0, 0] [0, 0.125] [0, 0] [0, 0] [0, 0]

[1, [0, [2, [1, [1, [0, [0, [0, [2, [0, [0, [2,

[1.875, 2.875] [0, 1] [2.875, 3.875] [2, 3] [0, 1] [0, 0] [1, 2] [0, 1] [2, 3] [0, 0] [2, 3] [0, 0]

D06

D07

0] 3] 0] 2] 1] 1] 0.75] 0] 0] 4] 1] 1]

3.125] 1.875] 1.875] 1.125]

2] 0] 3] 2] 2] 0] 0.25] 0] 3] 0] 0] 3]

Table 5 – The normalized direct-relation matrix (N). D01 D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

[0.000, [0.106, [0.000, [0.053, [0.000, [0.000, [0.000, [0.000, [0.000, [0.159, [0.000, [0.000,

D02 0.000] 0.104] 0.000] 0.069] 0.035] 0.035] 0.026] 0.000] 0.000] 0.139] 0.035] 0.035]

[0.106, [0.000, [0.000, [0.000, [0.046, [0.053, [0.106, [0.053, [0.053, [0.159, [0.000, [0.000,

D03 0.104] 0.000] 0.035] 0.035] 0.065] 0.069] 0.104] 0.069] 0.069] 0.139] 0.000] 0.000]

[0.106, [0.053, [0.000, [0.106, [0.000, [0.053, [0.053, [0.007, [0.106, [0.106, [0.106, [0.106,

D04 0.104] 0.069] 0.000] 0.104] 0.035] 0.069] 0.069] 0.039] 0.104] 0.104] 0.104] 0.104]

[0.053, [0.099, [0.106, [0.000, [0.106, [0.000, [0.106, [0.053, [0.053, [0.113, [0.053, [0.046,

D05 0.069] 0.100] 0.104] 0.000] 0.104] 0.035] 0.104] 0.069] 0.069] 0.108] 0.069] 0.065]

[0.106, [0.106, [0.053, [0.106, [0.000, [0.106, [0.106, [0.000, [0.046, [0.053, [0.007, [0.106,

0.104] 0.104] 0.069] 0.104] 0.000] 0.104] 0.104] 0.035] 0.065] 0.069] 0.039] 0.104]

[0.106, [0.146, [0.053, [0.000, [0.000, [0.000, [0.106, [0.000, [0.053, [0.139, [0.000, [0.000,

0.104] 0.130] 0.069] 0.035] 0.035] 0.000] 0.104] 0.035] 0.069] 0.126] 0.000] 0.000]

[0.106, [0.113, [0.053, [0.053, [0.007, [0.000, [0.000, [0.000, [0.000, [0.053, [0.000, [0.000,

D08 0.104] 0.108] 0.069] 0.069] 0.039] 0.035] 0.000] 0.000] 0.035] 0.069] 0.035] 0.035]

[0.106, [0.152, [0.106, [0.106, [0.159, [0.106, [0.106, [0.000, [0.106, [0.113, [0.159, [0.106,

D09 0.104] 0.134] 0.104] 0.104] 0.139] 0.104] 0.104] 0.000] 0.104] 0.108] 0.139] 0.104]

[0.000, [0.053, [0.113, [0.046, [0.046, [0.007, [0.053, [0.000, [0.000, [0.106, [0.053, [0.106,

D10 0.035] 0.069] 0.108] 0.065] 0.065] 0.039] 0.069] 0.035] 0.000] 0.104] 0.069] 0.104]

[0.106, [0.053, [0.007, [0.053, [0.000, [0.000, [0.000, [0.000, [0.000, [0.000, [0.000, [0.000,

D11 0.104] 0.069] 0.039] 0.069] 0.000] 0.030] 0.000] 0.000] 0.004] 0.000] 0.000] 0.000]

[0.053, [0.000, [0.106, [0.053, [0.053, [0.000, [0.000, [0.000, [0.106, [0.000, [0.000, [0.106,

D12 0.069] 0.000] 0.104] 0.069] 0.069] 0.000] 0.009] 0.000] 0.104] 0.000] 0.000] 0.104]

[0.099, [0.000, [0.152, [0.106, [0.000, [0.000, [0.053, [0.000, [0.106, [0.000, [0.106, [0.000,

0.100] 0.035] 0.134] 0.104] 0.035] 0.000] 0.069] 0.035] 0.104] 0.000] 0.104] 0.000]

