How to Eliminate Blind Spots in Supply Chain Visibility
Anyone with a basic understanding of black holes is aware of their paradoxical nature: You can’t see them, but you can see the effect they have on surrounding objects.
Supply chain blind spots are like that. Countless information gaps exist between suppliers, suppliers’ suppliers, third-party vendors and other stakeholders across the supply chain. You know these chasms exist because you can see the consequences (e.g., operational hiccups such as shipment delays, stalled production, contract violations, risk of regulatory infractions, damaged goods, etc.).
The global supply chain management challenge faced by manufacturers, retailers and 3PLs is figuring out exactly where those blind spots exist, bridging the gaps before an issue arises so data can be procured and refined, and then using that high-quality data to render a more complete picture of the end-to-end supply chain.
Solving the supply chain visibility problem really begins with the ability to diagnose why something has happened – which requires diagnostic analytics backed up by clean, accessible, and standardized data. It ends with the ability to predict where similar disruptions are liable to occur so that preventative action can be taken.
Where supply chain visibility blind spots originate
The most prominent inhibitor of visibility is the multitude of different IT systems used for record keeping.
These include, but aren’t limited to, ERPs, TMSs, inventory management software, ELDs, EDIs, and – in some cases – manual documentation. Multiply these by the number of trade partners, and it becomes clear that there’s little chance of data normalization.
The issue is further compounded by the fact that “nobody really owns or is accountable for supply chain visibility and data quality management,” according to Adrian Gonzalez of Talking Logistics.
Gonzalez provided a specific example of this from last year, when BMW experienced a production slow down caused by a shortage of steering gears from one its suppliers. That supplier, in turn, blamed its supplier, which had problems delivering the casings for the steering wheels.
The point is that the supply chain has so many degrees of separation between touchpoints, and they all need to come together to facilitate continuity of production and the flow of goods to the consumer.
Creating a universal ‘data language’
Data analytics in logistics is the best chance supply chain stakeholders have at achieving end-to-end supply chain visibility, but three things need to happen before it can be used to eliminate blind spots:
Data needs to mapped between all of a given supply chain’s touchpoints, from beginning to end. This means even suppliers’ suppliers need to be accounted for.
That data then needs to be centralized, where it can be deduplicated and validated for accuracy.
Data needs to be standardized, or canonicalized (others might say “enriched”) so that it can be given additional context. This ensures that a given data value is always decipherable, and that its meaning doesn’t get obscured by qualitative attributes (e.g., a tilted square being called a diamond).
Once this supply chain-wide data canon exists, businesses will be able to visualize the information about any given logistics event and take it as the actual truth. Consequently, sources of problems (and not just the problems themselves) can be understood and subsequently addressed.
Finally, with the implementation of machine learning algorithms that can create probabilistic models based on historical values, the door to the predictive supply chain is left ajar. And for a supply chain executive, that’s nearly equivalent to getting a glimpse inside a black hole for the first time.