24 business users of five organisations tested the OEPI system over a period of 4 weeks. The testers represented 11 out of the 12 defined business roles and covered the whole bunch of level of expertises; from environmental experts with deep domain specific knowledge to non-experts.



Average, MIN and MAX rating of test group



Based on the impact assessment and requirements analysis 8 evaluation criteria have been defined a) Usability – easy to use, b) Understandability, c) Transparency, d) Time reduction, e) Flexibility, f) Level of confidence of data quality, g) Availability of data and h) Comparability rated by the business users represented in the spider graph.



There were two extreme positions in “availability of data” and “time reduction”. The reason for the low rating of “availability of data” resulted from a test case in the telecommunication sector. Currently there are no real data of telecommunication systems available only theoretical data based on simulation can be used. This sector specific issue has been not in the focus of the OEPI project.
The extreme rating on time reduction resulted from a person who already uses a specific tool and had the opinion that using yet another system for environmental indicators only would increase his work-load. However it is not the intention to substitute tools in operation but integrate and connect them or at least enable the exchange of the results of certain tools with the network.



A number of highlights have been reported as major value of the OEPI system:


• Benchmarking capability with network and external sources
• Access to primary data at lower costs
• A very flexible all in one system providing a very good and fast overview on product and organisational EPIs



by Katrin Mueller

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According to wikipedia, metaprogramming is the “writing of computer programs that write or manipulate other programs (or themselves) as their data, or that do part of the work at compile time that would otherwise be done at runtime. In some cases, this allows programmers to minimize the number of lines of code to express a solution (hence reducing development time), or it gives programs greater flexibility to efficiently handle new situations without recompilation”.


Thanks to these features of dynamic languages, it is straightforward to create a mini-language modeling a particular business domain. These ad-hoc languages, called Domain-Specific Languages (DSL), make it easy for developers and subject matter experts to share a common way to create and deliver the final application to the end users.


In OEPI, as our prototype is based on Java, we have been leveraging groovy metaprogramming features to develop some nice features allowing our business users to describe formulas and calculations almost in “natural language”:


Unit arithmetic

3.kg + 250.g (3 kilograms + 250 grams)
2.kg + 3.lb (2 kilograms + 3 pounds)
100.MJ + 200.kW*h (100 Mega Joules + 200 kiloWatts * hour)
50.km/h + 20.m/s (50 kilometers per hour + 20 meters per second)

Currency calculations

1500.EUR + 1200.USD + 1300.CHF

Therefore, the impacts of some materials, processes, etc. can be expressed in a language easily understood by domain experts. Moreover, all the stuff related to calculations with different units of measurement is automatically handled under the hoods.
For example, carbon dioxide emissions for a given mass of stainless steel (from ELCD database) can be expressed as follows:

CO2 = 3.38.kg * mass / 1.kg

A similar example is the impact of the consumption of a given amount energy in a country. Using the data for Spain (from ELCD database), we get the impact formulated as follows:

CO2 = 0.634.kg * energy / 3.6.MJ

A little more complex example is the impact of the transportation of goods by an articulated lorry (ELCD database):

CO2 = (0.0044.kg * mass * distance) / (1.kg * 100.km)

Thanks to the power of the DSL, business can later set values that are compatible with mass units (kg, g, lb, etc.), energy units (J, kJ, MJ, W*h, kW*h, etc.) or distance units (m, km, ft, yd, mi, etc.) to perform calculations based on these formulas. Therefore, the main advantage of such solutions is that users are writing actual code snippets almost without realizing it.


