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Supply Chain Data Sharing for Greater Visibility in 2024

Data has become an invaluable asset in supply chain management (SCM), helping businesses achieve a higher level of visibility, transparency, and resilience. The term ‘supply chain data sharing’ has thus become a buzzword in the corporate realm. So, what does sharing data mean for supply chains, and why has it assumed such a crucial role today?

This article discusses these questions and explores ways to address the challenges surrounding sharing of data in supply chains. The article also presents some best practices to enhance and promote the sharing of data among suppliers and business partners.

To leverage no-code supply chain software in your business to facilitate data sharing, check out our data-driven list of no-code supply chain software to find the right fit.

What does data sharing mean for supply chains?

Data sharing in the supply chain refers to the process where different entities involved in the supply chain, such as manufacturers, distributors, retailers, and logistics providers, exchange information. 

This information can encompass a wide array of data points, such as: 

  • Inventory levels
  • Demand forecasts
  • Sales data
  • Production plans
  • Delivery schedules and more.

Sharing this data can lead to enhanced transparency, greater operational efficiency, and improved decision-making across the supply chain.

Why is it important now?

The importance of sharing data in supply chains has been particularly underscored in the post-pandemic business environment. COVID-19 significantly disrupted global supply chains, and the after-effects continue to ripple through industries even in 2023 (Figure 1).

Companies realized that without a higher level of supply chain resilience and visibility into their partner’s operations, and a shared understanding of critical data, their ability to respond to crises and rapidly changing market conditions is severely hampered.

Figure 1. The pandemic had a substantial negative impact on supply chains

A graph showing the level of impact the pandemic has on companies.
Source: EY

Top 5 challenges of data sharing in supply chains

An illustration listing the 5 supply chain data sharing challenge discussed in this section

While the benefits of sharing data throughout the supply chain are immense, it’s no easy task to achieve. Here are 5 supply chain issues that often stand in the way.

1. Technology integration

Diverse technological infrastructures can pose integration challenges. For example, if a retailer uses a different system for inventory management than its supplier, sharing real-time inventory data can be problematic. 

With the increasing number of citizen developers, low-code or no-code platforms can enable global supply chain managers to develop integrated solutions. No-code platforms can help supply chains overcome integration challenges and contribute significantly to encouraging the flow of data across the value chain.


UCBOS provides a no-code platform, especially customizable for supply chains, to help organizations build integrated supply chain solutions. The system equips supply chain leaders with dynamic business data models to help streamline the flow of data across the value chain.

2. Data silos

One of the significant barriers to sharing data in your supply chain is the existence of data silos. A data silo happens when data is isolated and hoarded within different departments or organizations, making it inaccessible to others who could potentially benefit from it. Any organization can end up with data silos without a strategic plan for data management. For instance, a manufacturer may not share its production plans with its distributor, leading to overproduction or shortages.

3. Data privacy and security concerns

Businesses often hesitate to share data due to concerns over data privacy and potential misuse. This becomes even more pertinent given the increasingly stringent data protection laws and the rising incidents of cyber threats. Another reason is that companies do not wish to disclose their internal operations and share data related to:

  • Their carbon emissions
  • Labor management practices
  • Sourcing of raw materials, etc.

4. Data standardization

With multiple entities involved, the data can be presented in various formats and structures, creating problems in assimilation and analysis. The lack of data standardization, in terms of data quality and formats, is a substantial impediment to effective data sharing.

5. Organizational culture

In supply chain management, an organization’s culture is a significant determinant of how readily data sharing is adopted. If a company’s culture doesn’t endorse openness and collaboration, data-sharing initiatives may be met with resistance.

Employees could view sharing data with other entities in the supply chain as risky, fearing that it might expose vulnerabilities or dilute their unique contribution.

5 best practices to overcome data sharing challenges in supply chain

An illustration lists the best practices for supply chain data sharing discussed in this section.

Embracing the power of sharing data necessitates a cultural shift and the adoption of best practices. Here are some best practices that can foster effective data sharing in the supply chain.

1. Adopt a collaborative mindset

Collaborate with suppliers beyond the tier 2 network. Studies have shown that only 2% of companies have visibility beyond the 2nd tier (Figure 2).

Check out this guide to improve supply chain collaboration.

Consider a global company that finds that its different teams—purchasing, logistics, warehouse, and sales—are functioning in silos. The purchasing team has valuable data about vendor performance and delivery times that the logistics team could use to better plan transportation routes and schedules. Similarly, the sales team has insights into customer buying patterns and seasonal trends that can aid the warehouse team in efficiently managing inventory.

Recognizing the potential benefits of collaboration, the SCM team adopts a shared mindset. They implement an integrated SCM system that enables each team to access and utilize data from the others. The outcome is a cohesive relationship where all teams can make informed decisions based on shared data. This results in improved overall supply chain performance with:

  • Improved forecasting
  • Optimized inventory levels
  • More efficient transportation schedules
  • Better service to their customers.

You can also find the right supply chain collaboration software through this vendor selection guide.

Figure 2. Lack of visibility in supply chains

The survey results are presented in a graph showing that only 2 percent of companies have visibility beyond Tier 2 making supply chain data sharing more important.

Source: McKinsey

2. Ensure data security

Take the case of a logistics and supply chain management company. They handle sensitive data like vendor details, transportation routes, and delivery schedules. To protect this information, the company implements multi-factor authentication for system access and encrypts data both at rest and in transit. They also anonymize the data to further safeguard confidentiality.

Additionally, the company is transparent about its data security practices, sharing updates with its different stakeholders and partners. This transparency helps build trust, ensuring a reputation for secure supply chain operations.

3. Standardize data

A global supply chain network collaborates with numerous partners, each having its own data format. They understand the need for data standardization to streamline sharing data.

The company adopts a universal data format like XML or JSON. They also use automated data exchange platforms for seamless data transfer between their systems and their partners. This uniformity in data leads to simpler analysis and decision-making based on comprehensive and unified data, enhancing the efficiency of their supply chain operations.

4. Leverage technology

With digital transformation on the rise, supply chains are being revolutionized by technology. A supply chain solutions provider faces challenges in real-time data sharing and secure data transfer with its partners. They turn to technology to resolve these issues.

The company uses APIs to integrate its systems with its partners’ for automated data exchange. They leverage artificial intelligence (AI) for data insights and blockchain for secure and immutable data records. As a result, they can make better real-time decisions, provide enhanced data security, and build trust amongst their partners.

5. Establish clear data-sharing agreements

Consider a group of companies in a supply chain network sharing data for improved operational efficiency. To ensure all members understand their data-sharing roles and responsibilities, they establish a detailed data-sharing agreement.

The agreement outlines what data should be shared, how, and who can access it. It also stipulates data usage, protection guidelines, and penalties for data misuse. This clear agreement promotes a harmonious and productive relationship among the network members, fostering trust and mutual growth.


While challenges exist, through cultural shifts, technological advancements, and the adoption of best practices, sharing data can be streamlined across global supply networks.

Further reading

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Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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