Mind the Gaps to Overcome the Challenge of Fragmented Supply Chain Data

COVID-19 exposed the fragility of the supply chain. Increased demand for certain products, shortages, delayed shipments and missed production deadlines caused widespread disruption. Weeks went by as manufacturers, suppliers, producers, and distributors struggled to close the gaps and address the uncertainties. 

It’s fair to say that the pandemic exposed one of the foundational weaknesses of the supply chain: the lack of visibility due to fragmented data. Without a full picture of the entire supply chain ecosystem at any moment, altering delivery dates, addressing inventory lags, spotting or reacting to disruptions is difficult, and maybe impossible in some circumstances.

Where are the gaps, and why are they there? 

Lack of supply chain visibility, as a result of fragmented data, is not a new challenge. It is an issue that many companies are trying to solve with varying degrees of success. Transformation doesn’t happen overnight, but building a universal data model that exposes all points in the supply chain is a solid start.

There are several root causes for fragmented data:  

• Siloed legacy systems—that are past their prime. Many systems were designed and implemented a decade ago or longer. These age-old platforms may execute a specific set of tasks well but cannot function across networked operations and indirect connections; 
• Multiple systems and processes that are nearly impossible to untangle. This holds true for companies that grew through M&A and have multiple operating units, each with their own systems. With this lack of standardization, there is no common data model for how to represent information from a single supply chain;
• Spreadsheets galore and not enough (or the right) people to read, analyze, and action them. For smaller companies, for which the standard mode of operating relies on outsourcing, the trade-off for financial gain is a mishmash of reports that require time and resources to assemble. A centralized view of the entire supply chain is nearly impossible without an army of number crunchers and analysts. 
  

Ripping out an aging ERP system is a painful undertaking. What’s more, is that ERP implementation projects fail at least 75% of the time. Fortune 500 companies can hire consultants for a lot of money and build a custom solution using different point solutions. Small- to mid-size companies usually don’t have the resources or funds to support this solution.

Closing gaps means getting at the data and putting it to work 

For all the reasons mentioned above, the concept of achieving end-to-end visibility is met with a substantial degree of skepticism: if no one’s solved this problem so far, then no one can. However, this is not a proper mindset considering the potential for another significant disruption sometime in the future. But, replacing the existing supply chain IT infrastructure is no easy feat. Often, it’s unrealistic and, frankly, not necessary. 

The best approach is to aggregate data from the entire supply chain—the company’s own systems, business partners, and other external sources including flat files and spreadsheets—into one place. From there, the data can be harmonized and normalized to preserve the ability to trace information back to its source. 

Rather than rip out and replace an ERP or WMS, all the data from these sources are made visible and accessible. For example, instead of looking up a PO number, search for the shipping info to see what got shipped, users just need to look at one screen and click a couple of times to find what they need and connect the dots.

The future of supply chain is connected

Once the data is inside a single system, users can set up their own automated workflows. For example, instead of manually placing orders, users can enlist software like UnitySCM to do it for them. If inventory goes below a certain level, the system evaluates the downstream impact and replenish automatically. 

Automation doesn’t need to remain up to data scientists and IT. With today’s no-code and machine-learning enabled technology, any supply chain management platform should be quick-to-onboard, easy-to-use, and empowering for the business user. 

Flexible and adaptable, users can monitor operations and make changes with a few clicks to oversee operations and address any disruptions. For this, the system needs:

• Disruption detection: Predictive analytics to indicate where things may not go according to plan; 
• Impact analysis: Descriptive analytics that evaluates the best solution to impact downstream operations;
• Decision intelligence: Prescriptive analytics to recommend the best course of action based on data available now and predicted.

When things do inevitably break, companies will have the information to immediately know how to react, increasing resiliency and minimizing risk. Hours-long tedious processes become easy, allowing employees to get back to their real jobs—getting products out to customers. 

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Amir Taichman is the Founding CEO of UnitySCM, focused on helping companies improve supply chain operations with visibility, disruption detection and automated mitigation flows. Prior to starting UnitySCM Amir was the VP of Product Management at Elementum, where he worked with supply chain leaders at Unilever, J&J, BASF, Starbucks and others to build more connected supply chains using data.