For the last few years, I have been researching on a wide range of sustainability-related topics, most notably around Sustainable Supply Chain (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, the OEPI collaborative 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. 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 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.