Nowadays, demanding legal regulations as well as sophisticated customer needs force companies in electronics and automotive industries to provide a multitude of di erent sustainability indicators. Since their products usually contain numerous components and subcomponents, companies must deal with complex, intransparent data collection processes along their supply chains in order to nally deliver valuable data. A myriad of di erent automatic and manual tasks, potentially long-running processes, and quickly changing situations result in great variability that is hard to handle. In the SustainHub project, a dedicated information system for supporting data collection processes is developed. Thereby, core challenges as well as state-of-the-art were systematically gathered, consolidated as well as assessed. The condensed results are presented in this paper.
These days, companies of the electronics and automotive industry face steadily growing demands for sustainability compliance triggered by authorities, customers and public opinion. As products often consist of numerous individual components, which, in turn, also comprise sub-components, heterogeneous sustainability data need to be collected along intertwined and intransparent supply chains. Thereby, highly complex, cross-organizational data collection processes are required, featuring a high variability, e.g., through dynamically integrating companies' employees and information systems (ISs). Further issues include incompleteness and varying quality of provided data, heterogeneity of data formats, or changing situations and requirements. Until today, there is no dedicated IS supporting companies in creating, managing and optimizing such data collection processes. Within the SustainHub1 project, such a dedicated information system is being developed. In this context, use cases, delivered by industry partners from the automotive and the electronics domain, have been intensively studied in order to consolidate core challenges and essential requirements regarding the IT-support of data collection processes. In relation, state-of-the-art has also been deeply studied to assess whether existing approaches and solutions satisfy the requirements. As a result, this paper systematically presents the condensed core challenges and state-of-the-art considering complex sustainability data collection process along today's supply chains. This domain is well suited for eliciting such challenges because of the complexity of the supply chains on the one hand and the requirements imposed by emerging laws and regulations on the other. However, they can be transferred to many other domains as well. Thus, this contribution identi es 7 core challenges for data exchange and collection in complex distributed environments and also reviews approaches in place to solve these challenges. Thereupon, future research in the area of adaptive business process management can be aligned to extend existing approaches for supporting more variability and dynamics in today's business processes.
Therefore, the fundamentals and an illustrating example are introduced in section 2. Subsequently, seven data collection challenges are unveiled in section 3, exposing concrete ndings, identi ed problems and derived requirements. In section 4, the current state-of-the-art is presented based on its origin. Finally, section 5 rounds out this paper giving a conclusion and an outlook.
This section elaborates on the domain of sustainable supply chains and gives background information.
In today's globalized industry, the development and production of many products
is based on intransparent, complex supply chains with dozens of interconnected
companies distributed around the globe. To ensure and extend competitiveness,
complex communication tasks must be managed properly for e ective and e
cient interorganizational processes. Generally, such cross-organizational
collaboration involves a variety of di erent manual and automated tasks. Involved
companies signi cantly di er in size and industry background, and they use
various di erent ISs, which are not able to intercommunicate easily. Due to this
heterogeneity, neither federated data schemes, unifying tools nor other concepts
can be realistically introduced without considerable e ort [
As sustainability is is an emerging trend, companies even face a new challenge in their supply chains: sustainable development and production. The incentives 1 SustainHub (Project No.283130) is a collaborative project within the 7th Framework Programme of the European Commission (Topic ENV.2011.3.1.9-1, Eco-innovation). are given by two parties: On one hand, legal regulations, increasingly issued by authorities, force companies to publish more and more sustainability indicators (like greenhouse gas emissions in production or gender issues) on an obligatory basis. On the other hand, public opinion and customers compel companies to provide sustainability information (e.g., organic food) as an important base for their purchase decisions.
Examples include ISO 14000 standard for environmental factors in production, GRI2 covering sustainability factors or regulations like REACH3 and RoHS4. Overall, sustainability information involve a myriad of di erent indicators. It relates to social issues (e.g., employment conditions or gender issues), to environmental issues (e.g., hazardous substances or greenhouse gas (GHG) emissions), or to managerial issues (e.g., compliance issues).
There already exist tools at market providing support for the management and transfer of sustainability data: IMDS5 (International Material Data System), for instance, is used in the automotive industry and allows for material declaration by creating and sharing bills of materials (BOM). A similar system exists for the electronics industry (Environ BOMcheck6). Despite providing useful support in basic data declaration and exchange tasks, these tools clearly fall short in providing dedicated support for the sustainability data collection and exchange along the supply chains.
