The Hidden Efficiency Killer That Has No Place in 2021

Too many companies were left unable to respond when the pandemic hit because of information delays and time lags.

Manager Analyzing Erp On Ar Screen, Connections, Bi, Hr, Crm 820886246 4500x3000

The hard lesson of 2020 was if there is supply risk anywhere in the supply network, or an actual disruption, organizations better know about it right away. And if there’s a demand surge, your suppliers and your suppliers’ suppliers need to know it too.

Too many companies were left unable to respond when the pandemic hit because of information delays and time lags – resulting in stale data and poor performance. In 2021, this new awareness of the cost of data latency will drive companies to improve collaboration by installing advanced technology approaches that provide real-time information and reduce time lags to near zero.

Data and Enterprise-Centric Systems

Good data is key to running any business efficiently, especially its supply chain. And there’s no shortage of it in the modern enterprise. Large companies today have tens, even hundreds, of enterprise systems to run their business and their supply chains. Some systems are commercial ERP systems, some are homegrown, and many of them are accumulated through acquisitions.

These systems typically comprise Point-of-Sale (POS) systems, Transportation Management Systems (TMS), Warehouse Management Systems (WMS), Yard Management Systems (YMS), Material Requirements Planning (MRP), and Enterprise Resource Planning (ERP). Today, new structured and unstructured data streams can include real-time IoT data, video feeds, social media, traffic, weather and news.

With all this data, you’d think that being data-driven would be easy. So why isn’t it?

While there is no shortage of information, much of it is scattered across the enterprise and the supply chain and is isolated in silos. It’s difficult to access and act upon in a timely and effective way. Because systems are often synched overnight or in weekly batch updates, there are often challenges with accuracy and timeliness. Critical data can be missing or in conflict with other data, or the data itself can be days old and no longer reflect the reality you are trying to manage now. 

The problem compounds as data is “tossed over the wall” between departments and to upstream suppliers and partners. One consequence is the classic “bullwhip effect,” where the demand signal and response are distorted as information moves further upstream and away from the source of demand. Upstream manufacturers and suppliers add in safety stock at every tier to handle the increasing uncertainty, as they become more disconnected from demand. This drives up costs.

While some of the data is used to inform decisions and feed optimization algorithms, in truth, the staleness, incompleteness and conflicts result in poor decision-making and sub-optimized supply chains. 

And as we learned from the pandemic, real-time visibility to your immediate suppliers is not enough, not if their suppliers are shut down by a natural disaster, pandemic or some other crisis. It also means new opportunities, both tactical and strategic, are missed because few companies are aware of what’s going on beyond their four walls.

The impact of poor data impacts visibility and the ability to collaborate with business partners. The consequences for operations and customers are severe -- service levels suffer, inventory and logistics costs rise, waste and obsolescence increase, and fire-fighting becomes a permanent way of life.

A Real-Time Single Version of the Truth

There’s one reality, so shouldn’t there also be one version of the truth? We all know that data is like currency, so imagine if every company used its own unique type, which had to be converted each time anyone bought anything. It would be unworkable. That’s why regions like the U.S. and the E.U. have common currencies to facilitate trade.

Yet a fragmented currency system is what companies are forced to deal with when they share data between enterprises systems and with trading partners. There is no single unified data model, database or system. Instead, data is often trapped in multiple ERP instances (even within the same company), and shared via a hub-and-spoke model in many different one-to-one relationships. This devalues the currency for everyone and compromises decision-making across the enterprise and the supply network. 

On the consumer side, the code has been cracked. Consider how painful it was to get a cab in the old days. You’d have to call and order one. You’d get a rough estimate of the ETA, which was usually wrong, and sometimes it didn’t even arrive. Now with services like Uber and Lyft, you are instantly connected, buyer-to-seller, with full real-time visibility, to a single version of the truth. Both buyer/rider and seller/driver know the exact details of the process in real time, throughout the transaction, complete to review and financial settlement. It’s simple, fast and super-efficient. 

The essential feature that makes this possible is the real-time data that is shared on a real-time network.

Leveraging Multiparty Networks

A multiparty business network is obviously far more complicated than a cab ride. There are many more participants, complex moves, and complex products. But this also means that there is much more value trapped in the supply chain, so the value that can be gained from implementing a real-time is huge. Having a real-time foundation that spans departments and systems, and covers supply chain partners across all tiers is of critical importance, as this is what enables a single version of the truth for all. This eliminates the data delays that contribute to stale data, poor decision-making, inefficiency and waste.

To be truly real-time, the network cannot be siloed systems strung together. It cannot be a hub-and-spoke model where hubs get all the value, and spokes simply “serve” the hubs. It cannot be any combination of the aforementioned patched together, as that creates not a network, but an unscalable conglomeration of siloed systems.

To ensure a near frictionless flow of information across the network, it needs to be designed to be multiparty from the ground up with:

  • A single data model so a multiparty transaction exists once with permissions controlling who can view which properties. For example, a sales order and a purchase order are really just two views (buyer and seller) on the same single multiparty transaction. 
  • Shared multiparty applications with the same user interface and workflows that work intelligently together, with demand, supply and logistics coordinated.
  • Multiparty Master Data Management with each company connecting once to the network with one representation of their data, shared via a secure permissions model.

This real-time single version of the truth removes the latency across demand, supply and logistics, across the end-to-end supply chain and all tiers of supply. All relevant parties have real-time visibility to their particular concerns, including forecasts, orders, shipments, inventory, routes, moves and capacities. They have earlier visibility to current and predicted supply and production issues, production orders, capacity, utilization and deliveries. 

In terms of resilience, real-time data that spans a network means organizations have visibility across the chain and down through every tier of supply. Demand can be shared in real-time to N-tiers of supply, while capacity, schedules, logistics, and other supply-side factors can be shared with customers, so they understand their network resources and constraints and can plan accordingly.

There are fewer surprises, better planning, and better execution. The real-time network also significantly improves the speed of finding problems, and enhances the quality of resolutions, being able to utilize many more options much earlier than with traditional, enterprise-oriented models. 

In particular, real-time data provides a much sounder basis for decision-making whether done by humans or using AI. It increases the power of AI and optimization algorithms, as they run on actual execution data, consider all relevant variables, and current states, resources and constraints from across the network. This enables AI intelligent agents to make better predictions, recommendations, and autonomous resolutions.

With a real-time single version of the truth, surrounded by an ecosystem of fully informed trading partners, organizations can immediately see and respond to demand shifts and disruptions. System lead time is virtually zero, which eliminates much of the variability and associated buffers on physical lead times and safety stocks. Eliminating these unnecessary inventory buffers yields enormous cost savings.

This real-time data also enables network services across every supply chain business function to work intelligently together. This means better alignment of supply to demand and lower logistics costs.  

As organizations face more uncertainty and risk than ever it’s likely they will look to real-time networks to connect, coordinate and collaborate across their business in real time to better serve their customers, anticipate disruptions and operate more intelligently and efficiently. Those who leverage these networks will be at a significant advantage, being able to keep and attract more customers, decrease operational costs and waste, lower their cost of goods sold, and grow revenues.


Joe Bellini is COO at One Network Enterprises, provider of an AI-enabled business network platform. To learn more, visit

More in Software