Overview
We propose using a Bayesian network (BN) to compute the likelihood of child labor in a supplier location based on the evidence from geography and sector, audits and news reports. BNs are a well-known probabilistic modeling technique introduced by Pearl (1988). BNs are based on Bayes’ Theorem. A BN is a directed acyclic graph (DAG) in which nodes correspond to random variables of interest and directed arcs represent direct causal or influential relation between nodes. The uncertainty of the interdependence of the variables is represented locally by a conditional probability table (Watthayu and Peng 2004).
BNs have previously been successfully applied to risk management due to their understandability and ease of information integration (Duespohl et al. 2012; Koks and Challa 2005; Wooldridge 2003). The key advantage of BNs is their explicit treatment of uncertain information supporting decision making (Reckhow 1999) and the possibility to include different types of sources into a single consistent model (Uusitalo 2007; Duespohl et al. 2012; Wooldridge 2003). A process of updates allows the inclusion of news as it comes up, continuously triggering updates of the likelihoods (Neapolitan 2003, 12–29). BNs tend to be easily communicable, fostering a common understanding (Duespohl et al. 2012; Correa et al. 2009). These features of BNs are of special interest for a quantitative risk model, as a decision-relevant information system must be understandable for company executives who have to make and defend their decisions based on the input (Hubbard 2009).
We suggest implementing a BN for each supplier location. We propose starting with the initial hypothesis that a supplier conforms to given, pre-defined social sustainability standards (e.g., a “code of conduct”). Then we calculate a relative measure for the likelihood of this hypothesis being false based on the evidence on the likelihood of a compliance breach. Hence, computing the likelihoods for individual supplier locations and relating them can provide a relative risk ranking (see Fig. 1). Figure 2 depicts the structure of a BN for a supplier location in the notation of Netica, which was used as implementation environment (Norsys 2013). Input data for the BN can come from multiple static or dynamic sources that either provide structured or unstructured data. To gather these inputs, survey data on child labor and audit scores are used. Text mining is leveraged to extract information on child labor incidents from unstructured news articles. For any new location, the prior distribution is taken. Once the location is known, the country, area type and sector are given and the priors can be updated. The priors for audits are updated anytime when the result of an audit of the respective supplier location is entered into the BN. The priors for observations are updated when information on a child labor incident relevant to the location is supplied as input to the BN.
The parameterization and testing of the system was conducted together with 28 experts, 13 of which with background in supply chain management, 6 with background in sustainability management, and 3 in general management, risk management and other, respectively. 15 of the experts consulted had more than 5 years of experience in their position while another twelve had between 1 and 5 years. Only 1 expert had less than 1 year of experience. 10 experts had their workplace in Austria, 7 in Germany, 2 each were from China, Malaysia, and the United Kingdom, and 1 each from the Czech Republic, Denmark, Romania, France and Columbia. The companies the experts worked for were in a variety of industries, but with a significant spike in manufacturing and wholesale/retail trade (18 experts), which have a potentially higher exposure to child labor. According to India, National Sample Survey Round 66 (NSS-R66) 2009–2010 (Understanding Children’s Work 2010), 23.6% of children from age 5 to 14 work in commerce in urban areas. This is the highest percentage of all sectors. 23 experts worked in companies with more than 1000 employees.
Although experts are subject to at least not fully rational (bounded) behavior (for an early discussion, see e.g. Edwards 1954), experts decide using a set of decision strategies that employ a significant amount of heuristics and learning from the past (March 1994; Shanteau 1988). Therefore, a comparison with expert output provides an indication of any overlap between the system’s approaches and these strategies. Moreover, it helps in discussing whether the experts’ responses can be covered by the system’s design.
