Your Supply Chain Risk Management Strategies Might Be Creating More Problems Than Solutions

Nine in ten companies have established risk management strategies in the years following COVID. These efforts, according to a 2022 McKinsey survey, include actions like implementing dashboards for end-to-end visibility (67%), scenario planning (37%), and working on gathering sufficient data (53%). 

While these efforts have had a positive effect—companies are reporting that they’re 1.5-2x less likely to suffer supply chain impacts—they’re not quite as powerful an antidote to supply chain problems as most supply chain leaders had hoped. 

Supply chain executives still don’t feel confidently equipped to handle the disruptions of today and tomorrow. From rapidly expanding sanctions lists to new environmental regulations that demand an unprecedented level of visibility into one’s supply chain, new obstacles are leaving leaders up at night, wondering what new risk is going to surface next. 

To overcome these challenges, businesses are increasingly turning to strategies that have become the hallmark of proactive risk mitigation. These include:

1. Increasing supplier collaboration (61%)
2. Investing in digital transformation (42%)
3. Enhancing demand management (37%)

While these approaches are a step in the right direction, they ultimately fall short of addressing today’s risks because of the current methods of implementation. Why? Because these strategies lack two crucial elements: scalability and sustainability.

Supplier Collaboration: A Manual Process Bound by Limitations

Supplier surveys continue to be the most common method of gathering critical information on a company’s supply chain. These surveys, typically conducted on an annual or biannual basis, provide insights into regulatory compliance, risk management, and overall supplier performance. However, as a manual process, supplier collaboration through surveys is burdened by several significant limitations, especially when considering scalability and sustainability.

Surveying Suppliers is a Static Process

First, the manual nature of surveys is one entrenched in the collection of static information. The data collected through questionnaires and other inquiries provides only a temporary snapshot fixed to the time when it was completed. For instance, if a supplier is surveyed in March and is placed on a banned entity list in June, companies may remain unaware of this development until the next survey cycle—which is often up to a year later. This creates a serious visibility gap, leaving companies vulnerable to the compliance risks, legal liabilities, and other forms of disruptions that emerge between survey intervals.

Supplier surveys also present another problem: they’re simply not scalable to the level that’s going to be required by the impending slate of ESG regulations. As directives demand more insight into supplier practices and ethics, the volume of surveys being submitted to Tier 1 suppliers inquiring into their sub-tier visibility is going to expand dramatically. As a result, companies that ramp up the quality, comprehensiveness, and rigor of their surveys may still face unresponsive suppliers and diminishing returns.

Suppliers Often Experience Survey Fatigue

Survey fatigue is a growing concern. A 2024 Voice of the Supplier survey of suppliers found that 61% of these manufacturers felt that “their most important customer sends them too many information requests.” Additionally, 60% of those surveyed also noted that “their most important customer expects them to do too much administration.”

Suppliers that deal with multiple clients who each demand extensive compliance information can quickly become overwhelmed. This results in incomplete or rushed responses, particularly from mid-tier suppliers who may not prioritize smaller companies. Even when responses are collected, the quality and depth of data can often be compromised. This further limits the effectiveness of supplier collaboration efforts.

Suppliers Don’t Always Share Your Priorities

It’s a truism that most professionals operating in the supply chain space know all too well: What’s good for the end manufacturer may not always be ideal for their suppliers—especially where ESG and CSR regulations are concerned. Suppliers may not share the same priorities or ethical standards as the companies they sell parts, subassemblies, and other products to. In trying to portray themselves as positively as possible, maintain customers, and avoid any potential regulatory issues, suppliers may selectively report information, misrepresenting their compliance status or omitting important details. This can leave companies investing in and acting on a version of the truth that may not fully reflect reality.

Digital Technology: Adoption Without a Roadmap

The wave of digital transformation has ushered in countless software solutions designed to help companies manage everything from CO₂ emissions to supplier risk. While adopting these tools is a move toward modernization, many companies have jumped into digital solutions without a comprehensive roadmap. 

One-Off, Specialized Technology Solutions Are Fragmenting Your Data

Most digital solutions on the market today are "point solutions," built to address very specific problems such as ESG scoring or monitoring supplier financial stability. In the push to address specific challenges in the market, many companies have adopted technology without a plan for fostering technological interconnectivity. The result is an increasingly complicated technology landscape fraught with data silos employees struggle to navigate. According to BetterCloud, companies now use an average of 112 SaaS applications, with employees shifting between 35 job-critical applications more than 1,100 times a day. 

