ThroughPut has been spending considerable time servicing Fortune 500 clients in the Supply Chain data
analytics space. According to IDC, the world’s digital transformation market has exceeded $1 trillion
now, growing in double-digits annually. The availability of data has initiated advanced technology
companies and Supply Chain Intelligence Research firms to leverage existing machine learning and
artificial intelligence libraries to improve operational visibility. Oftentimes, these modules are borrowed
from the B2C world and retrofitted into B2B environments.
There is early promise of utilizing industrial data at scale. Process analytics providers are just at the
beginnings of using scalable data architecture for better predictive forecasting, bottleneck detection,
and routing optimization. Data is there with some basic use cases. However, most “useful” data remains
siloed. This is common knowledge for the last two decades, but data is growing at a pace where it
should be considered an asset.
Making sense of the available data:
To cut through the noise and dirt around supply chain data, this brief ThroughPut blog was to
benchmark our perspective from the field, where we have been talking to almost half the Fortune 500s
in the last two years (from the warehouse to the C-Suite). While process improvement groups are
required to use data to reduce inventory waste, improve production times, and impact working capital,
most existing data infrastructure limits their holistic impact.
In fact, ThroughPut has found that even the most advanced technology companies have difficulty
extracting their data for initial analysis. Fully-digitizing rigs, warehouses, and factories at scale to mine
global insights is unrealistic without having the basic foundations of data transfer at scale. There are
only a handful of companies with that sort of data architecture to apply artificial intelligence to true
operational data, primarily in isolated, fully-automated facilities.
From our empirical data, we have found that over 70% of our clients have an even bigger problem than
utilizing the data. In fact, in pretty much all 12 industrial verticals ThroughPut has deployed its
bottleneck detection modules on, there is even a more pressing question to address: what data is
relevant to the problem we are looking to solve?
Outside of a few outlier projects in the Far East, Germany, and Silicon Valley, the current needs of most
supply chain data involve sorting it for initial dynamic analysis. Fortune 500s are focused on initiatives to
create data lakes, but dynamic data analytics at scale is just in its early days.
For now, we recommend most clients to work with toolkits that support existing data architecture for
stop-gap analytics, so they can get closer to artificial intelligence in the future. To learn more about
different verticals and how to leverage your supply chain and production data better, please reach out