Thus, we have thoroughly used these capabilities in the prototype:


Letting users create “composed EPIs” (i.e.: Global-Warming Potential):

GWP100 = 1*CO2 + 25*CH4 + 298*N2O + 22800*SF6 + …

Calculating impacts through the supply chain (one of the key points of the project). Assuming that a product has components purchased from suppliers (for example, two containers and an electrical panel), the impact of the components of such product is managed also by a DSL:

components.CO2 = 2 * container_XA300.CO2 + electric_panel_FX2.CO2


This can be read as “the CO2 emissions of the components is 2 times the CO2 emissions of the container plus the CO2 emissions of the electri panel”. Generally speaking, the term “product”.”name_of_epi” can be used by a domain expert to compose different formulas (business rules) when describing and calculating environmental impacts.
Therefore, from my point of view, writing a Domain-Specific Language for calculating environmental impacts has really paid off in OEPI. First of all, it is a more expressive language, quite close to natural language. As a result, the development team has been working closely with domain experts sharing a common understanding, writting together an important part of the business rules.


Summing up, metaprogramming and DSLs not only have been a neat way to tackle some challenges we have been facing in the development of our prototype but they have provided an efficient and expressive solution, allowing us to build a solution closer to our users’ needs and requirements.


José Antonio López Abad, Solution Architect at ERICSSON

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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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The OEPI project participated in the 7th International Symposium on Environmentally Conscious Design and Inverse Manufacturing EcoDesign2011. This conference was hosted in Kioto Japan and is arranged at every second year. Topics of the conference were aligned to design for value innovation towards sustainable society e.g Global issues in EcoDesign, Social Perspectives in EcoDesign and Economics of EcoDesign. Our team member Ms. Hanna Uusitalo from KONE Corporation spoke as a plenary speaker about “KONE’s Corporate Environmental Activities and Solutions that Contribute to Creating a Positive Impact on the environment”. The other speaker from our team was Mr. Asko Koskimäki from VTT and his presentation was in technical session Sustainable Assessment and LCA and he spoke about “Ecolabelling and Design for Environment in Building Transportation System”.


Listening to the plenary speakers the key message was clear. Companies are focusing to follow through the reforms on improving energy efficiency and material efficiency and CO2 reduction generated by their operations. The ecodesign principles are extended to the design of manufacturing sites and include the complete supply chain.





Looking on the more detailed technical presentations, it was rewarding to see that lots of research work is already started globally in order to promote the enabling to continuously reduce the environmental impact of daily operations across industries and supply chains. Topics like: Sustainable Energy System, Sustainability Assessment and LCA, Green Business Design and Eco-labelling were introduced.


In the same week OEPI also participated in the International Symposium “Environmental Accounting and LCA for greening the Supply Chain in Asia” in Osaka. Our team member Ms. Hannele Tonteri spoke as invited speaker about “LCA as a tool for Design for Environment”. In this symposium, very interesting issue for OEPI rose; the development of Chinese Life Cycle database (CLCD). You can read more about this database from e.g. http://lcacenter.org/lcaxi/final/357.pdf. This database is in Chinese language, but the work is going on and hopefully in future it will be translated in English. It was discussed at the symposium that CLCD database in China and ELCD database in Europa need to work close in co-operation.


Tonteri Hannele

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The U.S. Energy Information Administration (EIA), Department of Energy, on July 2010 published the International Energy Outlook 2010 report. The report presents international energy projections through 2035, including outlooks for major energy fuels and associated carbon dioxide emissions (EIA, 2010). Based on the provided data, we prepared two tables.





Table 1 and figure 1 present the ratios of renewable electricity to conventional electricity used in the world. We can directly see the huge difference between Central & South America, with an average of 83%, and all other areas of the world.





Table 2 and figure 2, show tons of Carbon produced by a person per year. The data cover the time frame from 1980 untill 2007.





Due to the increasing public awareness of environmental issues, it becomes a hot topic in governments’ and companies’ politics as well. For example, it is uncommon today to see an election campaign without a proposed environmental policy, section or targets. After a quick look on the two tables and the provided figures, we can see that governments’ policies directly affect our environment. In addition, it may have an effect on the international trade relations. For instance, if Europe reduces the GHG emission and another country do not act upon environmental standards, the business relation between Europe and that country would be hampered.





