5 Strategies for Using Supply Chain Data Management to Drive Resilience and Cost Savings

Learn five strategies for using supply chain data management to navigate disruptions, improve decision-making, and maintain competitive advantage no matter what the future holds.

In brief:

• Strong supply chain data management delivers measurable results: lower costs, better forecasts, faster problem-solving, and greater resilience when disruptions hit.
• Master data management breaks down data silos to create one reliable source of truth, enabling better data governance and setting the stage for supply chain analytics and AI.
• Integrating supply chain data across systems and partners provides real-time visibility for proactive supply chain planning and quicker responses to disruptions.
• AI and machine learning improve supply chain analytics by spotting patterns, sharpening demand forecasts, and automating processes for greater agility.
• Building a data-driven culture requires training employees and earning their trust by showing how data analytics in the supply chain supports their expertise and frees them for higher-value work.

Whether the result of global instability, extreme weather, geopolitical tensions, or capacity constraints, supply chains today face constant disruption and uncertainty. Increasingly, organizations are turning to supply chain data management strategies to build business resilience and lower risk.

That’s because harnessing the power of data is a proven way to improve supply chain planning, ease pain points, and prepare for whatever disruptions may come.

In this blog post, we’ll look at five supply chain analytics strategies that will help you better manage your supply chain:

1. Use artificial intelligence (AI) and machine learning for supply chain planning
2. Implement intelligent process automation in supply chain operations
3. Deepen visibility into supplier and partner data
4. Apply Lean Six Sigma for supply chain process optimization
5. Build data literacy and data-driven decision-making

But first, we’ll look at three steps you’ll need to take before tackling the five strategies.

3 Steps for Getting Your Supply Chain Data in Order

The five strategies flow from having “master data” — clean, un-siloed data that provides a single source of truth for everyone who needs to use it.

However, according to Cisco’s first annual AI Readiness Index, 81 percent of more than 8,000 CEOs surveyed admit that their data remains in silos, making it difficult to access and analyze.

As the Cisco report notes, this lack of centralization presents considerable risks for data and AI management, which poses risks to supply chains around the world.

The good news: If your company’s supply chain data is in multiple clouds, or you struggle with organizational silos between groups such as procurement, operations, finance, and information technology (IT), there is a solution: master data management (MDM).

1. Implement Master Data Management

MDM gathers data from multiple sources and cleans it by identifying inconsistent, redundant and trivial data.

MDM also supports regular data reviews. As Centric Consulting Data & Analytics Lead Rob Cochrane notes, “Effectively eliminating the bad data requires a multi-strategic, structured approach that purges your systems in a timely and consistent fashion, identifies how and why the data became compromised and, ultimately, pinpoints the root cause of the data infection.”

An MDM system can automate much of this work, and AI is continually improving the automations. Today’s MDM tools can:

• Alert you when they identify bad supply chain data
• Provide near-real-time data validation for your most critical systems when problems occur
• Spot data usage patterns, allowing you to schedule data cleanups where errors occur in a process
• Enable you to set rules to activate data cleansing, or to create a feedback loop for data users and caretakers

Proper data governance is also essential for establishing data stewardship and accountability, as well as implementing quality control and continuous improvement processes. As we’ll see later, these are needed to create a data-driven culture.

2. Modernize Your Data Infrastructure

Once you have high-quality, easy-to-access, and trustworthy data, you’ll be ready to store it in a more accessible place, such as a data lake or data warehouse.

“Think of a data lake like a city’s water reservoir,” says Centric’s Columbus Lead Leigh Helsel. “In the same way that a reservoir stores water for future use, a data lake stores information for later analysis.”

Data lake tools replicate data from its sources, in near real-time. That allows analysts to work with it without corrupting source data.

However, data lakes work best when in the cloud. Cloud vendor Cloudzero notes that most large enterprise companies are already enjoying the efficiency and security improvements the cloud offers, and many use multiple public and private clouds. Still, less than half of small companies have moved made the transition.

If you are not yet in the cloud but want to get there, a cloud readiness assessment can help.

3. Integrate Your Supply Chain Data

Your data is now out of its silos, cleaned, and stored in a single modern place. You’re now ready for the core technologies, data processing and standards needed for data integration: making all your data work well together.

You have the choice of many vendors for integration solutions, but for this blog post, we’ve chosen to focus on Microsoft solutions.

