Since beginning of 2010 I have been researching on how companies can incorporate sustainability in their business operations, within the OEPI project and several SAP-internal projects with LoB sustainability. In order to improve their operational efficiency and overall sustainability, companies rely on exchanging Environmental Performance Indicators (EPIs), since internal measures are incomplete and often only display a fraction of a product’s overall emissions . In our projects we have been investigating how to design inter-organizational Information Systems to exchange EPIs, Sustainable Business Networks (SBNs). SBNs, like their big counterparts in the consumer world (Facebook, Twitter & Co.) can only be successful if they attract a high volume of contributions from their participants. Unlike in social networks, the focus of these contributions is not on entertainment but on quality. But how can this quality be monitored, disseminated, and finally improved? Could the Wisdom of the Crowd, enabled by a crowdsourcing process be a solution to this problem?



The Expert Crowd
Crowdsourcing, as it is applied in Web 2.0 applications, is defined as “the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call. ” This definition already reveals the difference between classical crowdsourcing and expert crowdsourcing in Business Networks:

  1. Expert crowdsourcing in SBNs often enables new capabilities that a company did not have before and therefore only in few cases replaces jobs traditionally performed by employees.
  2. Expert crowdsourcing in SBNs does not address an undefined, generally large group of people but a specified group of experts. This limits the potential amount of task performers and to some extent also their diversity, but on the other hand increases their average competence and accuracy.

Some of the well-known examples of crowdsourcing in fact illustrate the potential of limiting the participation to experts: When the USS Scorpion, a US Navy nuclear submarine, went missing in 1968, the search continued without success for about 5 months. Since underwater listening systems had recorded several explosions, a radius of 20 miles for the location of the wreck could be determined. Dr. John Craven, who led the search for the U.S. Navy, finally selected a team of diverse marine experts, and made all information available to them. He then allowed the experts to bet on the parameters speed, direction, and angle of the submarine after the first explosion. The ship could finally be located only 220 yards from the position calculated by aggregation of these parameters.



In fact, in these cases, instead of talking about the “Wisdom of the Crowd”, it would be more accurate to speak about the “Wisdom of the expert group”. This fact is amplified by the requirement for efficiency in business tasks. Therefore it is logically consistent to restrict the access to business tasks to a group of experts. In the consumer world user contributions are often not paid or the monetary compensation is marginal.



Quality Aspects in Sustainable Business Networks

Quality, in any established definition, has many different aspects. The GHG Protocol, the most applied guideline for measuring carbon emissions, explicitly mentions the aspects relevance, completeness, consistency, and accuracy. Most relevant in the case of Sustainable Business Networks is the dimension of accuracy. In corporate reporting, the determination of an EPI follows a 5-step approach . In a first step the relevant sources have to be identified. Second, a calculation approach has to be determined. Third, the activity data (the data of the corresponding processes etc.) has to be collected. Fourth, the EPI can be calculated, and in a last fifth step the data can be rolled-up to corporate level. Consequently, information about each step should be provided so a significant judgment about the quality can be made. Most quality problems are related to the definition and collection of activity data. Specifically, activity data can be primary (collected from the company’s operation or supply chain), secondary (such as industry-averages or environmental databases), extrapolated (adapted from similar process) or proxy (directly transferred from similar process) data. These aspects should also be transparent when providing it to the SBN.




Connecting the Pieces
Now, how do you solve a problem with an expert team, in particular in the case of data quality in Sustainable Business Networks? Again, the approach can be divided into 5 steps, of which the steps transparency and aggregation methodology are particular important in the case of SBNs:

  1. Formulate the problem and problem parameters: In our case, the main problem is to ensure the accuracy of the EPI data within the SBN – parameters of the accuracy are the data source, the calculation approach, the activity data collection process, the EPI calculation, and the roll-up to corporate level.
  2. Select the experts: The selection of the experts is key in Sustainable Business Networks and it can be done straightforward: transaction-based, meaning that the sustainability experts consuming the data rate it afterwards – this guarantees that the voting entity also has an actual reference to the particular content.
  3. Make the problem and its parameters transparent: In order to make all parameters of the problem transparent, all 5 steps of the EPI calculation process should be documented in the system and all activity data should have a “tag” describing the data type.
  4. Let experts apply their own methodology and calculation: The experts can now easily explore the data and its parameters, and there should be a “Web2.0-style” easy and fast possibility to rate the quality of the data.
  5. Aggregate results: Aggregation is a complex topic of its own. The aggregation inherits many elements of reputation systems. The overall score of the rating should lead to a “reliability” score for the data owner, while the degree of accordance with other votings should be used to calculate a “credibility” score. Suitable algorithms can be based on Bayesian statistics or the Page-rank algorithm . The reliability score can later on be used in supplier evaluations, in order to provide suppliers with a suitable incentivation to submit high quality data.