As we said, emergent social awareness or public interest in environmental issues and governmental policies affect business policies in many ways. Green IT, Green Logistics, nsurance of environmental sustainability and energy efficiency are becoming new challenges or today’s companies. Environmental legislation is exerting additional pressure.


The same holds true for the mass media and society as a whole. Our focus in this paper will be on the SMEs’ sector which is one of the biggest sectors in businesses. For example, ´he European Commission for Enterprise and Industry stated that in 2009, 20 million SMEs operated in the European Union which represents roughly 99% of all businesses (EC-E&I, 2009).


Companies that envision the future and plan in advance will get a competitive advantage in the market. People started to realize the environmental issues, and have shown the interest to know more about the environmental performance of companies before purchasing products.


Similarly,  the companies also market  their products with environment  related slogans and details(Jamous, et al., 2011).  In  the  near  future,  certain  directives  would  be  passed  that  only  those  products  (from organizations) which are compliant with environmental standards will be allowed to be freely
traded, as nowadays with CE standard.


So  companies  that  fail  to  follow  environmental  standards may  risk  losing  the  potential markets  &  customers.  Environmental  directives  would  not  only  be  a  benefit  for  the environment  but  also  for  the  companies;  like  to  use  reusable materials,  reduce  their  costs, improve the processes and make the processes flexible to accept the changes from market.

Bibliography



EC-E&I.  2009.  European  Commission  for  Enterprise  and  Industry.  European  Union.  [Online]  2009. http://ec.europa.eu/enterprise/policies/sme/index_en.htm. EIA. 2010. International Energy Outlook 2010. U.S. Energy Information Administration.  [Online] 2010. Office
of  Integrated  Analysis  and  Forecasting  U.S.  Department  of  Energy,  Washington,  DC  20585.. http://www.eia.doe.gov/oiaf/ieo/pdf/0484(2010).pdf.


Jamous,  Naoum,  et  al.  2011.  Light-weight  composite  environmental  performance  indicators  (LWC-EPI)  concept. [book auth.] Paulina Golinska and Marek Fertsch. [ed.] Jorge Marx Gómez.  Information Technologies in  Environmental  Engineering,  New  Trends  and  Challenges.  Berlin :  Springer,  2011,  Vol.  3,  pp.  289-299. http://www.springerlink.com/content/j86665463452kt25/.

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[This is the first part of a blog post that outlines the case for introducing a many-to-many sustainability network solution. While we start here with outlining the problems that such a solution could solve, the next part will explain the business value that it brings to the participating companies]


For the last few years, I have been researching on a wide range of sustainability-related topics, most notably around (spanning both sustainable supplier management and green logistics) and Sustainable Products (including issues such as product compliance, product footprinting, and design for environment).


Throughout various discussions, end user interviews, and project workshops, one thing stands out as particularly common to all of these areas. All of these are inherently inter-organizational topics, which leads to and aggravates the known challenges of data availability, quality, and reliability.


In sustainable supplier management, companies typically incorporate sustainability KPIs into the supplier qualification and assessment processes. They collect these KPIs via questionnaires from their major suppliers, score the answers, and set the relative importance of each (sub)category of performance criteria which would be used as weights in the overall suppliers’ score. The result of the supplier sustainability assessment is used to generate a ‘list of preferred suppliers’ that are considered later in operational purchasing. Also, a global high-tech manufacturing company indicated that the aggregated scores determine whether the vendor ends up in one of four strategic cooperation groups, thereby receiving more influential status in future considerations. The whole process is naturally an inter-organizational engagement; the data collection process for sustainability performance indicators is tedious, error-prone, and not easily repeatable: Customer-specific content has to be provided in multiple formats and each supplier has to provide data separately for each request. The process represents a significant resource overhead for both data requestors and providers (many companies find themselves in both positions, depending on their role in the value chain).


