Blockchain in Supply Chain: Can You Trust Your Data Sharing Partners?

Not everything is changing.

When it comes to the blockchain, artificial intelligence and other emerging technologies we’ll increasingly implement in go-forward supply chain practices, we’ll still rely on the same essential element: trust.

Now more than ever, to benefit from these technologies, all parties in the supply chain will be required to share data at an unprecedented level. Possibilities for improved efficiencies, real-time visibility, data security, vendor compliance and other benefits will flow from shared data streams.

Yet many companies are culturally uncomfortable with the depth of transparency that will be required. Those organizations that do not participate will find themselves increasingly isolated from the economic mainstream.

Certainly, organizations should exercise due diligence in understanding the partners who will access their information and how it will be used. Organizations don’t have to share with every vendor or service provider that requests access. But enterprises must prepare for the new world of shared data with policies and procedures for these technologies emerging in the supply chain environment.

Do you have concerns about sharing data with your supply chain partners? If so, do you know why?

Blockchain Builds on Trust

Technologies like blockchain create a new “trust economy” where the old intermediaries are replaced by new systems. As blockchain and artificial intelligence enter day-to-day use, sharing data with third parties and vendors will be necessary. The system creates security through technology rather than relying on familiar relationships of the past.

To be useful, your organization’s data must be validated to ensure it is accurate and complete. Information stored in the blockchain isn’t valuable if it’s wrong.

Blockchain, in particular, is developing as a safe, customizable standard to share data in a way that protects proprietary information while providing value from the openly available information. For example, companies can manage supply chain vendor compliance issues without revealing specifics about their supply chain.

As the use of blockchain moves forward, it will be critical to strike a balance between transparency and confidentiality for all stakeholders as they adopt the technology to record and share supply chain data. With well-thought-out restrictions, a company could use the blockchain for internal purposes and share only the necessary data with other stakeholders.

Sharing data makes the most sense when it’s part of a strategy to improve processes or connect with partners in the supply chain. Blockchain information will drive tactical and strategic decisions that support predictive analytics and demand forecasting. Companies fear losing control of their data for any number of reasons, from baring their operations to competitors to sharing accurate costs with vendors. Some internal organizations see data management as their base of power and are reluctant to be open to external engagements.

Validate Captured Data to Maximize Technology Capabilities

Most organizations don’t have the internal capabilities to support endeavors focused on utilizing emerging technology applications like blockchain. An Enterprise Logistics Provider with deep analytical experience can help you identify and focus on the actionable information that you already capture on a regular basis.

With a trusted partner, your organization can manage its data-sharing strategies to share only what’s required and maintain control of your information, while connecting with the benefits of blockchain.

To find out more about why and how you should share your organization’s data, read our resource guide: AI, Blockchain, Machine Learning: Is Your Data Ready?

Data Analysis: What is Your Data Trying to Tell You?

Unfortunately, many organizations still operate in siloed environments with data collected and housed in fragments across different departments, such as location-based procurement teams. Organizations that expand their data management and data analysis capabilities often do so without verifying the accuracy and depth of the data. There may be a mismatch between what products have been sold, what’s been shipped, and what’s been returned. What’s in the database may not reflect the reality on the inventory shelves. Or product data may have incorrect dimensions, leading to false assumptions about warehouse space and shipping weights.

The results of initiatives such as inventory optimization and carrier compliance could be skewed from low-quality data, leading to decisions that could reduce efficiency in your supply chain.

Are you making decisions driven by inaccurate data?

Analysis Drives Decisions, Start with Better Data

Good decisions start with clean, accurate data. Data input via manual processes or information that may require on-the-spot decision-making tends to have lower accuracy than data collected through technology. Back-end systems that are incompatible may require redundant inputs, leading to duplication and mistake

As the flood of data grows, it’s vital to close the loop – collection is not enough. The information must be converted to actionable insights to deliver value across the supply chain. Clean data is simply information that reflects a high degree of confidence in its accuracy, stored in the correct, usable format.

Confirm Accuracy, End Goal before Analysis

Identify end uses. Decide which challenges you want the data to help solve to decide which data to collect.

Implement standards. Develop standards for collecting and manage data such as formats and keywords.

Focus on the most relevant information. Understand the inputs that are most critical to your business

Convert to actionable insights. Focus on data for KPIs and decision-making.

With accurate, thorough data, your organization can uncover hidden opportunities to optimize your processes. Optimization software and simulation tools can reveal options that drive structural changes to deliver the highest level of value to the customer. With increasing customer expectations for improved visibility into product locations and expected delivery times, data accuracy has never been more essential.

Objective Data View Accelerates Performance

Keep in mind that data accuracy is a marathon, not a sprint. It requires systems and policies in place over the long term. Work with an Enterprise Logistics Provider with deep technical expertise in data analysis and cleaning processes to improve current data and set up improved processes going forward. A trusted third party can help develop an objective view of your data landscape, including visibility down to the SKU level to generate strategic insights and shape demand forecasting. 

For more insights into your data accuracy journey, read our resource guide: AI, Blockchain, Machine Learning: Is Your Data Ready?