6 ( 2 0 1 9 ) 3 3 5 –3 4 8

D02

Information Processing in Agriculture

D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

D01

[0.194, [0.091, [0.232, [0.184, [0.044, [0.025, [0.117, [0.016, [0.175, [0.123, [0.160, [0.075, [0.134, [0.071, [0.179, [0.125, [0.082, [0.024, [0.060, [0.012, [0.165, [0.095, [0.054, [0.164, [0.134, [0.085, [0.022, [0.071, [0.014, [0.008, [0.022, [0.008, [0.015, [0.051, [0.009, [0.011, [0.107, [0.129, [0.183, [0.123, [0.077, [0.033, [0.115, [0.015, [0.067, [0.200, [0.102, [0.159, [0.329, [0.341, [0.261, [0.259, [0.233, [0.168, [0.254, [0.034, [0.238, [0.354, [0.244, [0.225, [0.164, [0.164, [0.078, [0.088, [0.028, [0.017, [0.040, [0.014, [0.030, [0.134, [0.019, [0.022, [0.193, [0.219, [0.088, [0.053, [0.024, [0.021, [0.152, [0.015, [0.086, [0.243, [0.022, [0.027,

6.

Results

0.309] 0.313] 0.285] 0.201] 0.238] 0.162] 0.274] 0.145] 0.230] 0.341] 0.200] 0.212]

The result of the grey-DEMATEL analysis provided the importance order of each driver and classified into two groups. The increasing order of the importance of the driver is measured by the increasing value of ‘‘R + C” (please see Table 7). In this analysis, the importance order of the drivers based on the ‘lower values’ is D04 – D08 – D03 – D02 – D10 – D01 – D09 – D12 – D05 – D07 – D11 – D06. The other importance order of the drivers based on the ‘upper values’ is D04 – D03 – D02 – D08 – D05 – D09 – D01 – D12 – D07 – D10 – D06 – D11. Further, these drivers are classified based on ‘‘R  C” values into two distinct group: ‘‘cause/influential” and ‘‘effect/influenced”. The cause group contains four drivers namely: D10, D01, D02, and D07 (Please see Figs. 2 and 3). Similarly, the effect-group is identified and is made up of eight drivers namely D08, D05, D06, D12, D04, D03, D09, and D11 (please see Figs. 2 and 3). The identified drivers towards implementation of the traceability system in FSC are ranked as per the values of ‘‘R + C” and ‘‘R  C” and shown in Table 8. The obtained results and graphs are discussed again with the experts for obtaining useful insights on the drivers. It has helped in understanding the efficient implementation of the traceability system in FSC.

[0.195, [0.216, [0.192, [0.103, [0.151, [0.046, [0.192, [0.071, [0.132, [0.256, [0.111, [0.121,

[0.236, [0.223, [0.139, [0.186, [0.043, [0.133, [0.192, [0.023, [0.117, [0.207, [0.058, [0.155,

‘‘i” towards other drivers and the value of  Dj shows the net effects received by driver ‘‘j” from other drivers. The prominence (Pi) and effect (Ei) of each driver are calculated using the Eqs. (8) and (9). The cause and effect of the driver depend on the sign (positive/negative) of Ei, if the value of Ei is positive, the corresponding driver is the net cause and if negative then net effect. Based on the value of ‘‘R  C” the cause and effect of the driver are decided and are shown in Table 7. The values of prominence (R + C), and effect (R  C) of each driver plotted on the graph and shown respectively in Figs. 2 and 3.

The result of the grey-DEMATEL analysis provides the importance order of each driver and classified the drivers into ‘‘influential” and ‘‘influenced” group. Significant research implications are derived from the cluster of the influential drivers as these drivers are having a high impact on the objective of the implementation of the traceability system. It should be noted that if we bring improvement in one or two drivers, it will not improve the overall system as there is inter-relationship among the criteria/drivers [64]. Therefore, to make effective decisions, the drivers need to be categorised into influential (cause) & influenced (effect) group. The influential group drivers are improved first which in turn improves the effect group drivers. The upcoming section details influential group and influenced group.

