Build a comprehensive supply chain traceability checklist

Why IT leaders need a traceability checklist

Supply chain traceability has two primary drivers: global complexity and consumer demand.

Global complexity

As business has become more globalized, supply chains have grown more complex and interconnected worldwide. For example, manufacturers and suppliers may be based in completely different parts of the world from the business.

While this scale can expand the business's market reach and present new growth opportunities, it also introduces larger, more complex risks that can be challenging to manage and mitigate. It also exposes supply chains to scrutiny from more regulatory bodies worldwide.

Consumer demand

Consumers demand transparency. They seek to understand where their products come from and how they are produced. Sustainability and ethics are top of mind, and consumers may choose to only buy products that are ethically sourced and environmentally friendly. Organizations that are transparent about their sustainability practices can build trust with consumers and foster brand loyalty.

Other drivers

However, there are other, more secondary drivers for traceability. Supply chain visibility and transparency are rising in importance, and IT is positioned to enable traceability through data, integration and automation. With greater control over supply chain operations and demystified sourcing and production, IT teams can cultivate a competitive advantage, stand out from the crowd and optimize operations.

That's what this checklist supports. IT leaders can use it as a roadmap to assess readiness and implementation steps to improve transparency and visibility across the supply chain.

Step 1: Map the supply chain data landscape

To build a supply chain traceability program, IT leaders must understand their data landscape.

Map out the supply chain data landscape as thoroughly as possible. Start at a high level and identify key data sources, like the following:

• Suppliers.
• Manufacturers.
• Distributors.
• Retailers.
• Logistics.
• Production.
• External partners.

Under each data source, list what data it collects, stores, accesses and shares. Then, sketch out the flow of data between these entities. Identify data dependencies and broader data relationships relevant to the supply chain. This will create a big-picture view of the supply chain data landscape, how data moves through it and which data requires insights across touchpoints.

Some common challenges include data silos and a lack of standardization across the supply chain. A data silo is a repository within a supply chain where data is isolated or trapped; in other words, information within the silo is not easily accessible to other parts of the business. This can cause bottlenecks, miscommunication or poor decision-making.

A lack of standardization can also be the culprit. When data moves across so many systems, it must be formatted and structured consistently for a seamless flow. Incompatibilities in data technologies or formatting can lead to silos and other inefficiencies, so teams should identify these issues upfront to resolve them.

Checklist

• Map out key data sources across the supply chain.
• List what information each source collects, stores, accesses and shares.
• Sketch out data flow, dependencies and relationships.
• Identify any visibility or traceability gaps, such as data silos.
• Evaluate data source reliability.
• Create standards for data collection, sharing and formatting, including data structure and completeness.

Step 2: Define traceability goals and metrics

Once IT teams understand the supply chain data landscape, they can determine the goals and metrics for the traceability program. Common goals include reduced waste, compliance with regulations, improved risk management and enhanced sustainability, among others.

Then, tie each goal to KPIs. For instance, if the goal is to improve risk management, set recall efficiency as a KPI. This measures how quickly and effectively teams can identify and isolate products to recall. Then, flag areas for improvement to reduce the time required to recall products and limit the number of batches affected.

High-level traceability goals and their related metrics can help determine what data to trace. For example, to measure recall efficiency, teams may need to collect data related to batch or lot number, manufacturing date, total units produced and distributed, who was contacted and when, and the number of returned units. Then, shape the traceability system around these needs.

Checklist

• Define high-level traceability goals.
• Outline metrics to measure goal progress or performance.
• Identify the necessary data to trace and report on metrics.
• Develop data governance policies to establish data collection rules.
• Define audit requirements to evaluate data reporting and identify areas of improvement.

Step 3: Choose the right traceability technologies

Next, develop a technology architecture to support traceability goals. This is often easier said than done, as no single service offers both deep visibility at the operational level and granular traceability at the product level across the supply chain. Instead, IT teams will likely have to set up an interconnected network of technologies working together to provide the insights and control needed.

A few core technologies often form the foundation of supply chain traceability. These include the following:

• Tracking technology. Barcodes, QR codes, RFID tags, GPS and Bluetooth technologies work together to assign unique digital identifiers to each item in the supply chain and transmit data to track items in real time as they move -- for example, from a warehouse to a delivery truck to a customer.
• Monitoring tools. In combination with the above, IoT devices and sensors can gather data on certain conditions, such as temperature, humidity and other environmental factors. This can help monitor and ensure product quality across locations.
• Data management tools. Data standardization, including data formatting and structure, can optimize supply chain traceability. Master data management (MDM) tools can organize and standardize data, improving its quality and completeness. Blockchain technology can also create secure, transparent ledgers for more accurate records throughout the product journey.
• Resource planning tools. In addition to data management, traceability software -- like ERP tools, and inventory, warehouse and transportation management systems -- offers a more complete picture of the supply chain. They also offer the control needed to predict future resource usage and optimize operations accordingly.
• Analytics and automation. AI and machine learning (ML) algorithms are fundamental supply chain tools. They can analyze supply chain data to evaluate performance, predict disruptions, detect anomalies and automate decisions to help operations run smoothly. Digital twins and predictive analytics can also simulate and model supply chain conditions to optimize operations.