The Microsoft technologies below can play important roles in master data management, supply chain analytics, and integration:

Core Technologies

• Microsoft Dynamics is an enterprise resource planning (ERP) system for centralizing procurement, inventory and financial data. Dynamics also supports additional core technologies, including Microsoft Dynamics Supply Chain Management, Microsoft Dynamics 365 Finance, and Microsoft Dynamics 365 Intelligent Order Management.

Data Integration and Connectivity

• Microsoft Azure Integration Services includes apps for automating workflows between supply chain systems, managing application programming interfaces (APIs) for real-time data exchange, and enabling messaging between systems.
• Microsoft Power Automate automates repetitive tasks and connects apps without requiring advanced coding.
• Microsoft Power Apps allows developers to build custom apps for supply chain processes.

Data Storage and Analytics

• Microsoft Azure Data Lake and Azure Synapse Analytics centralize storage and advanced analytics for large-scale supply chain data.
• Power BI provides visualizations into key metrics, such as inventory levels, lead times and supplier performance.
• Azure Machine Learning provides predictive analytics for demand forecasting and risk management.

Security and Compliance

• Microsoft Purview helps implement data governance and compliance across integrated systems.
• Azure Active Directory secures identity and access management for supply chain apps.

With integration checked off the list, you’re now ready to explore the five strategies for making the most of your supply chain data management practice.

5 Strategies to Improve Your Supply Chain Data Management

1. Use AI and Machine Learning for Supply Chain Planning

Until recently, the combination of more traditional AI and proven automation approaches such as machine learning (ML) was the new frontier of process improvement.

However, today’s generative AI makes the combination even more powerful. While traditional ML could only make predictions based on historical data, modern AI can transform demand planning.

Notes former Forbes Tech Council member Ahmer Inam: “Generative AI excels in analyzing extensive historical data to discern patterns and correlations that were previously elusive. By identifying key drivers such as seasonal trends and emerging market shifts, companies can anticipate demand fluctuations with greater accuracy, minimizing the risk of inventory imbalances.”

2. Implement Intelligent Process Automation

Generative AI can also use data analytics to automate your business processes and productivity.

For suppliers, AI-powered automation, or intelligent process automation (IPA), allows manufacturers and distributors across your supply chain to build agility and operational productivity. Workers at each supply chain node will be able to pivot quickly in response to changing conditions, and end customers will receive their products more quickly.

In other posts, we’ve taken deep dives into preparing manufacturing companies for AI and a specific AI in manufacturing use case. Similar approaches and technologies apply to supply chain initiatives.

For example, Centric Operational Excellence Co-Lead Kent Hansen has explained how Siemens used procurement tool Scoutbee to find alternate suppliers during an unexpected shortage of a patented product. Similarly, Hansen notes, Walmart integrates weather and demographic data to better predict demand.

In all these cases, the result is more satisfied customers, happier employees, and higher retention rates, all of which lead to lower risk.

The golden rule is to start small. Often, that means projects that may not sound exciting, such as back-office tasks, but these projects often have more demonstrable return on investment (ROI). As wins accumulate, employee excitement and innovation will grow.

3. Deepen Visibility Into Supplier and Partner Data

Your data and analytics team’s ability to help leaders make decisions will be even more powerful if they have visibility into your partners’ data as well as your own. But how can they attain this visibility?

Again, while you have many technology options to help, we’ll focus on Microsoft tools:

• Dynamics 365 Supply Chain Management can sync with Dynamics modules on partner systems, allowing for accurate supply chain planning, inventory optimization, and cross-functional/cross-partner visibility.
• Azure Synapse Analytics is your data engine for running what-if scenarios using data variables from multiple partners.
• Power BI can pull data from other suppliers to build dashboards and visualizations for shipment statuses, inventory levels, risk indicators and more.
• Azure IoT Hub and digital twins can track physical assets such as equipment, warehouses or shipments. They also allow you to build digital models of your supply chain.
• Power Automate is great at creating workflows and alerts to let teams know when they’re approaching risk thresholds.
• Microsoft Fabric is an analytics platform that helps create the single source of truth for your partners’ data, just as your MDM does for your data.

While these tools’ individual contributions are powerful, together they help move your organization from reactive to proactive management planning and risk management based on real-time data.

4. Apply Lean Six Sigma for Supply Chain Process Optimization

Supply chain data management also sets the stage for using process improvement methodologies such as Lean Six Sigma (LSS) to increase productivity, flexibility, quality and speed. It mixes elements of two similar, yet complementary, operational excellence approaches, Lean and Six Sigma.