Impact of region and sector
Let us now describe the various components of a supplier location BN in detail. Sources like UCW (Understanding Children’s Work 2014) show that the frequency of child labor varies regionally, depending on the country (C) and whether the area is rural or urban (R), and between sectors (S). Therefore, suppliers located in different areas and working in different sectors have different prior probabilities of employing children. These contextual priors can be determined on the basis of publicly available statistics on the number of children and companies per context C,R,S and the fraction of children who are non-self-employed workers. As one cannot derive the number of children working at a particular company from the statistical data available it makes sense to assume that the distribution of children working to the companies in the context is random. If one assumes that all child workers are randomly assigned to the \(NCOMP_{C,R,S}\) companies in the specific context the probability that a child is not linked to a specific company is
$$1 - \frac{1}{{NCOMP_{C,R,S} }}$$
Multiplying the frequency of children working non-self-employed in context C,R,S with the total number of children in the respective context yields the number of non-self-employed working children \(ANCL_{C,R,S}\). The probability that all children working in the respective context are not working for a particular company is given by the probability that a child is not linked to a particular company to the power of ANCL_C,R,S. Then, the probability that all these children are not working at the specific company is given as
$$\left( {1 - \frac{1}{{NCOMP_{C,R,S} }}} \right)^{{ANCL_{C,R,S} }}$$
and the probability of having at least one child working at the respective company is given as 1 minus the probability that all children working in the respective context are not working for a particular company:
$$P_{CL} = 1 - \left( {1 - \frac{1}{{NCOMP_{C,R,S} }}} \right)^{{ANCL_{C,R,S} }}$$
Table 1 shows the resulting \(P_{CL}\) values for supplier locations in India and Indonesia in year 2012 calculated on the basis of BPS Statistic Indonesia (2008), Diallo et al. (2013), International Programme on the Elimination of Child Labour (2013), Ministry of Statistics and Programme and Implementation of India (2006), The World Bank Group (2013) and Understanding Children’s Work (2010).
Table 1 Comparison of example child labor incident priors for different approaches, countries, area type, and sectors
To derive the standard deviation of this contextual prior, it is assumed that the errors made when calculating the probability of a child labor incident can be compared to those made when estimating the frequency of child labor in a particular country. Table 2 depicts the differences between the results of two surveys referring to the same reference period in nine countries analyzed (see Guarcello et al. 2010, 10). We interpret these differences as 2 \(\sigma\) intervals, and set the standard deviation of the contextual prior \(\sigma_{prior}\) to 13.32, the mean of the differences reported in Guarcello et al. (2010) divided by 2.
Table 2 Estimation of standard error of child labor rate calculations based on country comparisons
As we only have estimates for mean and variance the assumption of a normal distribution is the most parsimonious one based on entropy arguments (Cover and Thomas 1991, 409f.). We therefore assume that \(P_{CL}\) is the expected value of a normally distributed random variable. As can be seen from Fig. 2 negative values are dealt with in the BN by the categorizing the potential outcomes into intervals where the lowest one aggregates negative values. Initially, all realizations of the context are assumed as equally likely in the BN of a supplier location. Once the respective country, area and sector are known, one value is selected with 100% probability.
In order to validate the method for determining the contextual prior, the experts were asked to rank the four hypothetical supplier locations shown in Table 3 according to child labor risk. Based on the data provided, the experts suggested an initial ranking that is in many ways comparable to the one created using the model. In order to better compare the two approaches, Fig. 3 introduces a scaled measure. This measure was derived by norming the best-rated supplier to zero and the worst-rated supplier to one. For the model, relative distances were then calculated using the mean prior values. The model was set up using the values for the prior that correspond to the ones used in the questionnaire. The adjustments for the expert responses were calculated using a weighted score (response frequency \(f\) and weight \(w\) of 1 for best rank, 4 for worst rank with combination of \(f.w\)) and norming to 0–1. It must be noted that the experts’ responses have not been interval scaled, so the ordering is important. Experts tend to view the location of supplier B as worse than the one for supplier D when compared with the model under these assumptions. Nevertheless, in general the model prior and expert responses appear to have a comparable pattern.
Table 3 Supplier locations used for validation
A more granular analysis of the result, however, shows that experts often strongly disagree in their judgment of the riskiness of the different suppliers (see Fig. 4). The rank selected by the majority of experts is only equivalent to the one calculated by the model for ranks three and four. Suppliers B and D show a particularly large spread of answers.
Impact of audits
Audits are limited in what they can measure and can only be conducted within a defined timeframe, leaving the suppliers alone before and after this timeframe (Locke et al. 2009). Also, a higher number of compliance audits does not suggest that a supplier is better than others. Rather, often the compliance level of the supplier stays the same and sometimes even worsens (Locke et al. 2007). Consequently, we only include the result of the last audit into the BN and assume that the variance of the breach likelihood increases with the time since the last audit.