All that said, designing and building architecture for traceability is complex. IT teams must consider the legacy technology they already use, the compatibility and interoperability of technologies they want to deploy and the architecture's scalability. This may require extensive research, planning and strict vendor evaluations to determine which would make the best partner.

Checklist

• Design the architecture required to support traceability goals.
• Establish thorough vendor criteria.
• Research the vendors that supply the necessary technologies.
• Consider how legacy technology fits into the new IT architecture.
• Factor in scalability and interoperability to determine the right technology and vendors.

Step 4: Build a unified data and integration layer

To design the traceability architecture, IT teams must select the integration architecture and build a unified data layer. These points help standardize, transform and centralize data, making it easier to set up efficient data flows and implement data governance across the digital supply chain.

At this stage, teams should have a good idea of what data to track and why, so they can get more granular and identify the exact data types to integrate. This can include everything from customer and workforce data to production and inventory data and beyond.

When teams identify the necessary data types and sources, they can consider how best to standardize it and which integration architecture makes sense for their organization. For example, they may need to prioritize API connections to exchange real-time data with partners faster. Or they might prefer to centralize data in a data lake using extract, load and transform first. This decision will largely depend on how the organization collects, stores and processes data.

MDM tools can help. MDM can establish data governance policies and standardize formatting across data types. This can ensure data quality remains high and consistent, meaning data is complete, timely and accurate, and systems can exchange data without issue. MDM can also enforce data security protocols and minimize risk when exchanging data with third parties.

Checklist

• Identify the data types for integration, how they are stored and how they must interact with other systems and applications.
• Select the data integration architecture that makes sense for the organization.
• Establish data governance and security protocols across the supply chain.
• Use MDM tools to standardize data, maintain data quality and ensure cross-system visibility.

Step 5: Consider compliance, security and ESG reporting

Supply chain traceability can support regulatory compliance and environmental, social and governance (ESG) reporting with granular insights into every stage of the supply chain. However, the business must develop ESG and security policies to appropriately protect data and maintain comprehensive ESG and compliance tracking.

To start, research all relevant laws and regulations that the supply chain must adhere to. If the organization conducts international business or has partners in other territories, factor in the applicable laws there, as well.

Examples include the following:

• The EU's Corporate Sustainability Reporting Directive.
• California's Mandatory Greenhouse Gas Emissions Reporting.
• Sustainability Disclosure Standards from the International Sustainability Standards Board and the International Financial Reporting Standards Foundation.

Second, create a list of all industry standards and ethical best practices to meet. This may include corporate social responsibility and labor standards, such as fair wages and ethical treatment of suppliers, as well as ethical sourcing, such as minimizing pollution in areas where materials are extracted.

Third, factor in all environmental considerations. How can the organization reduce waste or emissions across the supply chain? How can it integrate more sustainable practices, such as circularity? How can business leaders optimize logistics to limit environmental impact?

And finally, think through how to protect data and supply chain systems from security threats. This may require extensive risk management processes, advanced cybersecurity measures and data privacy policies to safeguard sensitive data and customer information across every stage of the supply chain.

Checklist

• Research all laws and regulations relevant to the supply chain.
• Develop an ESG framework that factors in industry standards, ethical best practices and environmental considerations.
• Design comprehensive data security and privacy policies and systems to safeguard data.
• Create systems that support easily traceable documentation and audit trails to simplify compliance and governance.

Step 6: Plan for continuous improvement and automation

Supply chain traceability is not a set-it-and-forget-it process. The supply chain continually evolves, and as such, traceability and visibility processes must adapt with it. This is where continuous improvement comes into play.

Investigate different continuous improvement methodologies and determine which is a good fit. For example, the Kaizen methodology is a Japanese philosophy that focuses on incremental improvements to boost efficiency and optimize supply chain processes.

A variety of methodologies exist, so organizations don't have to settle on just one. They can create a unique approach to continuous improvement that combines the best of people, process and technology.

On the technology side, many organizations turn to automation to build adaptive supply networks, monitor performance and identify opportunities for improvement. This often includes the use of AI and ML algorithms, robotic process automation, predictive analytics and simulation modeling, among other technologies.

These tools require advanced information infrastructure, but once set up, they can automatically flag issues -- sometimes even before they happen -- remediate errors, predict future events or opportunities, and more.

With a continuous improvement program, IT teams can create performance feedback loops and regularly achieve operational breakthroughs through iterative improvements that result in an optimized supply chain system that can adapt to even the most severe changes.

Checklist

• Adopt a continuous improvement methodology that works for the organization.
• Invest in automation that seeks to iteratively improve supply chain operations.
• Create performance feedback loops that enable continual optimization.
• Establish systems that can monitor the supply chain on an ongoing basis.

Supply chain traceability is about more than just sustainability. It can help business and IT leaders drive efficiency, improve risk management, streamline compliance and elevate quality control, among other factors that can directly benefit the bottom line.

IT teams unsure where to start their traceability journey can use the checklist above to assess their organization's capabilities, understand what to track and why, and lay a solid foundation for their supply chain traceability program.

Jacob Roundy is a freelance writer and editor with more than a decade of experience with specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change and sustainability. His writing focuses on demystifying tech, tracking trends in the industry, and providing practical guidance to IT leaders and administrators.