[0.224, [0.158, [0.101, [0.191, [0.049, [0.076, [0.130, [0.026, [0.179, [0.241, [0.159, [0.169, [0.194, [0.093, [0.048, [0.060, [0.073, [0.075, [0.153, [0.061, [0.087, [0.255, [0.027, [0.034,

Four drivers are identified as influential drivers and are significantly important towards the effective implementation of

0.120] 0.203] 0.088] 0.157] 0.098] 0.093] 0.109] 0.041] 0.075] 0.248] 0.087] 0.093] [0.052, [0.141, [0.019, [0.076, [0.018, [0.012, [0.030, [0.012, [0.018, [0.208, [0.010, [0.012,

7.1.

D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

Discussion on results

D02

0.277] 0.174] 0.164] 0.178] 0.162] 0.162] 0.225] 0.121] 0.175] 0.316] 0.093] 0.103]

D03

343

D01

Table 6 – Total-relation matrix (T).

6 ( 2 0 1 9 ) 3 3 5 –3 4 8

7.

0.328] 0.276] 0.189] 0.289] 0.173] 0.181] 0.235] 0.115] 0.257] 0.329] 0.229] 0.242]

D04

D05

0.336] 0.318] 0.252] 0.290] 0.140] 0.219] 0.276] 0.115] 0.222] 0.309] 0.168] 0.237]

D06

0.277] 0.290] 0.192] 0.176] 0.134] 0.097] 0.226] 0.092] 0.175] 0.308] 0.090] 0.100]

D07

0.251] 0.242] 0.179] 0.189] 0.126] 0.114] 0.113] 0.054] 0.134] 0.227] 0.113] 0.123]

D08

0.425] 0.422] 0.357] 0.366] 0.322] 0.267] 0.340] 0.111] 0.324] 0.427] 0.309] 0.302]

D09

0.242] 0.249] 0.265] 0.233] 0.180] 0.142] 0.215] 0.102] 0.144] 0.296] 0.183] 0.226]

0.179] 0.145] 0.094] 0.129] 0.050] 0.073] 0.063] 0.030] 0.056] 0.095] 0.042] 0.045]

D11 D10

0.214] 0.137] 0.226] 0.198] 0.156] 0.078] 0.124] 0.054] 0.208] 0.150] 0.098] 0.206]

D12

0.288] 0.213] 0.288] 0.266] 0.158] 0.104] 0.212] 0.101] 0.241] 0.203] 0.221] 0.139]

Information Processing in Agriculture

Influencing drivers

344

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6 ( 2 0 1 9 ) 3 3 5 –3 4 8

Table 7 – Cause and effect of drivers. Drivers

R

D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

[2.158, [1.929, [1.542, [1.520, [0.838, [0.638, [1.456, [0.306, [1.308, [2.367, [0.975, [1.173,

C 3.247] 2.982] 2.579] 2.671] 1.937] 1.693] 2.413] 1.082] 2.241] 3.249] 1.833] 2.028]

[0.602, [1.162, [1.702, [1.782, [1.712, [1.142, [0.792, [2.942, [1.302, [0.452, [1.162, [1.432,

R+C 1.411] 2.150] 2.842] 2.912] 2.881] 2.158] 1.866] 3.972] 2.479] 1.000] 1.849] 2.434]

the traceability system in FSC. Additionally, the improvement in the influencing drivers brings the improvement in the other influenced drivers. Thus, we need to focus first on these drivers. The highest ‘‘R  C” score is ‘‘food safety and food quality” (D10). The food safety and quality are non-negotiable because this is affecting people globally, who can suffer from various deadly diseases by consuming the unsafe food. ‘‘Food safety and quality” is a combined responsibility of the government, certification bodies, producers, manufacturer, logistics provider, retailers, and consumers [24]. Traceability system assures food safety and quality by providing information about food origin, processing, logistics and warehousing [11]. Thus, the ‘‘food safety and quality” attains the role of the primary driver as it motivates the FSC actors to implement the traceability system in their supply chain. The second most influential driver among the identified drivers is ‘‘legislation” (D01). Due to safety and quality concerns, regulations are introduced for the effective functioning of the traceability system in FSC. The legislation is an effective mechanism to bring change in the structure and practices so that the regulation is driving efficient implementation of the traceability system. Thus, large numbers of food producing companies implement traceability systems to meet the regulatory compliances. The necessity of the traceability can be understood by the example of EU where it is mandatory, while in USA it is voluntary. Due to this reason, foodborne

[2.765, [3.091, [3.243, [3.306, [2.550, [1.781, [2.255, [3.246, [2.617, [2.818, [2.141, [2.609,

