How Manufacturing Analytics Software Can Streamline The CPG Industry’s Data Ecosystem

Consumer Packaged Goods

A schematic of the proposed eras of industry; currently, manufacturers are in transition between the 3rd and 4th eras. Source: Wikipedia user Christoph Roser.

Manufacturers of consumer packaged goods (CPG) are looking for ways to harness the power of information technology to improve their profitability. Chasing promises of machine learning algorithms which will offer the golden path to bottleneck-free production, companies within the industry have scrambled to fit every piece of manufacturing capital with a sensor and a connection to a centralized reporting dashboard. Indeed, it’s been said that the future of manufacturing originates from analytics.1

The reality is that at present, most manufacturers are simply generating new sources of data that they aren’t equipped to fully understand or react to. Without modern analytics and a coherent implementation strategy, creating more data streams offers no forward progress.

Getting By With the Industry Standard

So, if a lack of analytics is the disconnect between data and action, what’s stopping manufacturers from transitioning?  While cost may be a concern, it’s clear that the promise of increased efficiency and profitability would quickly temper hesitation.2 The real answer may be that manufacturers can remain relatively comfortable and profitable in the CPG space without a high level of analytics integration into their process. It’s the devil manufacturers know: mechanized, with information technology support, but not truly an integrated cyber manufacturing system for lack of strong information infrastructure. In short, it’s comfortable enough that the data of the manufacturing floor only makes it into the boardroom after it passes through a few sets of hands and vice versa.

For example, a high-level overview of traditional CPG manufacturers’ current data landscape might look something like the following:

  • Manually calculated production goals
  • Manually allocated supply chain to fulfill production goals
  • QC derived product metrics during production
  • Sparse sensors, or too many sensors and not enough ability to make sense of the data
  • Delayed, non-automatic reporting of production quality metrics, supply issues, equipment failures and faults
  • Manual maintenance scheduling
  • Siloed data; data must pass manually or in a semi-automated fashion from department to department
  • Manual analysis and prescription of action on a per-point basis; action orders must pass manually or only semi-automatedly from department to department
  • Centralized information only; no lateral comparison possible
  • Causes friction with attempts at vertical integration

Using traditional analytics, there’s a “safe” separation between manufacturing floor tactics and the entire organization’s strategy. This provides a feeling of control because human attention is required for any process modifications to occur. Fixing a manufacturing bottleneck requires manual identification of that bottleneck, and the formulation of a plan to address it—no software systems making automatic changes which will have unintended consequences.

However, the future of CPG manufacturing isn’t what is currently the industry standard. Manufacturers who move beyond the transitional analytics model and trust its fruits will have a palpable advantage, which is driving more and more to do so.

Unlocking the Power of Modern Analytics

Where can modern analytics take manufacturers? The answer depends on the level of analytics integration that they aim to achieve. For integration to be effective, it needs to remove data from silos and then use the larger pool of data, suggesting actions at every point in the entire business and manufacturing process so that inefficiencies are reduced. To suggest actions, modern analytics build models which can often find relationships between seemingly unrelated variables. This means that modern analytics is strongest when it is highly inclusive at every point of user interaction—a sharp departure from the balkanized upward reporting that manufacturers are used to.

Modern analytics opens quite a few doors which will result in cost savings, including3:

  • Multitude of product metrics from each stage of production through final QC
  • Ubiquitous sensors within equipment and targeted at products to take as many measurements as possible for model building purposes
  • Automatic realtime reporting of KPI and equipment data
  • Integration of reports into a single console
  • Bottleneck analysis and potentially automated remediation of bottlenecks
  • Automatically calculated derived metrics, projections and actions
  • Predictive modeling of supply requirements, production efficiency and maintenance needs in light of all factors4
  • Root-cause analysis (RCA) for superior quality control
  • Potential for thin-margin lean manufacturing without hiccup5
  • Strategic retrospective insights generated automatically, allowing the factory floor to see its place in the larger puzzle and vice versa
  • Advanced statistical techniques for understanding hidden relationships between manufacturing variables like production volume, newly measured variables and quality
  • Smooth total vertical integration, if desired

On the factory floor, a packaging line management, product identification and traceability system would be just one unit feeding its data streams into this larger picture and being influenced by it in turn.6 Given that most manufacturers already have the bare bones of this system in place, getting the benefits of modern analytics is a matter of having the right platform which all the pieces of production can hook into.

For such a platform to be effective for use in the CPG industry, it would need to seamlessly accept sensor inputs, customizable KPI calculations and provide a coherent picture that unites the inputs and outputs of every manufacturing site simultaneously. With the right platform, connecting extant systems, finding hidden correlations, and gaining actionable insights will be natural and profitable. Thankfully, we’re able to deliver you this powerful platform with the help of a notable collaborator.

Gartner is our partner in next generation analytics for use in CPG industry manufacturing. With Gartner, packaging manufacturing can be tied into big data, machine learning, sensor integration, and ultimately, increased automation throughout the supply chain. Contact us today to see how you can make use of our collaboration with Gartner to bring the power of data analytics to your factory floor and catapult your company into the era of cyber driven industry.  

  1. “The Evolution and Future of Manufacturing: A Review.” April 2016, http://www.sciencedirect.com/science/article/pii/S0278612516300024
  2.  “Are Predictive Analytics Transforming Your Supply Chain?” December 2013, http://sloanreview.mit.edu/article/are-predictive-analytics-transforming-your-supply-chain/
  3. “A Cyber-Physical Systems Architecture For Industry 4.0 Based Manufacturing Systems.” January 2015, http://www.sciencedirect.com/science/article/pii/S221384631400025X?via%3Dihub
  4. “Utilizing Big Data and Predictive Analytics to Manage Supply Chain Risk.” December 2014, https://www.questia.com/library/journal/1P3-3601906311/utilizing-big-data-and-predictive-analytics-to-manage
  5. “The Internet of Things and the Future of Manufacturing.” June 2013, http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-and-the-future-of-manufacturing
  6. “Trends, Drivers, and Automation Strategies in CPG Manufacturing Operations.” September 2013, https://arcadvisorygroup-public.sharepoint.com/myarc/myreports/arcreports2013/Trends,%20Drivers,%20and%20Automation%20Strategies%20in%20CPG%20Manufacturing%20Operations.pdf