Top 4 Challenges Facing the CPG Industry and How AI of Things Can Help

The CPG industry's path to overcoming manufacturing challenges lies in leveraging the power of AI with Internet of Things.

Kaikoro Adobe Stock 245853295
Kaikoro AdobeStock_245853295

According to a recent study published by Towards Packaging, the global consumer packaged goods (CPG) market size is expected to hit around $3,171.11 billion by 2032. Like most every industry, CPG manufacturers face challenges in unpredictable shifts in supply and demand all the time. The solution to most of those challenges lies in intelligence.

At this scale, organizations need to embrace artificial intelligence (AI) to manage the complexity of data and systems, but AI success depends on the amount and quality of available data. The promise of digital transformation projects and the value of Industry 4.0/Smart Manufacturing solutions also relies heavily on the amount and quality of available data. And as businesses grapple with thin margins, globalization, and high consumer demand the stakes have never been higher.

The Top 4 manufacturing challenges in CPG

The CPG industry faces immense complexities and unpredictability in its manufacturing operations. From inflation, rumors of a recession, geopolitical factors, continued supply chain disruption, the threat of potential recalls, and shifting customer expectations, manufacturing leaders have been forced to either tighten budgets or invest in new innovations.

Amid this shifting landscape, four key challenges stand out as particularly pressing for CPG manufacturers today:

1.    Unpredictable shifts in supply and demand impact production planning

CPG production planning is complex. Mapping changes in consumer demand to the availability of raw material supplies becomes an almost impossible exercise when confronted with the unpredictability of environmental, sociopolitical, and global health care issues. While enterprise resource planning (ERP) systems have historically helped, the growing complexity of these unpredictable impacts has risen beyond the limitations of ERPs. The challenge becomes exponential with the new requirements of raw material provenance traceability and accountability to global political issues which in turn impacts supply availability and potentially demand as well. This is where the combination of AI with Internet of Things (AIoT) can provide better answers.

2.     Low end-to-end visibility due to lack of asset tracking and providence

CPG manufacturers need visibility into inventory, provenance, and material flows. It is critical to ensure product consistency, quality, and timely demand requirements. Any variation may compromise quality, profitability, and/or safety to finished goods. Knowing where raw materials through to finished goods are throughout the production process can mean the difference between operation efficiency and production yield or escalating waste and potential recalls. New Industrial Internet of Things (IIoT) technology can be deployed to provide granular and contextualized data on the location and history of critical assets in real-time.

3.     Meeting quality assurance standards

CPG manufacturers should not allow the minutia of manufacturing to result in lower quality. Individual supplies are expected to be delivered to rigorous providence standards and without delay. To maintain finished goods quality, raw materials and environmental conditions must be tracked and accounted for at a granular level as much as equipment needs to be maintained and kept operational without interruption. You also can’t talk about quality without considering the management of waste and waste by-products. AI allows for detection of anomalies that can be caused by imminent equipment failure or changes in environmental conditions resulting in potential quality dips, waste spikes and at worst case a time consuming and costly recall.

4.     Ingesting large amounts of data while addressing data gaps

It’s no secret that the ability to ingest and analyze more timely data equates to better operational and production decisions. In a typical CPG plant, this data is so profound, proper analysis is far beyond manual scan. AI becomes the only way to realistically ingest and analyze it all without error (or going cross-eyed). Combined with IIoT asset tracking, digital-twin modeling makes possible great intelligence from far less real-world trial and error by filling in the gaps in digital data currently addressed with manual processes. A more complete overview can then be achieved with greater insight into yield, operating expense, and safety thanks to data history, semantic modeling, and data analysis.

These four CPG challenges are just the tip of the iceberg. The complexities are numerous, and the solution is greater than any single concept. By combining the advanced technologies of AI with your IoT infrastructure, manufacturers can create more efficient IoT operations, improve human-machine interactions and enhance data management and analytics.

AIoT generates data that doesn’t exist

The key to eliminating these four challenges is directly proportional to the amount of granular and contextualized data available to your AI initiatives.  Implementing an AIoT solution will provide that critical data. Start by understanding your IIoT asset tracking and digital twin requirements to collect data on:

·       Environmental conditions. By tracking temperature, humidity, and other environmental .factors throughout the production line, AIoT solutions can ensure optimal conditions for material handling and processing, preventing quality issues.

·       Asset location, history, and provenance. Granular, real-time tracking of raw materials, work-in-progress goods, and finished products enables complete visibility into the supply chain, helping maintain quality, comply with regulations, and facilitate recalls if needed.

·       Equipment health. Sensors monitoring equipment performance can detect anomalies and predict maintenance needs, reducing downtime and extending the equipment’s lifespan.

·       Process health. Combining data from multiple sources, AIoT provides a comprehensive view of process parameters, enabling optimization, identifying bottlenecks, and ensuring consistent quality output.

The CPG industry's path to overcoming manufacturing challenges lies in leveraging the power of AIoT. By generating and contextualizing data, CPG manufacturers can gain unprecedented visibility, control, and optimization of their operations. As the global market continues to grow, embracing AIoT will be crucial to CPG manufacturers looking to maintain a competitive edge through better quality data, greater efficiency, and end-to-end visibility.