To illustrate the complexity of sustainability data collection processes in a distributed supply chain, we provide an example. The latter was composed with the problems and requirements provided by SustainHub's partner companies for the automotive and electronics industry by formal and informal surveys and interviews. Please mind that data collection in such a complex environment does not have the characteristics of a simple query. It is rather a varying, long-running process incorporating various activities and involving di erent participants.
The example illustrated in Fig. 1, depicts the following situation: Imposed by regulations, an automotive manufacturer (requester) has to provide sustainability data considering its production. This data is captured by two sustainability indicators, one dealing with the greenhouse gas emissions relating to the production of a certain product, the other addressing the REACH regulation. The latter concerns the whole company as companies usually declare compliance to that regulation on a company basis.
To provide data regarding these two indicators, the manufacturer has to gather related information from his suppliers (answerer). Hence, it requests a 2 Global Reporting Initiative: https://www.globalreporting.org 3 Regulation (EC) No 1907/2006: Registration, Evaluation, Authorisation and Restriction of Chemicals 4 Directive 2002/95/EC: Restriction of (the use of certain) Hazardous Substances 5 http://www.mdsystem.com 6 https://www.bomcheck.net Process: Request 1 Process Parameters Requester Preferences: Completeness Quality Validity Process: Request 2 Integrate Data Check for available Data
Find / Select Right Contact
Submit Data Request
Approve Data
Request
Sign Data Available Data Completeness Quality Validity period
Answerer 1 Approval Processes Systems Platforms Formats
Answerer 2 Approval Processes Systems Platforms Formats
Indicator: GHG Reference: BoM Emissions – 2 Positions Validity date: 1 Standard: ISO year 14064
Request 1
IndCicoamtoprl:iaRnetach CRoemfepreanncyeX: Due date: 2 Verification: months in future Legal statement
Request 2 Answerer 3 Approval Processes Systems Platforms
Formats Check for available Data
Approve Data
Request Approve Data
Request Check for available Data
Find / Select Right Contact
Submit Data Request
External Assessment
Convert Data
Col ect Rrequested
Data Start Event
EndEvent
ANDGate
XOR Gate
Activity
Subprocess REACH compliance statement from one of its suppliers. To get the information, the activities shown in the process Request 1 have to be executed. Furthermore, the product for which the greenhouse gas emissions shall be indicated has a BoM with two positions coming from external suppliers. Thus, the request, depicted by the second work ow, has to be split up into two requests, one for each supplier.
Hence, the basic scenario involves a set of activities as part of the data collection processes. Some of these are common for the requests, e.g., on the requester side, checking available data that might satisfy the request, selecting the company and contact person, and the submitting the request. On the answerer side, data must be collected and provided. The other process activities are speci cally selected for each case. Thereby, the selection of the right activities is strongly driven by data (process parameters) coming from the requester, the answerer, the requests and indicators, and possible already available data.
For example, Request 1 implies a legally binding statement considering REACH compliance. Therefore, a designated representative (e.g., the CEO) must sign the data. In many cases, companies have special authorization procedures for releasing of such data, e.g., that one or more responsible persons have to approve the request (cf. two parallel approval activities (Approve Data Request ) at Request 2, four-eyes-principle). In some cases, data may be already available in a company and does not have to be manually gathered (cf. Request 2, Check of available Data). However, every time the company-internal format of the answerer does not match the requester's one, a conversion must be applied. Further, some indicators and requests also directly relate to a given standard (e.g., ISO 14064 for greenhouse gases) where this can directly trigger an assessment of the answerer if he cannot exhibit the ful llment of the standard (cf. Request 2, External Assessment ).
Finally, another important aspect for often long-running data collection processes is that process parameters might change over time and, hence, exceptional situations could occur. Even in this very simple example, many variations and deviations might occur: for example, if the CEO was not available, activity Sign Data could be delayed. In turn, this might become a problem if there are de ned deadlines for the query answer.
Following rst insights provided in Section 2, this section presents seven concrete challenges for an information system supporting sustainability data collection processes along a supply chain (IS-DCP). The results are based on ndings from case studies conducted with industrial partners in the SustainHub project. Three gures serve for illustration purposes: Fig. 2 illustrates data collection challenges (DCC) 1 and 2, Fig. 3 illustrates DCC 3 and 4, and Fig. 4 illustrates DCC 5-7.