In order to infer the relationship between audit score and breach likelihood we asked the experts “Which probability of having a child labor incident (if only audit data is taken into consideration) would you associate with a random supplier reaching either a minimum (worst), a medium, or a maximum (best) audit score?”. As can be seen from the high standard deviations provided in Fig. 5 and Table 4, the relation between audit scores and average probability of an incident is judged to be very ambiguous. While some experts put a lot of trust in audit scores, others see only limited value. Even if an audit attributes the best score to a supplier, experts tend to still see a certain probability of an incident. Similarly, the worst audit score does not necessarily indicate that child labor is present.
Table 4 Average estimated probability values (incl. standard deviation) of incident for different audit scores
We assume that an audit yields results in the range [\(a_{min}\), … \(a_{max}\)], where the minimum audit score \(a_{min}\) is assumed to be greater or equal zero. As we only have estimates for mean and variance, the assumption of a normal distribution is the most parsimonious one based on entropy arguments (Cover T, Thomas J 1991, 409f.). Based on these judgments, it is assumed that the audit likelihood \(P_{audit}\) follows a normal distribution, whose expected value \(E\left( {P_{audit} } \right)\) is related to the audit score via
$$E\left( {P_{audit} } \right) = m - a\times\frac{m - n}{{a_{max} }}$$
where \(a_{min} \ge 0\) is the a minimum audit score and \(a_{max}\) the maximum audit score. For the prototype, the minimum audit score \(a_{min}\) is set to 0 and the maximum \(a_{max}\) to 5, and the parameters m and n are set to m = 57 and n = 48.2. This formulation fits the values given in Table 4 and was considered valid by the experts. Table 5 contains the time-dependent values used for the standard deviation of the audit probability.
Table 5 Audit probability standard deviation values depending on time since last audit
For the prior of the audit score, a normal distribution is assumed with a mean of 4 and standard deviation of 1, while for the variance of \(P_{audit}\) we assume a prior value of a mean of 9 months with a deviation of 3 months. If an introductory audit for a supplier location is entered (i.e., marking one discretization with 100% probability), the prior for the node has no influence anymore. The respective distributions were derived from discussions with the experts and given to them for validation.
Impact of observations
Both observations of child labor incidents in related contexts and news on drivers affecting the demand and supply of child labor are candidate inputs for the determination of the breach likelihood. Empirical research has uncovered a multitude of factors influencing the extent of child labor. Only few of these factors such as socio-economic dislocation (economic crisis, political and social transition) or production peaks/labor shortages are observable from external information and have short-time impact. These are difficult to detect automatically, though, as they cover a wide array of happenings, including earthquakes, volcanic eruptions, strikes, or demand surges. Moreover, while the literature identifies connections between these events and child labor, the propensity of the effect varies by context. Also, descriptions of actual child labor incidence observations are more homogeneous than descriptions of factors that influence child labor. Thus, descriptions of child labor incidents are easier to detect and to codify automatically than descriptions of factors influencing child labor. Also, one can argue that they indirectly cover relevant influence factors, as socio-economic dislocation or production peaks/labor shortages should affect all companies operating in a similar context. We therefore choose to only include news reporting incidents of child labor into the expert system.
In order to determine the impact of various types of news on child labor the experts were confronted with the four hypothetical news articles in the Appendix 1. Then they were asked “How much does the news report influence the perceived probability of a child labor incident at Supplier B?” and a five-step Likert influence scale was used (extremely influential-not at all influential; Wigas 2006) to code the answers. As can be seen from the score depicted in Fig. 6 all articles provided have at least a slight influence on the experts’ decisions. Comparing articles one, two and three, the additional geographic detail (region) is nearly as influential as the explicit mentioning of the company. Hence, closely related geographic proximity drives relevance. This is not the case for the article obtained through social media (the worsening could also be due to the reference to a different sector).
Consequently, we suggest considering two variables, credibility and relevance, to represent the content quality of an observation. Credibility c is defined as comprising the content of evidence captured by a sensor which includes veracity, objectivity, observational sensitivity, and self-confidence (Blasch et al. 2013), while relevance r assesses how a given uncertainty representation is able to capture whether a given input is related to the problem that was the source of the data request (Blasch et al. 2013). In other words, the model understands relevance as capturing how closely the messages used as input for a certain supplier location are in fact related to the supplier location. In order to derive a relevance measure, the availability of dimensional attributes in the news articles is used as an approximate indicator. The more an observation can be linked to a certain location in a granular and specific way, the more relevant it is. If observations with partly conflicting dimensional information are included, the relevance can only be derived based on the non-conflicting dimensional information. Credibility is suggested as being defined either at an input channel or source level in order to cover different media types as completely as possible.
