Did you know that if the CPG sector was to be thought of as a country, “its carbon-dioxide emissions would be second only to those of China”?  Understandably there is growing consumer awareness about sustainability, and it is driving their purchase behavior. Consider this,
In 2018, a premier legacy CPG company that reinvented legacy products even proved that its sustainable living brands were contributing to 70% of its turnover growth, and the sustainable brands within the business witnessed a growth rate that was 46% faster than its counterparts.  Therefore, the need to go sustainable is obvious, but which sustainability initiatives should companies focus on, which ones are operationally viable and are going to generate optimum ROI? We address these questions through the course of this article and highlight how AI & ML can prove to be a valuable ally in making sustainability viable for CPG firms.
Blockchain technology helps keep an accurate ledger of inventory movement, identify any dampeners to product quality and ensure that product wastage remains minimal, because it tracks the movement of a product at multiple points along the supply chain.
Consider for instance, a packaged beauty product, where one SKU is flagged off for possible allergens. Usually, all the products will be pulled off the shelves until the source of the issue is identified. However, with blockchain technology, the product that is under scrutiny can be traced back to the plant it was manufactured in and identify the factor that caused the allergen. The products from this plant can be recalled until the issue is resolved, while the products from the other plants continue to remain on sale. This way product wastage is minimal, the efforts are sustainable, and operational costs are reduced for the company.
This can also prove beneficial for packaged products that need to be manufactured at a particular temperature. With blockchain technology, the products can be tracked, and its status can be updated and shared real-time, eliminating the possibility of oversight in case there is fluctuation in the optimum storage requirements. Automated alerts can help address issues earlier, ensuring reduced wastage. The data collected can also help understand product movement along the supply chain and identify areas that can be streamlined further.
Further, this data can provide insights about resource usage in the plant from electricity to fuel usage, machinery performance/depreciation, ensuring that emissions are kept under check, are in adherence with environmental laws, and ensure that late responses do not translate to hefty losses for the companies.
Apart from reducing supply chain bottlenecks, the continuous flow of data collected with the help of this technology can also alert stakeholders on any possible counterfeited/unauthorized products, identify any illegitimate behavior on the part of suppliers, etc., ensuring that product quality is always at optimum levels, and that the supply chain movement is aligned with ethical rules and regulations and devoid of harmful carbon emissions and disposes of waste responsibly, with no negative impact to the environment. Therefore, not only can blockchain help regulate current supply chain movement and waste regulation, but it can also collate valuable data upon which future ML models can be created to determine ways in which existing business practices can be improved, made more sustainable and cost-efficient.
CPG companies generate waste that needs to be disposed of responsibly to ensure that it does not negatively impact the environment. For food products, for instance, not apart from waste disposal, CPG companies are getting creative about using all parts of an ingredient, generating new products from scraps or leftovers. This improves cost efficiency, resource management, makes them environmentally responsible. In fact, "CPG businesses that are considered leaders in adopting environmental, social, and governance (ESG) practices have an 11% valuation premium over their competitors."  And two technologies that will prove vital to track resource usage and waste management for initiatives like these are AI & IoT.
For example, once the waste is gathered, RFIDs can be used to separate and analyze waste generated, under particular tags. This tag can help track whether the waste is being disposed of appropriately, and prove to be a gold mine of data in analyzing waste generation. Furthermore, intelligent waste baskets with sensors can analyze the amount of garbage being generated and convey when the basket is full, to ensure automate disposal. Not only can AI and IoT help sort waste but it can also bolster recycling/ reusing efforts. These systems can identify and segregate the scraps that are meant to be disposed from the scraps/packaging that can be recycled/reused. This helps reduce iterative, redundant human effort spent on these tasks and directs them to more esoteric efforts.
The key to successful sustainability analytics is constant data analyses and research. There always was and will be more and more research in how business practices can become more sustainable, more so, in recent times as there is growing awareness about depleting resources and the need to be more environmentally considerate. In such a scenario, CPG companies that aim to be ethically sound and pioneer sustainability efforts need to be informed about the latest innovations. For the same, companies must look at automating their research efforts and employ their human resources to identify initiatives that best align with their business practices. A leading confectionary and food manufacturer leveraged an automated NLP engine for research efforts, and reduced manual research efforts significantly, saving an estimated $1.3M USD, annually. Human resources can also help improve the accuracy of the results generated by the AI-powered ML model, improving research result relevance and accuracy and remaining constantly updated on latest developments in the industry.
Apart from research data, operational data pertaining to assets, functions, resource utilization etc., is collected org-wide, and upon analyses helps gauge sustainability. But without appropriate analytics practices, the data will go to waste. By leveraging automated data gathering and setting up robust data pipelines and warehouses, data governance capabilities can be improved and better data reports can be generated. This will help keep track of the company’s sustainability initiatives, tracking fuel/resource management, machinery performance, supply chain movement and much more. By ensuring optimum resource management, companies can also guard themselves against severe supply chain disruption and better respond to risk events where price, demand and supply requirements are volatile and prone to extreme fluctuation. This can also help to preemptively predictive future risk scenarios and be better prepared to deal with the same.
All of these analytics initiatives can help companies showcase consumers and competitors that they are prioritizing sustainability. Furthermore, they will also have the needed data inputs to prove to investors and stakeholders that these initiatives are bolstering and bettering business practices, improving their overall operational ROI.
With AI & ML, companies can identify the business practices that best enhance sustainability efforts for them, cater to customer requirements, use resources effectively, and ensure that they remain unperturbed by severe market fluctuations.
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