In green logistics, shippers and carriers alike monitor and report the CO2 emissions resulting from the transportation and warehousing of products. This is driven by market-pressure to increase operational efficiency, lower fuel consumption, and offer customers a “greener”, differentiated service.  Third-party organizations are also playing a catalyst role, be them public-private initiatives such as the EPA-sponsored SmartWay program in the US or solely private such as the inter-organizational Green Freight Europe consortium. Since most shippers subcontract Logistic Service Providers (LSPs) and carriers to perform their transport operations, estimating the emissions per shipper becomes a complex, inter-organizational problem suffering from data issues. A major third-party logistics provider described to us that they are receiving every week an increasing number of requests from various clients, each requesting their tailored CO2 reports summarizing the emissions that their shipments caused in a certain timeframe. Listening to the shippers’ perspective from a global consumer products company reveals how tricky the problem can become. They subcontract many different LSPs, each using different CO2 calculation methodologies and emission factors, so asking each for the CO2 values would result in numbers that cannot be easily aggregated or compared, therefore they prefer doing the calculations themselves (even though they lack the needed activity data). Without real data, companies revert to global or industry averages to estimate environmental indicators, which leads to average results that do not differentiate alternatives or foster improvement.


The next example area is the environmental compliance of products, which is driven by regulations and affects many industries. Prominent examples of compliance requirements are two EU directives for electronic and electrical equipment, namely RoHS (Restriction of Hazardous Substances) and REACH (Registration, Evaluation, Authorization and Restriction of Chemicals). The former directive limits the use of six hazardous substances, e.g. lead and mercury, to 0.1% by weight of the electric or electronic component and the latter requires reporting any amount of chemical substance used in production or imported to Europe that exceeds 1 ton per substance. To ensure compliance with such regulations, OEMs request from their suppliers data on the materials and substances used in the components they procure. On one hand it’s the OEMs who need to comply with the regulations, and on the other hand it’s the suppliers who own the data and need to provide it for each requesting client. Insights from discussions with OEMs reveal a surprisingly low rate of supplier responses, probably attributed to the significant overhead that doesn’t have an obvious ROI for the suppliers. Again we see the data availability & quality problem recurring due to the inter-organizational nature of sustainability.


Finally, many companies are performing life cycle assessments to determine the environment footprints of some of their key products, and find new way to reduce this, often by modifying some product design decisions. Drivers for product footprinting and sustainable design are a mixture of internal motives (e.g. improving and protecting their brand) and external ones (e.g. customer requests and competitive positioning). The challenge here is also due to the inter-organizational nature of the problem: most of the environmental lifecycle impacts of products are often not generated by the brand-owners, but rather upstream or downstream in the value chain. For example, food brand owners such Unilever and Danone perform bottling and packaging operations that have a relatively low environmental footprint, whereas most emissions were caused by material production and transport (upstream suppliers). Also, high-tech brand owners such as Lexmark and Philips assemble final products, but most environmental impact is due to raw material extraction and the end product’s energy consumption. A study by Unilever shows that only 3% of the greenhouse gas emissions from 1500 representative products of their portfolio are due to their manufacturing, while 94% is due to raw materials and consumer use. This problem requires brand-sensitive companies to engage with suppliers, and the collection of high quality data is the first step towards reducing the environmental impact. According to an LCA expert in an electronics and electrical engineering company, only 5% of their studies actually rely on such primary data and the rest are quick scans using industry averages. With this company already considered a sustainability leader, the severity of the problem in other companies can be extrapolated.


To approach these problems, OEPI 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. The “many-to-many network” aspect is probably the single-most important underlying concept that can address the problems outlined above. Such networks are very limited in business environments today, despite being very successful in the consumer world (think “Facebook”). Probably the only really successful many-to-many networks in a business context are limited to personal networking applications such as LinkedIn and Xing. However, these are still used by people representing themselves and not their companies. The vision of business applications running on many-to-many networks where companies connect with each other, collaborate, share data, and execute processes is a bold one, but definitely one worth investigating. Being a relatively small research project, OEPI will only start developing this vision into a first prototype, covering focused use cases within environmental sustainability and not the whole exploitable landscape. In the next part of this post, we will investigate, based on the problems outlined above, how such a solution can bring value to the different companies, and thereby justify a business case for a solution provider and the participating users.