4.658] 5.132] 5.421] 5.583] 4.818] 3.850] 4.279] 5.053] 4.720] 4.249] 3.682] 4.462]

RC

Cause/Effect

[1.550, 1.836] [0.767, 0.832] [0.159, 0.264] [0.266, 0.242] [0.873, 0.943] [0.505, 0.465] [0.657, 0.547] [2.634, 2.890] [0.002, 0.238] [1.917, 2.249] [0.190, 0.016] [0.263, 0.406]

Cause Cause Effect Effect Effect Effect Cause Effect Effect Cause Effect Effect

illness affects about 1% of the population in Europe while approximately 16.7% of the population is affected in USA [65]. Therefore, there is a need to enforce the effective regulation towards implementation the traceability in FSC to reduce the food born illness and protect the consumer. The third influential driver is ‘‘agro-terrorism threat” (D02). A disease outbreak in an agricultural system, which may affect financially by reducing the food supply and drive the food prices up in the market is an outcome of agroterrorism. A good example of agro-terrorism threat was observed in the USA livestock, where the cattle production sector, infection at a single point resulted in a loss of around 23 million cattle within eight days [66]. Traceability system plays an important to control such type of agro-terrorism activities. The prevention of the ‘‘agro-terrorism” can motivate and drive the FSC actor to implement the traceability system in their respective supply chain. The fourth influential driver is ‘‘certifications” (D07): such as organic food certification; fair trade certification, and Halal certification. The prerequisite of these certifications is the product origin, ingredient, processing and logistics and aligned operations in the documented form. Therefore, to obtain such type of information, traceability system is required in the FSC. These certification schemes drive the FSC partners to implement the traceability system to achieve improved sustainability and gain a competitive advantage.

Table 8 – Ranking of the drivers of the traceability system in an FSC. Drivers

Average ‘‘R + C” score

Ranking of drivers

Average ‘‘R  C” score

Ranking of drivers

D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12

3.711 4.111 4.332 4.444 3.684 2.816 3.267 4.149 3.669 3.533 2.911 3.535

5 4 2 1 6 12 10 3 7 9 11 8

3.386 1.599 0.422 0.508 1.817 0.970 1.204 5.525 0.240 4.166 0.206 0.668

2 3 7 8 11 10 4 12 6 1 5 9

Information Processing in Agriculture

7.2.

Influenced drivers

Based on the effect score (i.e. ‘‘R  C”) eight drivers of traceability implementation in FSC is fallen in the influenced group (effect group). The cause group drivers influence these effect group drivers. The most influenced driver is the ‘‘competitive advantage” which can be justified as it is influenced by many drivers such as food safety & quality, certification, and animal welfare. The remaining influenced drivers increasing order are as ‘‘sustainability”, ‘‘tracking of goods”, ‘‘welfare”, ‘‘ICT systems”, ‘‘information quality” and ‘‘information sharing” based on their received effect by the implementation of traceability system in FSC. These influenced (effect) group drivers are the requirement/benefit of the implement the traceability system in the FSC. These drivers can be improved with the improvement in the cause group drivers because it has a dependent relationship with the influence group drivers.

7.3.

Ranking of the drivers

The dataset (R + C) is known as ‘prominence’ and representing the ‘total cause and effect’. The higher value of the (R + C) greater overall prominence (visibility/importance/influence) of driver i concerning overall relationships with other drivers. Whereas, the (R  C), i.e. ‘Relation or influence’ shows the ‘net effect or cause’ of drivers [63]. The ranking based on the value of ‘‘R + C” shows prioritising based on the total cause and effect (importance) and ranking based on the ‘‘R  C” value shows the influential (net effect) order of the drivers. Based on the value of ‘‘R + C” and ‘‘R  C” score, the ranking of the driver of implementation of traceability is done in FSC. The increasing order of the importance based on the average score of ‘‘R + C” are D04 – D03 – D08 – D02 – D01 – D05 – D09 – D12 – D10 – D07 – D11 – D06. The most important driver for implementing the traceability system is ‘‘information quality”, and it needs the highest attention towards the implementation of a traceability system in FSC. Based on the average value of the ‘‘R  C” score the increasing order of drivers are D10 – D01 – D02 – D07 – D11 – D09 – D03 – D04 – D12 – D06 – D05 – D08. The most influential driver is the ‘‘food safety and quality” which provide the highest effect on the implementation of the traceability system in FSC. The increasing order of influence shows the effect of the drivers on the implementation of a traceability system.