Requester Answerer Service Provider Human Data Storage Application
Findings Sustainability data collection in a supply chain involves various parties. A single request may depend on the timely delivery of data from di erent companies. For manual tasks, this mostly has to be done by a speci c person with sustainability knowledge or authority. In big companies, it can be even di cult to nd the right contact person to answer a speci c request. In relation, contact persons may change from time to time. Furthermore, as the requested data is often complex, has to be computed, or relates to legal requirements, external service providers may be involved in the data collection request as well. Finally, regarding the timely answering of a request, many requests are adjusted and forwarded to further suppliers (cf. Fig. 2) { thus answering times can multiply. Problems The contemporary approach to such requests heavily relies on individuals conducting manual tasks and interacting individually. There are tools (e.g., email) which can provide support for some of these and partly automate them. However, much work is still coordinated manually. As a request can be forwarded down the supply chain, it is quite di cult to predict, who exactly will be involved in its processing. Resulting from that, answering times of requests can be hardly estimated in a reliable manner as well.
Requirements An IS-DCP need to enable companies to centrally create and manage data collection requests. Thereby, it must be possible to simplify the dynamic selection process of involved parties and contact persons regarding the request answerers as well as potentially needed service providers. This is a basic requirement for enabling e cient request answering, data management, and monitoring.
Findings In a supply chain di erent parties follow di erent approaches to data management. Big companies mostly have implemented a higher level of automation while SMEs heavily rely on the work of individual persons. Furthermore, sustainability reporting is a relatively new area and a uni ed reporting method is not implemented along supply chains. This implies great variability when it comes to accessing companies' internal data. Some companies have advanced software solutions for their data management, some manage their data in generic databases, some store it in speci c les (e.g., Excel), and some have even not started to manage sustainability data yet.
Problems The contemporary approach to sustainability reporting is managed manually to a large extend. This involves manual requests from one party to another and di erent data collection tasks on the answerer side. This can impose large delays in data collection processes as sustainability data must be manually gathered from systems, databases or speci c les before it can be compiled, prepared and authorized in preparation to the delivery to the requester. Requirements An IS-DCP must accelerate and facilitate the access to requested sustainability data. On the one hand, this includes guiding users in manual data collection as well as automizing data-related activities (e.g., data approval, data transformation) as far as possible. On the other hand, automatic data collection should be enabled whenever possible. This involves accessing the systems containing the data automatically (e.g., via the provision of appropriate interfaces) and including such activities with manual approval activities when needed. Finally, data conversion between di erent formats ought to be supported as a basis for data aggregation.
Findings The management and con guration of sustainability data requests in a supply chain relies on a myriad of di erent data sets. As aforementioned, this data comes from various sources. Examples of such parameters include the preferences of the requester as well as the answerers (including approval processes and data formats) or the properties of the sustainability indicators (e.g., relations to standards) (cf. Fig. 3). As a result, potentially matching data might be already available in some cases but exposing di erent properties as requested. Problems As requests rely on heterogeneous data, they are di cult to manage. Requirements are partially presumed by the requester and often implicit. Hence, answerers might be unaware of all requirements and deliver data not matching them. Moreover, it is di cult to determine whether data, which has been collected before, ts the requirements of a new request. Finally, as a supply chain might involve a large number of requesters and answerers, this problem multiplies as crucial request data is scattered along the entire supply chain. Requirements To be able to consistently and e ectively manage data collection processes, an IS-DCP must centrally implement, manage and provide an understandable meta data schema addressing relevant request parameters. Thereby, instanced data based on the uniform meta data schema can be e ectively used to directly derive and adjust variants of data collection processes.
Challenge 3: Meta Data
ReQqQuueeurseytr1y11 Meta Data Requester Data Meta Data Answerer Data
ReQquueersytVVaarriiaanntt11
ReQquueersytVVaarriiaanntt22 Challenge 4: Request
Variants
Findings As mentioned, sustainability data exchange in a supply chain involves a considerable number of di erent manual and automated tasks aligned to the current data request. Hence, execution di ers greatly among di erent data requests, highly in uenced by parameters and data and distributed on many sources (cf. DCC 3 and Fig. 3). Moreover, the reuse of provided data is problematic as well as the reuse of knowledge about conducted data requests: persons in charge, managing a data collection, might not be aware of which approach matches the current parameter set.