  6. In our research we show how to work out such a mechanism for data quality in SBNs in detail – and discuss other related aspects such as the matter of critical mass in SBNs.



    Hans Thies, SAP Research St. Gallen

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What value would it bring?As indicated in the first part of this post, the OEPI research project envisions a solution that connects participating organizations in a many-to-many network where they can share sustainability indicators, thereby reducing the efforts for provisioning the data and at the same time improving the data availability, quality, and reliability. We highlighted the current pain points that such a solution would address using four sustainability use cases: sustainable supplier management, green logistics, product compliance, and product lifecycle assessment/design. In the following paragraphs, we outline the key value delivered to companies via the many-to-many network, again focusing on the above-mentioned use cases.


With such a solution in place, companies can save time and money, in addition to make better sourcing decisions in “sustainable supplier management”. Bigger companies have several employees whose core function is to manage the process of collecting, analyzing, and improving supplier sustainability KPIs (upstream, client perspective) and/or respond to requests from the various customers and NGOs to provide and improve these KPIs (downstream, supplier perspective). A core functionality of the solution is a network-centric approach for sharing and provisioning sustainability KPIs among clients and suppliers in a many-to-many fashion. That way, data providers save time and effort because they enter the KPIs once instead of responding separately to each request. Similarly, data requesters can find the KPIs already published by some of their suppliers while others might need to simply update their data. Sharing KPIs instead of going through the lengthy request-collect-remind process would ultimately save much of the resources dedicated to the current manual process. With content rating features derived from Web 2.0 consumer applications, expert users would be able to judge the quality of the provided data. The resulting high-quality and better-available data leads to improved sourcing decisions after the comparison and analysis of data from alternative suppliers. These decisions can be based on supplier-level KPIs (for general supplier rating, not product-specific) or product-level KPIs whose values are supplier-specific and not only average (for sourcing a specific component).


In “green logistics”, shippers can save costs by finding load sharing opportunities across organizational boundaries, while carriers and logistics service providers (LSPs) can prepare the CO2 reports of their customers more easily and accurately. One of the biggest consumer products shippers we recently interviewed around their green logistics activities explained that they already do lots of optimizations based on their own transport planning, but there is a huge potential for companies to cooperate in order to optimize by collaborating in a network. If shippers and transportation companies share parts of their planning data in a many-to-many fashion, the solution could suggest consolidating loads belonging to various shippers, ultimately brining the vehicle utilization ratio up and the percentage of empty truck returns down. This translates directly into saved fuel costs for the various companies involved. A more urgent need though, both for shippers and carriers alike, is to improve the speed and quality of providing customer-specific CO2 reports. Similar to the overall supplier KPIs discussed above, the logistics emission reports suffer from the data availability, quality, and comparability problem. With a network solution holding the activity data – or even fuel data from the carriers – shippers and LSPs can get quick reports with comparable results. The concrete value for the participating companies is, similar to the supplier management scenario, reduced time and resources to prepare the customer reports (on the provider side) and better logistics decisions (on the client side).


Finally, let’s see what the business value is that a sustainability network solution brings into the product-related use cases of compliance and lifecycle assessment/design – considered together due to their similarities. Currently, suppliers are separately requested by many customers (as part of the mentioned use cases) to provide environmentally-relevant data about their products, e.g. amounts of hazardous materials used and production energy consumed. With a network solution where the material declarations and environmental KPIs are published once per suppliers and shared with selected customers, significant time and resources will be saved by both the data providers and requesters. The OEMs benefit from a bigger percentage of supplier response which is major shortcoming in the current approach. Higher response rates lead to more assured product compliance and better lifecycle assessments and design decisions, not unlike the “sustainable supplier management” use case. All this happens at a lower investment of resources to collect the data, directly translated into saved costs for the data requester. The suppliers also benefit from total saved time (publish once, share many) in addition to features that enable benchmarking with similar, anonymized companies.


In this article, we outlined, based on concrete use cases and current pain points, the business value of adopting a sustainability network solution for sharing and provisioning environmental data and indicators. This value falls in two categories: reduced time and effort (thereby resources and money) to collect and provide the data, in addition to better business decisions across these and other use cases due to the higher data availability and quality.


Ali Dada is a sustainability research lead at SAP

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