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We would like to invite you to a workshop meeting that we are going to
arrange on October, 13th 2011 in Oldenburg. This workshop will introduce
the Oldenburg Environmental Technology Network (www.uno-oldenburg.de) as
a framework for companies to concentrate their forces and knowledge on
common projects that further a sustainable development.

At the same time the recently started project “IT-for-Green: Next
Generation CEMIS for Environmental, Energy and Resource Management”
(www.it-for-green.eu) is presented as a cooperation of 4 universities
and 6 industrial and municipal partners. Within the context of the
ertemis network (www.ertemis.eu), IT-for-Green aims at making companies
and their processes environmentally sensible and – on the long run – at
making cost reduction and environmental protection going hand in hand.
As a complementary topic to OEPI, this project offers the chance for
intensive knowledge exchange.

The workshop is mainly held in German language, but an international
corner with guest from South Africa is also offered.

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You will find here an introduction to the ontology for environmental performance indicators. Due to its origin in the OEPI project , the ontology is referred to as the “OEPI Ontology” though it is intended to be useful beyond the scope and duration of this project .

In order to keep this article tight we provide only basic information about the ontology that is helpful to browse, understand, and possibly use or extend it on your own. For a first understanding of OWL ontologies you may read the good tutorial of Matthew Horridge [7]. Details about installation and use of the required tools or interfaces can be retrieved from the references given at the end. Further insight into the domain of environmental performance indicators can be gained through other publications on the OEPI project website [1].

What is the OEPI Ontology about?


Today, there is a great variety of environmental performance indicators (EPI). Their main purposes, for example according to Jasch in [4], are:

  • to quantify the current environmental performance of some entity,
  • to track the change of environmental performance of an entity between different points in time,
  • to compare or to benchmark the environmental performance of different entities,
  • to utilize the environmental performance of entities in decisions, or
  • to assess the achievement of quantitative goals related to environmental performance  in transformation or improvement processes.

The OEPI Ontology defines the concepts that are needed to describe EPI in a formalized, computer-accessible way. The aim is to establish a common understanding of their meaning and interpretation across different organizations and applications.

The ontology establishes the “reference body of domain knowledge” for the implementation of the OEPI platform and the OEPI user portal. The OEPI platform can be regarded as a common resource of selected and preprocessed data for defined EPI from various data sources. The OEPI user portal is the single point of access for the business-oriented user who is neither an expert of the domain of EPI nor an expert of web technology. The portal relies on the OEPI platform to feed its collection of use-case-oriented services with a wealth of suitable EPI data whenever users request it.

The OEPI Ontology uses Web Ontology Language OWL [5] and has been built with the ontology editor Protégé 4 [6]. Though the ontology will evolve further during the remaining course of the OEPI project, a first version is available for the interested public now through the OEPI website.


Some hints to get you going on your own exploration


When you open the OEPI Ontology in Protégé, the ontology annotations are displayed by default. They give you some information related to the current version of the ontology. In the respective functional tabs, you will find views of the asserted hierarchies of classes, properties, and individuals. Further insight can be obtained by running a reasoner (We recommend to use the Hermit reasoner [8] which can be selected from menu “Reasoner“ in Protégé 4.1.) to produce and display the inferred hierarchies.

Furthermore, you will see that all entities of the OEPI Ontology have comments (as annotation properties) that give up-to-date textual descriptions of the meaning and purpose of the entity. They are not replicated here for obvious reasons and will help you to explore the ontology in depth on your own.

For example, figure 1 shows a screen capture of the OEPI Ontology loaded in Protégé 4.1. We have opened the functional tab “Entities”. After running the Hermit reasoner, we have selected class EPI_Statement in the class hierarchy. Therefore, its annotations and its description can be browsed in the respective windows on the right side of the screen. On the left side below the class hierarchy, we could explore the object property hierarchy.