8.

Research implications

This research work is beneficial for practitioners, policymakers, and researchers. The upcoming section discusses significant implication.

8.1.

Managerial implication

This research is beneficial to the food processing industries if they want to implement the traceability system. Managers/ professionals involved in implementing the traceability systems cannot focus on every driver of its implementation simultaneously. Thus, they need to focus on the highly influ-

6 ( 2 0 1 9 ) 3 3 5 –3 4 8

345

ential drivers primarily and then at later stage low influential drivers can be considered. The causal relationship among drivers proposed in this study can support the organisation in their decision making regarding the implementation issues of traceability system in FSC. This research motivates the practitioners of the developing countries to take the initiative towards the implementation of traceability system in their highly perishable food supply chain (fruit and vegetables) to prevent wasting out a massive amount of fruits and vegetables. This research may help the government/policymaker in understanding the antecedent and consequences of traceability system implementation in the supply chain. Effective implementation of the traceability system may assure good public health, which can motivate the government in legislating laws to regulate the traceability system in FSC. This paper also gives the direction to the practitioner to identify and evaluate the drivers to implement the traceability system in their circumstances. The practitioner such as managers of food producing companies can design their plan to implement the traceability system and take respective initiatives to implement the traceability system in their organisations. The policy maker can simulate/analyse the cause and effect group of drivers and formulate the policies which may enhance the performance of the FSC.

8.2.

Academic implications

This paper identified the major drivers to the implementation of the traceability system in FSC and evaluated their causal relationship among them. These drivers and their causal relationships can be utilised to develop a framework to implement the traceability system in FSC. These drivers are studies in another type of specific supply chain such as bulk grain supply chain, cold supply chain, Halal supply chain. The other advanced MCDM tools can be used for the decision support to the organisation in the context of traceability implications. The interrelationship among the drivers can be improved using other quantitative tools. The relative influence and strength of relationships evaluated using other MCDM techniques.

9.

Conclusion, limitation and future scope

In the current scenario, food safety & quality is receiving significant attention from practitioners and researchers. Implementation of the traceability system in FSC is vital to reduce the ‘‘food born illness”, ‘‘production & consumption of poor quality”, and ‘‘hazardous/unsafe food” [18]. One of the major issues is the identification of significant drivers towards the efficient implementation of the traceability system. This study aims to identify and evaluate the interrelationships among the drivers associated with the implementation of the traceability system in the FSC. The combination of literature review & expert’s input is used to identify twelve drivers significantly affecting the implementation of a traceability system in FSC. Therefore, to evaluate the interrelationships among the identified drivers, an appropriate

346

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MCDM technique (grey-DEMATEL) is used. Grey DEMATEL, categorises the identified drivers are into two groups: influential (cause) group and influenced (effect) group. The analysis shows that four drivers belong to the influential group and the remaining eight drivers from the influenced group. Further, the drivers are ranked on the basis of the ‘total cause and effect’ or importance (based on the R + C) and ‘net cause/effect’ (based on the R  C) separately. The finding of this research suggests that the most influential driver is, ‘‘food safety and quality” and the most influenced driver is, ‘‘competitive advantage”. This research is providing direction to policymakers to design the policies for the efficient implementation of the traceability system in FSC. This work is beneficial to industries that seek to implement the traceability system in their FSC and is understood to contribute to the ongoing research areas of food safety, quality, security and FSC management. This study is based on the expert’s inputs; however, the opinion of experts may be biased which may affect the results. The significant insights developed from this study, hold for a broader set of organisations (inside and outside India) needs investigation. Thus, it is interesting to validate or improve this research work through a case study or a simulation-based exercise. The results so obtained from this research can be further generalised through statistical tools such as structural equation modelling. The identified drivers are higher level drivers, and these drivers can be decomposed into sub-drivers, and further, more detailed level of analysis can be done. Additionally, this study is focused on the FSC, but in future studies, it can be extended to another specific supply chain such as fruit supply chain, Halal supply chain and perishable food supply chain. The identified drivers in the implementation of the traceability system in FSC can be analysed using other MCDM technique such as AHP, fuzzy AHP, ANP, TOPSIS, and VIKOR. in future studies. The structural relationship among the identified drivers can be investigated using the multivariate analysis such as structural equation modelling.

Conflict of interest

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The authors declare that there is no conflicts of interest. [18] R E F E R E N C E S

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