A manufacturer of home decorative items engaged my company sometime ago to conduct a scope 1 and scope 2 GHG emissions inventory, mainly to meet the requirements of a retailer’s sustainability scorecard. The manufacturer was eager to take this a step further and engage 20 of their key suppliers and put together a scope 3 emissions inventory based largely on primary data. In the end, only three of those suppliers signed up and the effort went nowhere. These small companies couldn’t see an immediate benefit to spending time and money on this, and our client wasn’t large enough to convince them otherwise.
Lack of supply-chain visibility is a common problem in other sectors. In the food and beverage industry, food ingredients are generally purchased on the open market and often through third-party distributors. The actual mix of producers can change from season to season or year to year – it is next to impossible to even know who produced an ingredient for a particular batch of your product, let alone work with that producer on sustainability.
If your company is not named Wal-Mart, Procter & Gamble, Unilever, Pepsi or something similar, the upstream supply chain might look rather opaque or the upstream suppliers might not be in a position to cooperate on sustainability initiatives. There are really two variables at play here. One is sheer size: Large companies will always get more cooperation out of their suppliers than small and medium enterprises. The second variable has to do with where a company is located on the supply chain. Companies that are closer to the consumer – such as retailers – can often identify their immediate upstream suppliers more easily. Companies that are both large and close to the consumer have a unique capacity to mobilize their suppliers in search of supply-chain-wide efficiencies.
This, of course, leaves a large part of the economy to fend for itself when it comes to environmental sustainability. I have seen many instances of smaller companies struggling to get basic process information from suppliers in order to complete a product life cycle assessment. Asking the same suppliers to participate in efficiency improvements – some of which may require upfront investment and possibly payback periods greater than a year – is simply not realistic in most cases.
The obvious solution for most SMEs is to look inward, and focus on design and process improvements within the boundaries of their own organizations. But isn’t sustainability all about (or mostly about) the supply chain? The short answer is, yes and no.
The supply chain provides the context in which a company can identify its most significant environmental impacts as well as opportunities. Most of the improvements that a company can make in this context will be within its own boundaries where it has full control.
There is in fact an excellent analogy for such a paradigm in the design of microprocessors and other complex chips in the semiconductor industry (where I spent two decades before starting CleanMetrics). The process there is heavily data-driven, and a typical chip is partitioned into many different subsystems – just like a supply chain. Engineers working on any one subsystem usually start with an approximate data model of the other subsystems while they design and optimize their own subsystem – similar to using secondary data to model other parts of the supply chain outside the organizational boundary. In chip design, more accurate data is normally used in later stages for verification and fine-tuning of the full system.
This highly successful methodology essentially breaks down a complex system into smaller and simpler pieces that can be worked on independently. A similar approach can provide an adequate basis for serious resource optimizations in the sustainability domain – leading to both cost savings and smaller environmental footprints. The key is to develop comprehensive and widely available industry-average life-cycle data for all common (and some uncommon) materials and processes, including data unique to specific sectors such as construction, food, packaging and apparel. Ideally, the data model would also account for important variations in production methods, geography, climate and other parameters.
Armed with a life-cycle inventory database that can provide secondary data for most supply chain functions, potential hot spots and areas of concern can be identified. Individual companies can focus on quantifying and optimizing their part of the supply chain – in context – without heavy dependence on primary data from many other companies. Thus, the iterative analysis and optimization loops can be smaller, faster and manageable – remaining within the boundaries of a company and selectively including other companies when optimization opportunities are identified at a broader level.
Companies can use this analytical framework to explore their resource use without reference to specific suppliers, and evaluate a full range of solutions to improve resource productivity – such as use of alternate materials and ingredients, changes in manufacturing processes, transition to renewable energy, internal recycling of materials and energy, waste reduction/diversion, and redesign of packaging. Most importantly, much of this can be analyzed and decisions can be made without unnecessary information flows up and down the supply chain.
The best way to support sustainable production is by developing information systems that are simple and elegant, rather than unduly complex. Part of this can be achieved by using modeling techniques to match accuracy levels to the needs of the problem at hand. An elegant system would also keep information flows and overheads to a minimum, so that companies in a broad swath of the economy can begin to make progress without excessive effort.
Kumar Venkat is president and chief technologist at CleanMetrics Corp., a provider of analytical solutions for the sustainable economy.