OEPI Ontology in Protégé 4.1, sample view


Some more hints to support your exploration


The OEPI Ontology contains a class hierarchy with primitive as well as defined classes, a considerable number of object properties, data properties, and individuals.

Primitive v. defined classes: The aim might be to have only defined classes in the end but in the current state primitive classes clearly indicate that their definition is considered to be incomplete. There might as well be defined classes in the current version that are still incomplete (with regard to the concept they represent) but have been converted to “defined” for reasoning purposes. They will probably need further revisions in the future.

Use of property characteristics: There has been no intense use of the characteristics of properties so far, except that for object properties inverse properties have usually been created when appropriate. Furthermore, the characteristic “functional” has been used for some object properties rather than introducing a cardinality of 1 in an object restriction.

Naming scheme: Names of classes and individuals (e. g. EPI_Statement) start with a capital letter, property names (e. g. is_Compliant_With) begin with a small letter. Names may be composed of multiple segments separated by the underline character. The first letter of the second and all further segments is a capital for all kinds of entities. Special characters other than the underline character and “white space” have not been used in names.

Roles of individuals: Some of the individuals are considered as part of the ontology (e. g. EPI_Descriptor_Aspect_Emissions) and some have been created only as example data for the purpose of demonstration. The individuals of this second kind have a name prefix “DEMO_” to make them easily recognizable (e. g. DEMO_Product_KONE_Monospace).

Two flavours of class definitions: When looking closely at class definitions, you might encounter two different approaches to class definitions. The first one, which might be called “descriptive”, aims to express the necessary defining conditions for members of the class as complete as possible within the scope of OEPI. The second one, which might be called “inferring”, exploits the reasoning mechanism to derive membership in classes by the existence of a few distinctive object properties (or even only one). There are representatives of both kinds in the OEPI Ontology and furthermore some class definitions which combine both approaches. There might be no general reason for a class to be defined in either of these ways beyond reflecting the current “state of cognition” in OEPI ontology development.

Some classes are defined in a very simple way because they are candidates to be re-used from other existing ontologies in the future, for example class Bibliographical_Information which is currently equivalent to

Thing and has_Simple_Bibliographical_Information some string

Therefore, bibliographical information is currently just represented by a functional data property linking to a character string, which is expected to hold the full bibliographical information.


In the Focus – the “EPI Statement”


In a simplified view, environmental performance indicators are different ways of expressing certain aspects of environmental performance of entities. In the OEPI Ontology, an “EPI Definition” describes all relevant characteristics of a specific EPI. If we use one defined EPI and assess it for a real entity, the result constitutes what is called an “EPI Statement” in the ontology. (See the “OntoGraf” visualization (created by the OntoGraf plugin in Protégé 4.1 which is very useful to visualize the ontology) of this class and its neighborhood). An EPI statement states quantitative information about a specific entity, which is called the “observed” entity, with all additional details that are required to make the statement compliant with its associated EPI definition.

Class EPI_Statement and neighborhood


Different kinds of EPI statements and their typical structure and content are represented in the ontology by different subclasses of EPI statements, for example EPI_Statement_ODP for statements about ozone depletion potential (ODP). An individual member of such a class would be one concrete data record matching this kind of EPI statement.


Conclusion


Hopefully, you are now keen to dig deeper into the OEPI Ontology on your own. This short introduction has provided you with hints where to start and how to do this. Now you may download the ontology – and an ontology editor if you don’t use one already – and jump into the water. Your feedback will be highly welcome at this website.