While these digital tools help companies address individual challenges, they also create a new, broader one: data fragmentation. Data fragmentation refers to circumstances where information is disorganized, disconnected, and scattered across myriad databases. Professionals operating in work environments plagued by data fragmentation often find it nearly impossible to piece together a cohesive picture of their entire supply chain.

A Lack of Integrated Data Is Obscuring Your Big-Picture Visibility

This lack of data integration contributes to one of the biggest flaws in most companies' digital transformation efforts: lack of visibility. Typically, when companies talk about suffering from a lack of visibility in supply chains, they’re referring to a dearth of data. That remains a core recurring problem. But another, newer kind of blindness is emerging, one that is as much a function of our technological tools as it is of our dense, sprawling value chains. Disconnected data sets that cannot be effectively synthesized into a coherent, holistic view are preventing companies from seeing the bigger picture the data they do have is painting.

Data silos are nothing new, and organizations have been fighting against them for decades. But the haphazard embrace of multiple disparate technology platforms without regard for the impact they’re going to have on overarching data visibility and cross-departmental collaboration is leading companies astray. These fractured, disconnected data ecosystems can lead supply chain professionals to miss core vulnerabilities across their suppliers and within their own internal operations. 

Synthesizing Data Often Demands Employees Use Shadow IT

To deal with the lack of integrations between databases, many employees turn to "shadow IT" solutions—unsanctioned software or manual workarounds like Google Sheets or low-code tools—to aggregate data from multiple systems. However, these stopgap measures come with their own set of problems, particularly in terms of data accuracy and cybersecurity. As much as 32% of SaaS solutions used by companies are not approved by IT, leading to significant risks in data management.

These workaround systems are also rife with errors. A 2024 academic study found that 94% of spreadsheets used in business decision-making contain errors, posing severe risks of financial losses and operational mistakes. These databases are often populated through manual copy-paste methods and have no standardized processes for verification, leading to makeshift collections of data that have not been adequately vetted. 

As companies continue to collect more and more data for risk management and the various assessments that come with it, the absence of a solid technology strategy can lead to a SCRM process that is laden with immense volumes of information that may not be factually accurate. 

Demand Management’s Data Problem

While demand management is essential for optimizing resources and aligning with supply capabilities, it faces one fundamental challenge: the accuracy of the data driving its decisions. Demand management relies on precise, clean data to forecast and plan effectively. But when that data is flawed, outdated, or fragmented, even the most carefully crafted demand management strategies will fail to deliver the desired results.

Your Databases Are Compromised by Dirty Data

The success of demand management hinges on the integrity of the data professionals are feeding into demand forecasts. If the data from sources like sales and marketing teams, suppliers, and historical records is inconsistent or inaccurate, the entire process is compromised. Gartner research indicates poor quality data costs organizations “at least $12.9 million a year, on average.”

A Z2Data analysis of product BOMs further solidifies the case that inaccurate or flawed data can impose a serious strain on supply chain operations. The analysis found that companies used over 500 different spelling variations for Texas Instruments’ products in their systems, creating unnecessary complexity in tracking and managing suppliers. This type of disorganization can either trigger shortages that lead to lost sales or—on the opposite end of the spectrum—surpluses that drive up holding costs and contribute to wasted product.

There’s No Verification Process for Supplier-Provided Data

Many companies rely heavily on supplier data for demand planning without having robust verification processes in place. If supplier information on lead times or available stock is incorrect or not independently validated, it can lead to significant disruptions.

In fact, a Z2Data analysis revealed that of the 473,190 parts that became obsolete in 2023, around 142,000—or an appreciable 30%—had no accompanying product change notification (PCN) issued from the manufacturer. 

Relying on unverified or incomplete data leaves companies vulnerable to sudden shortages or delays—scenarios that could have been mitigated with better demand management controls. Without accurate data, companies are pushed into a reactive response, forced to make snap decisions based on incomplete or misleading information. As regulatory demands increase the volume of data that must be collected and managed, these challenges will only become more pronounced, compounding existing issues with data accuracy and verification.

Embracing Dynamic Supply Chain Risk Management

Despite significant advancements in proactive risk management, current approaches are struggling to keep pace with today’s supply chain complexities. Manual processes like supplier surveys, fragmented digital ecosystems, and unverified data are creating gaps in visibility and scalability, leaving companies vulnerable to compliance risks and disruptions. 

These limitations highlight a critical need for strategies that are not only forward-thinking but also capable of addressing the fluid, interconnected challenges of modern supply chains. Without scalable solutions and real-time data integration, even the most well-intentioned risk management efforts will fall short of expectations.