order to provide both a better palpableness and a better understanding of the societal pillar, we split it into its two sub pillars Working Environment, named ‘Work’ and Ethical Issues, named
‘Ethics’. Thus, the labours’ respective working conditions as well as the ethical issues related to their employments need to be measured in the same logic than the economical and the ecological areas.
Working Environment related Indicators
In European countries, the broad terms of employees’ working conditions are legally determined. Company owners may accord additional advantages, as, for example, additional holidays, etc. but those cannot be considered in terms of sustainability. In fact, to get sustainability, both employees and companies should benefit from enhanced working conditions. The indicators used to analyse the working environment should therefore consider the well-being of all stakeholders. We hence consider that a company should analyse the indicators [1] Trainings per Employee to Improve Skills (LLL18), [2] Security of Employment (SE), and [3] Health, Security and Safety (HSS).
Trainings per Employee to Improve Skills (LLL)
In most companies, employees are trained so that they may improve their professional skills and savoir-faire. It is evident that this KPI can easily be measured, as the human resource (HR) department normally stores this kind of information. The latter do not only consider the specific work sequences as trainings concerning security and safety, compliances, or hygiene are not less important in the matter of improving the working environment. Nevertheless, as people advance in their job position, they will not necessarily stay in the same department. If there is personnel change within a department, the trainings allocated to the considered division need to be reallocated. It is hence evident, that this kind of reallocation cannot be tracked within a company. For this reason, we consider the average of trainings given per assigned employee within a month t. Let i be the ith variable of the sample, whereas i = [1, n].
Let ULLL be:
ULLL : Trainings per Employee, Numerical, Real, 0.01, Hours of Training per Assigned FTE, N = [0,1]
f(LLL) = ∑ 𝐿𝐿𝐿it ∙ αt
LLL = training hours given during the considered month t
and αt = Percentage of FTEs assigned to the considered SC during the considered month t.
Security of Employment (SE)
The question to be answered by this KPI is: In case of reorganisation of the company, will there be redundancies? Or may labour have to change the department to avoid them losing their employment. The Security of Employment (SE) can obviously not be measured via numerical variables as it considers peoples’ subjective opinion about how they perceive their situation. For this reason, let UES be:
USE : Security of Employment, linguistic, ordered,
L
SE= { very poor ; poor ; medium ; good ; excellent }Where
L
SE= Linguistic set of SE. Health, Security and Safety (HSS)
The Health, Security and Safety (HSS) indicator is twofold. On the one hand, it considers the products’ security on the other hand it regards the labours’ safety. The merchandises have to be delivered on time and without any damage. In case of high valued goods or pharmaceuticals, the security restrictions are much more severe as in case of FMCG’s, for example. To rephrase, the level of security highly depends on the considered commodities.
The workers’ safety could be measured via the hours of security trainings passed, the obligation of wearing safety shoes, working gloves, safety helmets or vests, or via the amount of accidents within the different considered departments. It is not possible anymore to find out the details per months in a retroactively manner, but since we need to use the same methodology for the whole case study, we could gather the data via questionnaire or calculate this indicator in another way. For this reason, we consider the number of accidents or incidents resulting in either material damage or human injuries, and calculate the average per assigned FTE within a given month t.
Let i be the ith variable of the sample, whereas i = [1, n].
Thus, let UHSS be:
UHSS : Health Security and Safety, Numerical, Real, 1, Number of Accidents per assigned FTE, N = [0,1]
f(UHSS) = ∑ 𝐴𝑐𝑐t ∙ αt
where ACC = Accident or incident during the considered month t
and αt = Percentage of FTEs assigned to the considered SC during the considered month t.
Ethical related indicators
To our knowledge, the ethical sub-pillar has been neglected in former works until now.
In order to measure how far ethical principles have been implemented into a company’s working procedures, we suggest using three essential indicators, namely, [1] Gender Equality (GE), [2] Actions taken against Xenophobia and Discrimination (AXD), and [3] actions taken to increase Employees Motivation (EmMo).
Gender Equality (GE)
Nowadays, Gender Equality (GE) is considerably gaining in importance. In order to improve a company’s performance, the European Commission has voted a female quota of 40% objective in non-executive board-members positions in publicly listed companies. One of the main economic arguments is that greater gender diversity would have the potential to improve a company’s performance (European Commission, 2013).