“LSS combines rapid improvement with long-term capability building,” Hansen and Centric Business Improvement Lead Jack Johnston said. “By systematically applying the LSS framework, you can eliminate confusion about what to do and when while keeping everyone on the same page.”

An important LSS concept is Modular Kaizan, “designed for busy workplaces with a high level of interruptions” — perfect for supply chains. When disruptions occur, LSS provides a disciplined framework for ensuring that employees use supply chain data to measure the disruption’s impact before deciding on a course of action.

In a 2022 Forbes article, an industry analyst described how Apple assembler and consumer electronics giant Foxconn used LSS principles to be more flexible at a time when agility was crucial. For example, according to Forbes, its production lines switched quickly to make ventilators for hospitals during the first wave of the COVID-19 pandemic in 2020.

Adopting process improvement best practices can also help you achieve strategy-to-execution goals by isolating process issues, improving effectiveness, and delivering more value on the spot.

5. Build Data Literacy and Data-Driven Decision-Making

No matter how clean your data is, what technology you’ve implemented, or how well you’ve integrated your data, you won’t achieve the best supply chain data management results without your employees’ support.

Fortunately, the master you’ve created with your MDM and other supply chain data management programs will help you overcome the biggest hurdle: trust. Still, employees will need help understanding the “why” behind your data.

One group that can be especially challenging to reach is employees — or even executives — who have learned to make decisions based on their work experience or their gut. To convince these workers that data is the first place to go for decisions, you must answer their “what’s in it for me?”

Some ways to address that question include:

• Showing that data will likely validate, not negate, their hard-won experience
• Demonstrating that a more data-driven organization will give them time back (they can finally go on vacation because they can delegate with confidence!)
• Encouraging them to think about more strategic work they can take on as data and AI support day-to-day tasks

Of course, building both data literacy and a data-driven culture comes down to change management. At Centric, we have a change management team for overall support, but some tactics we’ve seen work well for supply chain data are:

• Focusing on the consistency, reliability and resilience employees will enjoy by working in a more data-driven organization
• Making sure everyone can get access to the data they need and not the data they don’t need
• Training employees to access and use the data
• Celebrating and sharing data wins

Once you’ve begun implementing one or more of these strategies, you’ll need to measure your efforts’ success to determine if you’re on the right path. Below are some common supply chain metrics to track:

• Cost Savings and Efficiency Gains: Inventory carrying costs, transportation costs, or procurement costs often result from efficiency gains
• Improved Forecast Accuracy and Demand Planning: Forecast accuracy, stock-out incidents, production planning cycle length
• Reduced Time-to-Decision and Faster Issue Resolution: Average time to identify and respond to supplier disruptions, issue escalation time, number of cross-functional alignment meetings
• Quality Improvements and Customer Service Metrics: Defect rates, on-time delivery performance, customer satisfaction score
• Business Resilience Indicators: Number of single points of failure, recovery time for supply chain disruptions, supply chain risk score (composite index of supplier financial health, geographic concentration, and alternative sourcing options)

Become More Resilient With Supply Chain Data Management

Disruptions that bring supply chains to a halt can happen at any time, but data-driven organizations are better prepared to respond more quickly and with greater confidence — and less risk.

With high-quality data and a strong supply chain data management program in place, these companies are more ready for:

• Demand volatility and inventory optimization
• Supplier failure and alternate sourcing
• Logistics delays and route optimization
• Regulatory changes and compliance tracking

If your organization lacks the data access and visibility to unlock these benefits, the time is now to invest in supply chain data management that will secure the better, faster decision-making, efficiencies and agility that challenging times demand.

If you need help with transforming your organization to enjoy these benefits up and down your supply chain, seek a vendor who offers integrated consulting services.

For example, at Centric we have technical teams, data and analytics teams, operations excellence teams, and change management teams. These groups support each other, allowing you to begin prioritizing your data initiatives more quickly.

Assessment tools can also help you measure your current data maturity and resilience levels. For example, we offer an 11-question business resilience assessment, as well as other assessments for related topics like AI readiness, enterprise architecture, automation prioritization and more.

Our Operational Excellence team works to create scalable, flexible solutions centered on your supply chain needs. Interested in working together to improve your processes and data management? Let’s Talk