Tables 6 and 7 show the particular values used for credibility c and relevance r.
Table 6 Credibility values based on publishing channels
Table 7 Relevance values based on dimensional attribute availability
A BN is initialized at a particular point in time which can serve as a basic reference point. Until this time, zero or more observations of child labor incidents may have been stored and a set of observations can be retrieved as discussed above. In general, when revising the probability based on evidence from textual media sources, two options may be considered. Either only the latest observation is entered as a single finding or the network is continuously updated with the evidence from new observations. If the observations are assumed to be independent, each one is likely to include valuable information. Consequently, the BN will be modeled using the latter option, allowing the inclusion of evidence from multiple reports. It is then the task of the input procedure to ensure independence between the incidence observations. Relevance and credibility can be evaluated for each observation as described above using the pre-configured values in Tables 6 and 7 given an observation’s data. Even observations with low credibility or relevance are understood to increase the overall observational probability. Given these assumptions, the expected value of the observational likelihood \(P_{obs}\) needs to be a monotonically increasing function of the number of independent incident observations included: \(\forall f \ge 0:E\left( {P_{obs} \left( {f_{1} ,c_{1} ,r_{1} } \right)} \right) \le E(P_{obs} (\left( {f_{2} ,c_{2} ,r_{2} )} \right);\,f_{2} \ge f_{1} ;0 \le p,c_{1,2} ,r_{1,2} \le 1;\,f_{1,2} \in {\mathbb{N}.}\)
Both the credibility and the relevance will be continuously updated with new evidence from observations. The evidence will then be entered into the BNs whose context overlaps with the observations’ context. This leads to a model containing frequency f, credibility c, and relevance r as variables. These are combined via the equation \(x = f\cdot\left( {c + r} \right)\), which fulfils the requirement of monotonicity requirement formulated above and conforms to the notion that both credibility and relevance increase with frequency in a simple way. However, this monotonically increasing function x has no defined upper bound. Therefore, a scaling function is needed to return a value between 0 and 1 for the mean of the normal distribution of \(P_{obs}\). For this purpose, a monotonically increasing function with limit 1 is suggested. This can be achieved with an inverted, shifted hyperbola. The frequency score function s(x) is suggested for this (the function can be parameterized through the parameter τ, which is initially set to 5): \(s\left( x \right) = 1 - \left( {\frac{1}{{1 + \frac{x}{\tau }}}} \right)\). This formula has the desired property of limit 1 and is simple.
The node of the observational likelihood \(P_{obs}\) is represented with a normal distribution given relevance \(r\), credibility \(c\), and frequency \(f\). Hence, knowing about \(f\) observations for a specific supplier location, the calculated likelihood of a child labor incident should be within a predefined confidence interval. This interval should be smaller the higher the number of observations with high reliability and credibility received. For a known standard distribution, 95% of its probability mass lies within the mean \(\mu\) plus/minus 1.95994 times the standard deviation. If, as defined by the user, the area covered by the 95% interval is \(p^{\prime}\) percent points if no observation has been received and \(p^{\prime\prime}\) percent points if ten fully credible and relevant observations have been received, then the respective standard deviations in percent points can be calculated with \(\sigma \left( p \right) = \frac{p/2}{1.959964}\). For example, setting \(p^{\prime}\) to 40 and \(p^{\prime\prime}\) to 10 percent points yields \(\sigma^{\prime} = 10.204\) and \(\sigma^{\prime\prime} = 2.551\). \(\sigma\) is seen dependent on the values of \(f, r, c\) and a linear functional connection is assumed.
$$\sigma \left( {f,r,c} \right) = \alpha - \beta .f.r.c$$
Using \(\sigma^{\prime}\) and \(\sigma^{\prime\prime}\), the values for \(\alpha\) and \(\beta\) can be determined leading to the following function
$$\sigma \left( {f,r,c} \right) = \alpha - \beta \cdot f\cdot r\cdot c = 10.204 - 0.7653\cdot f\cdot r\cdot c$$
As shown in Thoeni (2015), this specification is consistent with the above stated monotonicity condition. Besides fulfilling the monotonicity requirement, it also was considered plausible by the experts.