However, in this work, the indicator ‘female quota’ would be useless. Effectively, in this work, we consider sustainability from a SC angle. In the logistic sector, stricter in the
Every company will consider this kind of data being highly confidential. It is hence not surprising that this data needs to be requested in an anonymised way. In other words, the records are not handed out as raw data, but are clustered and treated as shown in Figure 24. The handed out average salaries can then be compared.
Hence, let USalary be:
USalary : Salary, Numerical, Real, 0.01, Average Salary in Euro, N = [0,1]
f(Salary) = 𝑊1β ∑ 𝑆𝑎𝑙𝑎𝑟𝑦𝑖𝑤βt − 𝑀1β ∑ 𝑆𝑎𝑙𝑎𝑟𝑦𝑖𝑚𝛽𝑡 Let i be the ith variable of the sample, whereas i = [1, n],
and Wβ = Number of female workers within the cluster β, and Mβ = Number of male workers within the cluster β,
and Salaryiwβt = Salary of a female employee included in cluster β during the considered month t, and Salaryimβt = Salary of a male employee included in cluster β during the considered month t.
If the above formula results in a negative number, the average salary of female workers within the cluster β is less than the average salary of male workers within this same cluster. If the result of the above equation is zero, male and female workers of a given cluster β have the same average salary. Finally, if the result is a positive number, the average salary of female workers within the cluster β is greater than the average salary of male colleagues within this same cluster.
Figure 24 – Composition of the Clusters to calculate the Difference of Salary Indicator
For confidentiality reasons, we integrated those results, being the differences of salaries, into a linguistic set named DifSalary:
Let UDifSalary be:
UDifSalary : Differences in Salaries, Linguistic, Ordered,
Where
L
DifSalary= Linguistic set of DifSalaryThe other data entering into this KPI are the assessment of the abidance concerning the Female Quota (FeQuo), as well as the employees’ subjective opinion about how they perceive the situation within the company considering gender equality (SubGE), which are both measured in a qualitative way, i.e. via questionnaires.
Let UFeQuo be:
UFeQuo : Abidance concerning the Female Quota, Linguistic, Ordered,
L
FeQuo= { very poor ; poor ; medium ; good ; excellent }Where
L
FeQuo= Linguistic set of FeQuo Let USubGE be:USubGE : Subjective opinion about Gender Equality, Linguistic, Ordered,
L
SubGE= { very poor ; poor ; medium ; good ; excellent }Where
L
SubGE= Linguistic set of SubGEHence, by providing the above mentioned indicators, experts may assess the GE indicator in a qualitative manner. Their responses will be converted into quantitative ones afterwards, so that:
UGE : Gender Equality, Linguistic, Ordered,
L
GE= { very poor ; poor ; medium ; good ; excellent };where
L
GE= Linguistic set of GE Actions Taken Against Xenophobia and Discrimination (AXD)
Many companies, especially large concerns, are exposed to the risk of racism and xenophobia. To measure this kind of indicator, as no labourer only works on one single SC, we could count the total number of known actions taken against xenophobia and discrimination (AXD) and to break this number down to the number of employees working on the considered SC. Nonetheless, one has to take into account that in those cases, there is always an unknown number of unreported cases. We suggest that an expert’s feeling about the situation may be more confident than the number of reported cases. The unreported ones could be estimated, but even experts may not dare to estimate such important values. We hence suggest evaluating the AXD indicator in a linguistic manner. Let UAXD be:
UAXD : Actions taken against Xenophobia and Discrimination, linguistic, ordered,
L
AXD= { very poor ; poor ; medium ; good ; excellent}Where
L
AXD= Linguistic set of AXD. Actions Taken to Increase Employees’ Motivation (EmMo)
Depending on the company, the board of director attaches more or less importance on actions taken to increase employees’ motivation (EmMo). To achieve such a growth of motivation, financial bonus can be paid. Depending on the local legislation, this may be considered improper or even illegal. To show their respect concerning their employees’ hard work during a certain period, some companies organise summer celebrations, Christmas dinner or after-work events. In addition to this, they may give some chocolates for Easter or Christmas. Another possibility to show respect in a more personal interaction is to congratulate the labourers for their birthdays: Those data are easy to get, as every employee has to leave day and place of birth in the Human Resource (HR) department. In short, no limits are set to imagination in regard to motivate employees. According to this, it seems logical to evaluate the EmMo KPI in linguistic terms. Let UEmMo be:
UEmMo: Actions taken to increase Employees’ Motivation, linguistic, ordered,
L
EmMo= {very poor ; poor ; medium ; good ; excellent }Where