Problems This makes the whole data collection procedure tedious and error prone. Based on the gained insights, to each data request a data collection process is manually de ned initially, and evolves stepwise afterwards. Relying on the various in uencing parameters, every request has to be treated individually { there is no applicable uniform approach to a data request, instead a high number of variants of data collection processes exist. So far, there is no system or approach in place that allows structuring or even governing such varying processes along a supply chain.
Requirements An IS-DCP needs not only to be capable of explicitly de ning the process of data collection. Due to the great variability in this domain, it must also be capable of managing numerous variants of each data request relating to a given parameter set. This includes the e ective and e cient modeling, management, storage and executing of data collection request processes.
Findings Sustainability data requests are demanding and their complex data collection processes evolve based on delivered data and forwarded requests to other parties (i.e., suppliers of the suppliers) (cf. Fig. 4). Furthermore, they are often tied to regulative requirements and laws as well as involve mandatory deadlines. Therefore, situations might occur, in which not all needed data is present, but the request answer must still be delivered due to a deadline. As another case, needed data might be available, but on di erent quality levels and/or in di erent formats.
Problems Contemporary sustainability data collection in supply chains is plagued by quality problems relating to the delivered data. Not only that requests are incompletely answered, the requester also has no awareness of the completeness and quality of the data stemming from multiple answerers. Moreover, answerers have no approach to data delivery in place when being unable to provide the requested data entirely, or their data does not match the request's quality requirements. Missing a uni ed approach, de nitive assertions or statements to the quality of the data of one request can often not be made and requests might even fail due to that fact.
Requirements An IS-DCP must be able to deal with incomplete data and quality problems. It must be possible that a request can be answered despite missing or low quality data. Furthermore, such a system must be able to make assumptions about the quality of the data that answers a request.
Challenge 6: Monitoring Requester Feedback
Feedback
Challenge 5: Quality Request 1
Feedback Sub-Request 1-1 Feedback
Feedback
Request 2
Feedback
Sub-Request 1-2
Deviation 3
Findings Sustainability data collection along the supply chain involves many parties and logically may take a long time. The requests exist in many variants and the quality and completeness of the provided data di er greatly (cf. DCC 5). The contemporary approach to such requests does not provide any information about the state of the request to requesters before the latter is answered (cf. Fig. 4). This includes missing statements about delivered data as well as the intermediate requests along the supply chain. If request processing is delayed at the side of one or more answerers, the initial requester cannot access such information without huge e ort.
Problems As a requester has no information about the state and potential data delivery problems of his requests, problems only become apparent when deadlines are approaching. However, at that time, it is mostly too late to apply countermeasures to low quality, incomplete data, or answerers that simple deliver no data at all.
Requirements An IS-DCP must be capable of monitoring complex requests spanning multiple answerers as well as various di erent manual and automatic activities. A requester must have the option to get actively or passively informed about the state of the activities along the data collection process as well as the state of the delivered data.
Findings Data collection requests can take a long time to answer as they dynamically involve a great number of di erent parties. Further, they expose manual and automatic activities, di erent kinds of data and data formats, and various unforeseen in uences on the data collection process. This implies that parameters, applied at the beginning of the request in uencing data collection, may change during the run time of a data collection process. Exceptional situation handling occurs as a result of expiring deadlines or answerers not delivering data. Problems The variability relating to sustainability data collection processes constitute a great challenge for companies. Running requests might become invalidated due to the aforementioned issues. However, there is no common sense or standard approach to this. Instead, requesters and answerers must manually nd solutions to still get requests answered in time. This includes much additional e ort and delays. Another issue are external assessments: they could not only be delayed but also completely fail, leaving the answerer without a required certi cation. The nal problem touched by this example concerns mostly longrunning data collection processes: data, that was available at the beginning of the query, could get invalid during the long-term process (e.g., if it has a de ned validity period).
Requirements An IS-DCP must cope with run-time variability occurring in today's sophisticated sustainability data collection processes. As soon as issues are detected, data collection processes must be timely adapted to the changing situation in order to keep the impact of these issues as considerable as possible. This requests a system which is able to dynamically adapt already running data collection processes without invalidating or breaking the existing process ow.
This section gives insights on the state of the art in scienti c approaches relating to the issues shown in this paper. It starts with a broader overview and proceeds with more closely related work including three subsections.