Text mining child labor incidence observations
Methodology
Manually reading, extracting and coding child labor incidents from a continuously arriving stream of text is clearly infeasible. However, automatic extraction is difficult: the examples shown in Appendix 2 give an impression of the diverse way in which child labor is depicted in various texts. First, the datasets also include broader reports. They often reference a broader array of different child labor incidents (#1, #2), together with contextual references (e.g. #3). Beyond these general reports, other texts also give broader references to a combined set of multiple child labor cases (#4, #5). Incidents may be depicted in narrative fashion building on a single individual case (#6) or at least referencing it directly (#7). However, incidents are also reported directly, as can be seen in the later text excerpts (#8 to #11). There, the reports on child labor can also include child labor categories such as prostitution, begging, or domestic work that are less relevant from a company perspective (#4).
The automatic text mining and BN updating procedure depicted in Fig. 7 has been developed to cope with this challenge. Given the low frequency of child labor incidents and the large variety of forms in which these are expressed, we employ data-driven document classification with candidate set reduction and tagged event extraction. In particular, the following four text mining steps are performed:
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1.
Preprocessing and Candidate Set Reduction A tokenizer splits the words and other characters, a sentence splitter detects sentence boundaries, and a POS-tagger is used to differentiate word lemmas. As sentences close together in a text tend to be on the same topic (Zha 2002) and a direct mention of “child labor” may be seen as the most obvious trigger of a child labor incident event, a distance-based approach using a cut-off distance is suggested to prune negative cases. The distance is measured as the number of characters between “child” and a word indicating “labor.” Indicative words may be synonyms, hypernyms, or other related word sets. Stop words are eliminated between “child” and “labor”. The cut-off distance to be used is determined together with model selection and parameter estimation so as to optimize the F1 measure combining precision and recall.
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2.
Classification The thus identified text passage between “child” and “labor” could describe a child labor event. This could in turn contain several child labor observations, as demonstrated by the following example: “As many as 36 cases from Koderma and 22 from Khunti were brought to Dube´s notice when he visited these districts. Such incidents include a girl from Khunti missing since 2009 when she went to Delhi for work […]”. Classification based on the relative word frequencies of the words within the feature is used to verify if the extracted feature actually deals with a child labor event. For training, the Reuters TRC2 corpus containing 1,800,370 news articles (Reuters, National Institute of Standards and Technology 2009) was used. 16,948 articles in Reuters TC2 contained the word “child” and manual tagging yielded 117 articles that contained a child labor event. A number of variants for feature construction (maximum or minimum distance between “child” and “labor” including or excluding leading or trailing words to complete sentences), model selection (SVM, PAUM, KNN, NB, C4.5) and cut-off values were tried out using this gold standard (Li et al. 2005; Quinlan 1993). This resulted in the choice of SVM with a cutoff distance of 80 applied to the sentences within the maximum distance as the best variant with precision 97.1%, recall 73.7% and F1 value of 83.4%.
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3.
Event extraction The result of the classification step is a list of news reports of which each should (and in the case of 100% precision actually does) contain at least one child labor incident observation referring to a child labor incident event. The goal of the next step is to extract these incident observations together with the corresponding attribute values from the text. DBpedia Spotlight and Open Calais by Reuters were used for geography tagging, and Open Calais for company tagging, yielding respective URIs. Sector tagging must yield a sector conforming to United Nations ISIC industry classification so as to conform to the statistical data. This was done via a rule-based gazetteer and an ML-based approach, where the labels and descriptions from the ISIC classes were used to train a classifier based on the lemmas of the respective tokens. Given that different taggers produce syntactically and also partly semantically different tags, they have to be aligned to a common tag set via a domain ontology (Gangemi 2013; Rizzo et al. 2012).
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4.
Independent Observation Extraction Output of event extraction is a frame with zero to several values for each of the dimensions (components of context) location (hierarchy country, region, city), company and sector hierarchy. In a next step, child labor incident observations that contain at most one value per dimension are generated using heuristics based on proximity and child labor distances. This procedure yielded an overall F1 value of 44.7%, and F1 values of 57.6% for geography, 74.4% for organization and 27.2% for sector in the best alternative. Comparing these results to those obtained for the classification step reveals that observation extraction shows weaker results. This is particularly true for the sector dimension. Consequently, a manual cleaning step appears necessary before values are eventually entered into a risk management system in a productive scenario. Finally, these child labor observations are checked for duplicates by checking if there are no conflicts with already stored observations within a given time-frame. If this is the case, the old observation is deleted and the frequency of the new one is increased (see Fig. 8).
