From intention to action: Decision Intelligence for Sustainability

Decision Intelligence helps you bridge the gap between intention and action and embed sustainability in supply chain planning and execution.

In brief

• Organizations are facing a high complexity in their supply chain and operations, and often miss out on the opportunity to improve operational, financial and sustainability outcomes
• By embedding sustainability in their supply chain operating model and combining it with AI-powered Decision Intelligence, they will be able to bridge the gap between intention and action, and improve their environmental and social impact efficiently and at scale

The necessity for companies to take sustainable actions has become unavoidable due to the growing demands from customers, investors and society at large, as well as the increasingly stringent laws and regulations (e.g. EU CSRD, Taxonomy, CS3D1).

These new requirements are pressuring companies to disclose their environmental and social impacts, and more importantly, to take tangible actions towards reducing the negative and maximizing the positive ones.

Customers are more sensitive to sustainability. According to the findings of the EY Future Consumer Index, “72% feel companies and organizations should be driving better social and environmental outcomes”2. This has direct consequences for companies in the consumer packaged goods (CPG) sector.

In this context, many organizations have made sustainability part of their core strategy, setting ambitious and time-bound targets. Some have joined forces to tackle systemic issues together and multiply their impact, through initiatives like the World Business Council on Sustainable Development, Zero100, Climate Action 100+, Alliance to End Plastic Waste, Alliance for Water Stewardship or Roundtable on Sustainable Palm Oil to name but a few.

Yet, beyond ambitions, strategies and pledges, the current trajectory in terms of measurable progress shows a major disconnect between intention and action; this is because some hard challenges are yet to be overcome. In addition, customers are increasingly wary of greenwashing and do not buy into one-off, superficial initiatives.

The challenges

Organizations are struggling to translate strategic commitments into actions at the operational level and embedding them into everyday decision making, in part because of the sheer volume and granularity of decisions that need to be considered. The operationalization of sustainability strategy requires updated governance, KPIs, processes, controls, systems, and of course human skills.

When sustainability gets established at the operational level, the inherent supply chain complexity – due to network and/or product structures – constitutes a major obstacle to making scalable impact.

Even without considering sustainability, planners must rely on heuristic methods, prioritize and focus on the most critical materials and resources. Integrating sustainability adds a whole new level of complexity to the planning equation, by broadening the set of constraints to be optimized under. Once the data model gets enriched with sustainability-related insights, it becomes ever more costly and time-consuming to analyze and optimize for sustainability on top of the existing parameters such as costs and service levels.

Therefore, advanced software is key to augment planners in decision making, helping to reveal optimization potential (in finance, operations and sustainability) that would otherwise not have been realized.

To illustrate the above considerations, let us consider a company experiencing a sudden demand plan disruption due to an unforeseen event such as a new regulation requiring an updated packaging labelling. This can make the whole inventory obsolete and, depending on the level of stock on-hand, result in large financial write-offs and environmental impacts due to the physical destruction.

What is Decision Intelligence?

Decision Intelligence is the digitization, augmentation, and automation of decision-making. It goes beyond simple reports and KPIs, giving actionable and impact-oriented recommendations that optimize business objectives against complex, interrelated constraints and real-time data. Leveraging AI and machine learning, a Decision Intelligence platform can act as a virtual member of your team – a trained digital analyst delivering well-researched and informed business recommendations that can be executed on your behalf. By digitising your decisions, you can make more and better decisions, greatly increasing the value your teams can go after.

How can Decision Intelligence impact sustainability?

Reduce emissions and costs by identifying and mitigating risks of obsolescence and wastage

Large CPG organisations are typically losing tens to hundreds of millions a year from obsolescence - from old packaging to expired raw materials and finished goods1. This is also having a significant sustainability impact – wasting the materials and emissions created during production, and depending on end-of-life scenarios, creating more emissions during destruction. Based on Demand and Supply data, Shelf-life and other master data, Aera can predict the risk of obsolescence and recommend the appropriate supply chain planning, manufacturing, logistics or procurement actions to mitigate it. Across several global CPG companies ($10bn+), Aera is digitising, augmenting, and automating the following recommendations in response to obsolescence risks:

• Recommending an update to the forecast
• Recommending a promotional deal to clear stock at risk
• Cancelling / updating purchase orders
• Recommending an inventory transfer in the network to rebalance inventory
• Recommending a change to the production schedule
• Recommending changes to planning parameters

Reduce CO2 and transport costs by digitising the trade-off between CO2, cost and service level when deciding route-type for every order

Over the COVID-19 pandemic, many organisations took to sending orders via air to meet service levels and avoid missed sales. Many are now struggling to wind this back. Sending an order via air rather than ocean has a significant CO2 impact. Based on transport data, emissions data, order data, inventory data, and planned vs actual lead times, for every order Aera can trade-off between cost, CO2 and service levels to identify opportunities to send orders via ocean rather than air.

Reducing plastic usage through alternate suppliers

Using data supplied by the company themself or external audit companies, Aera can analyse, predict and prevent virgin plastic usage, in three ways:

• Where multiple plastic specifications from different suppliers (e.g. virgin plastic vs recycled plastic) are available, Aera can trade-off cost, supplier performance and plastic indicators into the decision making for supplier selection
• Minimizing plastic waste by reviewing usage against sold product, completing a root cause analysis and generating recommendations to tackle waste
• Reviewing performance of plastic usage against targets to project future performance based on machine learning and generate recommendations to address gaps

Why Decision Intelligence is ideal for these use cases

Decision Intelligence, delivered by Aera Decision Cloud, is ideally suited to factoring sustainability goals into everyday decisions made by teams. With the ability to analyze data, apply machine learning and intelligence, and consider dynamic problems in real time, Aera can manage the balance between sustainability goals, service levels, and costs – and can do so at the most granular level possible, down to individual SKUs, raw materials, and locations.

With these capabilities, Aera is taking companies’ sustainability goals from insight to action. And, as Aera Decision Cloud makes more decisions over time, the platform continuously learns and improves, capturing each decision and its outcome to deliver the best possible recommendation.

Aera Decision Cloud can be deployed in a matter of weeks, without ripping and replacing your current technology stack or data sources

Client example

One example of these capabilities can be seen at Pulmuone, South Korea’s leading fresh food company and the world’s number-one tofu producer, who chose Aera Decision Cloud for a new approach to supply chain management. Aera is helping this company respond to disruption and meet their rigorous standards for sourcing, processing, packing, and distribution. Decision Intelligence is helping support the company’s growing global footprint and avoid business waste for its short shelf-life products through improved product SKU forecasting. Doing so has helped the company eliminate master data challenges and move from less flexible, function-oriented systems to execute faster demand, supply, and logistics decisions at scale.

The Aera & EY partnership

Our partnership combines the supply chain transformation knowledge of the EY organization with Aera’s innovative cognitive technology. The EY-Aera Alliance helps deliver broad digital transformation and leading-edge technology tailored to customers’ business needs, providing a road map to reimagine processes and derive value.

EY has 200+ certified Aera practitioners globally and a breadth of experience delivering Decision Intelligence transformations across CPG, life sciences, and oil & gas organizations.

Acknowledgements 

Special thanks to Michele Vismara and George Simons of the Aera Technology team who co-authored this article with Nicolas Bosshard, Marc Hetzer, Ivan Kostakev, and Sebastian Kuhn.