Section 3 underlines that exchanging data between di erent companies along a supply chain in an e cient and e ective way has always been a challenge. Nonetheless, this exchange is not only necessary|it is now a crucial success factor and a competitive advantage, these days. However, many in uencing factors hamper the realization of a data exchange being automated and homogeneous. In particular for those companies aiming to address holistic sustainability management, the inability to implement automated and consistent data exchange is a big obstacle. Please remind that these companies need to take into account existing and even emerging laws as well as regulations requesting to gather and distribute information about their produced goods. Furthermore, that requested information need be gathered from their their suppliers as well. Hence, complex data collection processes, involving a multitude of di erent companies and systems, have to be designed, conducted, and monitored to ensure compliance. So far, we could not locate any related work that completely addresses the aforementioned challenges (cf. Section 3).
For complex data collection processes, IS support in the supply chain is
desirable supporting communication and enabling automated data collection. The
importance and impact of an IS for supply chain communication has already
been highlighted in literature various times. In [
As automation can be a way to deal with various issues for sustainability
data collection, various approaches addressing that topic can be found in
literature. However, none of them applies to the domain and speci c requirements of
sustainable supply chain communication. For example, [
There also exist approaches addressing sustainability reporting (e.g., [
Besides approaches targeting generic sustainability, SCM and data collection issues, there are three closer areas that are mainly related to our problem statement and issues. As discussed, sustainability data collection processes involve numerous tasks to be orchestrated. Data requests may exist in many di erent variants based on a myriad of di erent data sources and may be subjected to dynamic changes during run-time (cf. DCC 7). This sub-section reviews approaches for process con guration (Section 4.1), data- and user-driven processes (Section 4.2), and dynamic processes (Section 4.3).
Behaviour-based con guration approaches enable the process modeler to specify
pre-de ned adaptations to the process behaviour. One option for realizing this
is hiding and blocking as described by [
Another way to enable process model con guration for di erent situations is
to incorporate con gurable elements into the process models as described in [
Process con guration approaches are a promising option to the problem presented in this paper. Nevertheless, that approaches do not completely match the requirements for exible data collection work ows in such a dynamic and heterogeneous environment, as many di erent data sources must be considered and request can be subjected to change even while they are running.
In contrast to classical process management approaches focusing on the
sequencing of activities, the case handling paradigm [
The approaches shown in this sub-section facilitate processes that are more user- or data-centric and aware. The creation of processes from certain objects could be interesting for SustainHub, however in the dynamic supply chain environment processes rather rely on context parameters than objects and are also continuously in uenced by their changes while executing.
In current literature, there are two main options for making the automatically supported execution of work ows dynamic: Normal, imperative work ows that are dynamic or adaptive or constraint based declarative work ows that are less rigid by design. This sub-section brie y reviews both kinds of approaches starting with adaptive imperative work ows.
Adaptive PAIS have been developed that incorporate the ability to change a
running process instance to conform to a changing situation. Examples of such
systems are ADEPT2 [
In case a human shall apply the adaptations, approaches like ProCycle [
As mentioned before, another way to enables exibility into work ows is
by specifying them in a declaring way. By such speci cation, a strict activity
sequencing is not rigidly prescribed. Instead of this, a number of di erent
constraints can be used to specify certain facts that the work ow execution must
conform to. This could be the mutual exclusion of two activities or a sequencing
relation between two distinct activities. Based on this, all activities speci ed can
be executed at any time as long as no constraint is violated. Examples for such
approaches are DECLARE [
This paper motivated the topic of sustainability data exchange along supply chains to subsequently present core challenges as well as state of the art in this area. We have clearly identi ed seven core challenges for today's data collection processes based on intensive interaction with our SustainHub partners most of them relating to variability issues. Especially, design time as well as run time exibility are clear requirements for any approach supporting companies aiming at sustainable development and production. The presented challenges can serve as starting point for applications developed to support today's complicated supply chain communication. The challenges are expressed in terms of sustainability data collection, however they describe generic problems that may occur in many domains. Thus the results can be easily transferred and be used for other domains. There exists a substantial amount of related work in di erent areas touching these topics. Yet, none of these approaches or tools succeeds in providing holistic support for the process of sustainability data exchange in a supply chain. The support of data collection requests and processes along today's complex supply chains is a challenge in the literal sense. Nonetheless, SustainHub is actively working on a process-based solution to deal with, and successfully manage the high variability occurring during design and run time. Future work will describe the exact approach, combination of technologies, and the architecture of the system to cope with the aforementioned challenges.
The project SustainHub (Project No.283130) is sponsored by the EU in the 7th Framework Programme of the European Commission (Topic ENV.2011.3.1.9-1, Eco-innovation).