Download


To download the OEPI Ontology you need to provide your Email



References


[1]    OEPI Consortium, Project website http://www.oepi-project.eu/

[2]    OEPI Consortium, Deliverable D1.3: Reference Ontology for EPIs – Requirements and Design, January 2011

[3]    OEPI Blog, Why OEPI needs to invest in ontology development, http://oepi-project.eu/blog/2011/05/18/why-oepi-needs-to-invest-in-ontology-development/, May 18, 2011

[4]    Christine Jasch, Environmental performance evaluation and indicators, Journal of Cleaner Production, Volume 8, Issue 1, February 2000, Pages 79-88

[5]    OWL Working Group, OWL 2 Web Ontology Language Document Overview, W3C Recommendation, 27 October 2009, http://www.w3.org/TR/2009/REC-owl2-overview-20091027/ (accessed: May 30, 2011)

[6]    The Protégé Ontology Editor and Knowledge Acquisition System, website http://protege.stanford.edu/ (accessed: May 30, 2011)

[7]    Matthew Horridge, A Practical Guide To Building OWL Ontologies Using Protégé 4 and CO-ODE Tools, Edition 1.3, The University of Manchester, 2011, most recent version available for download at http://owl.cs.manchester.ac.uk/tutorials/protegeowltutorial/ (accessed: June 14, 2011)

[8]    Hermit OWL Reasoner, website http://www.hermit-reasoner.com/ (accessed: May 30, 2011)

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Why the development of environmental indicators needs ontologies


Investment in ontology design for the OEPI project is not to be taken literally as “just ontology”. It is one important and necessary part of the Semantic Web approach and thus this investment is actually an important step in setting the direction for the whole implementation approach in OEPI. Therefore, the actual question is why OEPI needs to go for application of Semantic Web technologies with Ontology being a part thereof.

It is about this alternative:



Is OEPI technology old-fashioned and obsolete before its implementation even has begun and fails to meet its goals? – Or is OEPI technology future-oriented and able to leverage the capabilities of “the Web” even beyond its current exploitation and succeeds in fulfilling its mission?

The mission of OEPI is to deliver more than what is possible today regarding Environmental Performance Indicators and their widespread use.
Semantic Web technologies – including ontology as one important piece – are necessary to ensure the fulfilment of this mission, in more detail:

  • OEPI wants to use and to combine as much existing (web) resources as possible and bring them close to the user. The ability to exchange the semantics of such resources is of paramount importance for the practical implementation.
  • OEPI wants to automate these processes in an easy and user-friendly way.
  • OEPI wants to enable flexible and ad-hoc use of resources.



A very big ontology
Conventional technologies alone, even including modern concepts as, for example, Model Driven Architecture or Service Oriented Architecture, are helpful but not sufficient to meet these requirements because they lack explicit semantics of the modelled domain as also of existing data and services. Currently, conventional Systems and Software Engineering is able to solve configuration tasks in advance and according to predefined decision patterns but not
by inferring decisions from semantically rich descriptions, constraints, and restrictions at runtime. Semantic Web technologies add capabilities for provision of such features to the conventional technologies. One first approach to bridge the gap from ontology to conventional MDA, which is quite common already, could be the definition of a domain-specific UML profile
representing ontology.


OEPI needs Semantic Web technologies (in combination with conventional technologies) to describe, to categorize, to find and to utilize existing resources and to formalize spontaneous requirements of users for supporting their daily work flexibly.


In this approach, the ontology has the purpose to unambiguously and explicitly describe all the “OEPI things” with their properties, relationships, and semantics in a formalized way that can be evaluated by humans and by software. It constitutes the common reference and resource for all data models, service descriptions etc. that have to be developed for the implementation of the platform as well as for different independent services or solutions. It is
needed to enable and ensure semantic interoperability among existing, new, and future services and solutions.


OEPI will not be able to fulfil all these requirements completely during the limited course of the project but it is responsibility of OEPI to provide a sound foundation that can be built on and further extended and enhanced by industry as well as research beyond the end of the
project. OEPI Ontology will be one part of this foundation.

Reference:

“Ontology Driven Architectures and Potential Uses of the Semantic Web in Systems and Software Engineering”, Working Group Note of W3C Semantic Web Best Practices & Deployment Working Group, http://www.w3.org/2001/sw/BestPractices/SE/ODA/