CPG Leaders Need Faster Decision Making. The Solution is Already Here.

It feels nearly impossible to get people to agree on anything nowadays. With so many unique perspectives, objectives and desires, getting even half of a group to agree on a topic feels significant.

Which is why, when Gartner reported that 96% of all supply chain leaders in consumer packaged goods (CPG) are focused on finding a way to close the gap between making a decision and executing it, the importance of solving this shortcoming becomes even more pronounced.

Even then, there is a need to address a second issue raised in the survey. 53% of respondents shared that even after deploying a solution to accelerate real-time decision making, they are concerned about the limited benefits of that solution due to information silos within their organization.

Luckily for these CPG supply chain leaders, not only does a solution that eradicates this gap already exist, but it goes a step further to address the problem of siloed information. This technology is called Decision Intelligence, and while it has not yet reached a point of mainstream name recognition (like cloud computing or cryptocurrency), it’s already a well-established technology delivering real and measurable results for some of the largest CPG companies on earth.

It’s understandable that so many CPG leaders are skeptical of the ability of their legacy tools to meet these evolving needs. Various software vendors from planning solutions to BI tools are positioning themselves with marketing messages that they enable “better, faster decisions.” They are clearly picking up on the need from customers and prospects, and jumping on the emerging technology bandwagon. Unfortunately, this activity of aligning their marketing messages to market opportunity without the functionality to back up their claims is creating more confusion in the market.

Definitions are useful as a starting point, but customer use cases are more important to help separate the Decision Intelligence wheat from the chaff.

What Decision Intelligence Is and Isn’t

Gartner and IDC are two analyst firms that are leading the charge to define and educate the market on a technology that is not just a sea change, but an entirely new category. IDC wrote a defining document, A Case for Decision Intelligence: From "What Data Is Needed?" to "What Decisions Need to Be Made?" that states:

Gartner® defines Decision Intelligence as “a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback.”

The common thread within these two definitions is that Decision Intelligence is more than just identifying a disruption or a point in time where a decision needs to be made. While BI tools and APS can alert users to decisions that need to be made, they cannot make and execute those decisions (such as deciding to cancel a PO or move inventory to avoid a shortage). Nor can they write back to systems of record or data sources. They can flag potential issues, but the burden of solving them remains on the user.

The step change comes from evaluating between tradeoffs, weighing the pros and cons, executing the decision, and then writing back the decision and outcomes to all relevant source systems. This is what our AI-powered Decision Intelligence platform makes possible.

Aera’s Decision Intelligence Platform Delivers Measurable Results in CPG

These theoretical definitions are one approach to understanding Decision Intelligence. However, putting the actual use cases and problems solved will better illustrate what’s possible with Decision Intelligence. Here are three recent projects we’ve done with customers:

• A large CPG customer’s supply chain organization’s primary focus is fill rate; however, the commercial team is incentivized around OTIF. This commonly created cross-functional friction between teams with opposing priorities. On top of this was a repeating issue with supply shortages from N-1 and N-2 suppliers. This company deployed Aera which began ingesting data from internal systems and suppliers right away. The Aera Decision Cloud™ platform represents both metrics in the dashboard to improve transparency and collaboration – and, after harmonizing data, Aera delivers data-driven recommendations that optimize for the best holistic outcome for the company as a whole. Furthermore, users can now implement modeling that enables users to leverage “What If” analysis on those recommendations to understand the impact they have on those metrics, allowing for optimized decision making.
• Another CPG customer had both manufacturing and capacity constraints at a mixing center, which created issues with fulfillment rates. The customer was unable to allocate the right raw material inventory at their manufacturing facility, which ultimately impacted distribution to their DC. By using the Aera Dynamic Stock Deployment Skill, the customer can now preemptively evaluate raw material needs at the plant, as well as manufacturing capacity constraints and end user demand, to improve their end-to-end manufacturing process — maximizing the product flow across their network and improving margins.
• A beverages company needed to find a solution to optimize sourcing locations in their manufacturing process, with the ultimate goal of improving OTIF rates while also reducing waste from product expiry. The customer adopted the Dynamic Order Sourcing Skill, which utilizes a rule-based automation recommendation at an order level, to improve their KPIs. This company improved not only financial metrics, but sustainability metrics as well, through cost savings from a reduction in mileage traveled between locations (gasoline savings as well as CO2 reduction) as well as reducing product waste.

There are many more stories like these, and many more potential AI skills that can be developed to address other outstanding decisions within the CPG industry.