The output of these steps is a list of independent incident observations with fully or partly filled attributes linked to a domain ontology representing this frame. These can then be incorporated into the risk model by activating the supplier location BNs whose context variables overlap with the dimensions of the observations incorporating hierarchical relationships.
Validation of input data availability
A key criterion for the usefulness of the system is the availability of child labor incident observations in sufficient number and granularity. To probe into this issue, the suitability of two publicly available data sets was investigated. News was gathered through searches on Google News, the European Media Monitor, and two selected RSS feeds, resulting in a very broad coverage of news. Altogether, this dataset contains 48,339 news articles published between 15th March 2011 and 16th September 2014. Most articles were retrieved from the British Broadcasting Corporation (BBC), Times of India, The Daily Mail, The Guardian, and The Hindu.
Also, a list of NGOs that potentially post on Twitter was built in order to retrieve the related content. In order to cope with the amount of data, we restricted ourselves to India, given the importance of English (Crystal 2004) and prevalence of child labor (Understanding Children’s Work 2010), and Indonesia, due to its high Twitter use (Bennett 2012) and the presence of child labor (Understanding Children’s Work 2009). 5138 unique NGO websites (predominantly in India) were automatically parsed to determine whether a link to Twitter was provided on the first page that opened by following the website link (see Table 8). Altogether, this resulted in a set of 778 unique twitter accounts. Using the Twitter search API iteratively, produced a set of roughly one million tweets, published between 11th July 2007 and 10th March 2014. When downloading each tweet, external links (included in the tweet) were followed. This website data is stored together with the tweet. The tweets have been reduced to a set where each linked text contains “child” at any place in the text, similar to the assumption used in the text mining methodology. Consequently, 85,020 texts were then stored as a new set for further processing.
Table 8 Overview of number of NGOs collected with and without websites, including sources (author’s representation)
The two sources were input into the text mining procedure described above. This resulted in 708 texts from the news data set and 280 texts from the NGO data set (see Fig. 9). The results of the analysis of the random selection of 100 articles from the news and NGO datasets are presented in Fig. 10. Manual inspection shows that the large majority of articles in the sets (96 and 89% respectively) do in fact include business-related child labor incidents. Only five cases had no dimension, i.e., the text contains a reference which can be classified as a child labor incident under the definition used in this thesis but which is too broad to be considered a dimension for text mining. Furthermore, only 24 cases mention only the country, but in many cases the additional detail does not go significantly beyond this. In fact, most articles also provide the sector (not shown above) without giving any details as granular as a geographic reference to the city level. However, the sample from the NGO dataset has more geographically detailed cases. Analyzing the types of links in the NGO dataset random sample reveals that a large share of NGO posts redirect to classic news pages such as The GuardianFootnote 1 when the tiny URLsFootnote 2 in the posts are expanded. Nevertheless, many unique references (41 in total) still link to non-classic news pages such as blogs, NGO websites or videos (with descriptions), and special news pages or special websites. Thus, one can state that publicly available sources provide child labor incident observations in sufficient number and granularity.
Determination of breach likelihood
The final node of the BN models is the likelihood of a breach of child labor compliance standards \(P_{breach}\). It combines the contextual prior with the audit and observational likelihoods, thus revising its prior. Its distribution is modeled via a sampling process. Netica creates this using a Monte Carlo sampling based on the model equations, i.e. calculating the result for each of the nodes of the BN based on the equations outlined above. In order to determine the weight to be used for the three contributing nodes, the experts were asked “Which weight would you give the following three probabilities if they are combined in order to calculate an overall probability of a child labor incident at a supplier location? The sum should equal to 100%.” Table 9 summarizes the answers. It turns out that audits are still seen as providing the most important source of information, being most frequently weighted highest (Fig. 11). In contrast, statistics are seen as the least important for a ranking. All three are significantly different from zero (0.000 level).
Table 9 Average estimated relative importance of independently found probability values for overall supplier risk judgment Additionally to these values, the final node incorporates the distinction between supplier locations that have signed the “code of conduct” and (potential) supplier location that have not. It is assumed that suppliers need to comply with the code of conduct irrespective of a signature. However, not signing it increases the breach risk significantly. Thus, for locations that have signed “codes of conducts”, the prior probability’s mean is shifted by a user-defined factor, for which 0.25 was used. This factor is modeled via a discrete node with two states representing whether the supplier has signed a code of conduct or not. The mean of the resulting final node can be used to establish the prioritization of the supplier locations.