In supply chain management, you can expect the unexpected. A storm grounds flights. A supplier half a world away faces production delays. A shipment sits stranded. These situations happen every day and are especially critical in healthcare, where on-time delivery can make the difference in a patient’s life.
Regardless of the industry, supply chain disruptions can impact outcomes. While efficiency and cost management are important, a primary challenge is to maintain service consistency in an inherently unpredictable environment. The goal is to anticipate change, not simply react to it.
Likewise, the time to optimize the resiliency of your supply chain is now to help keep operations running smoothly no matter what happens next. And building an optimization playbook relies on prescriptive and predictive data analytics, which are essential to transforming operational pressures into a strategic advantage that helps build customer trust.
The two-step approach to analytics
The foundation of an effective resiliency strategy is understanding your operations, what you need and when you need it. Then, knowing how to not only address supply chain disruptions, but also how to help minimize or prevent them in the first place.
Data analytics can help you get there. Start with prescriptive analytics, which helps you look into the past to understand historical trends and the current state of your operations. After all, you can’t know where you’re going if you don’t understand where you’ve been.
By examining past performance, prescriptive analytics can help you identify when and why an event occurred — and more importantly — what you can do about it, as historical data helps guide recommendations.
For example, prescriptive analytics can help improve resiliency by identifying the need for dual sourcing when suppliers have documented risk, adjusting safety stock levels for SKUs with proven volatility and establishing alternate routing where needed.
Prescriptive analytics can also be used for cost management, such as revealing year-over-year spending on overnight delivery service, the highest cost shipping mode. If that spend is trending higher, you’ve pinpointed a key cost savings opportunity.
The second step is predictive analytics, which builds upon the foundation established by prescriptive analytics. Predictive analytics helps forecast future scenarios and inform proactive decisions that help address them. The goal is to act with foresight, not hindsight, so you can prepare in advance to address potential supply chain issues and cost savings opportunities.
Continuing with the overnight delivery example above, use predictive analytics to identify the individual shipments where ground service would have still arrived the next day but at a lower cost. With the savings quantified, you’ve established the model for making a more cost-effective shipping decision in the future.
The prescriptive/predictive approach to data analytics transforms resiliency from a defensive posture to an offensive performance advantage. You’ll help enhance risk mitigation efforts while increasing efficiency and improving resource planning. Most of all, you’ll make more informed decisions across the entire supply chain network.
How data analytics helps improve resiliency
Combined, prescriptive and predictive analytics help protect outcomes. And that helps build and maintain trust with customers, especially during peak seasons or during supply chain disruptions such as weather events. In these moments when on-time delivery is at risk, you have the opportunity to build trust even further by implementing a data-driven resiliency plan.
Data analytics help optimize your logistics and drive resiliency in several ways:
· Enhance visibility: This is the foundation of resiliency planning. More than tracking products, you’ll have a total view into what, how and where you’re shipping. By having complete transparency into shipping behavior across an entire organization, you’ll have the insights needed to make more informed decisions for mitigating risk.
· Support earlier intervention: An important aspect of service consistency is responding quickly to supply chain issues before they escalate into bigger problems. Data analytics empowers you to operate with foresight — not hindsight. Advanced analytics with integration capabilities (such as APIs or webhooks, which automatically connect your systems to shipping partners’ systems and continuously exchange updates without manual checks), provides near real-time data-sharing that notifies immediately of shipping delays or disruptions so you can take action faster.
· Improve communication: Greater visibility and faster response times help keep customers informed about shipping status, including what is happening now and what the future may hold. This is especially important during supply chain disruptions when proactive communication is essential to set customer expectations and help ease any potential concerns. Proactive communication can also set these expectations before issues occur, so customers know in advance what will happen next.
7 steps to creating a resiliency and optimization playbook
Take these steps to improve resiliency planning and optimize logistics:
1. Assess your current state: Start with the basics: Determine the health of your existing resiliency plan, if you have one. To help ensure service consistency, does it address current needs while anticipating future ones?
2. Identify risks: Map and prioritize the greatest risk points in your supply chain network. How do these risks impact your operations?
3. Develop mitigation strategies: Create an “emergency manual” to help address the risks you’ve identified. Define who’s doing what — how they will communicate — and how those actions will be synchronized.
4. Build your prescriptive analytics foundation: Analyze historical shipping data to gain greater supply chain visibility. Use these insights to better understand how disruptions have the potential to impact operations.
5. Create further insights with predictive analytics: Improve forecasting capabilities by layering in predictive modeling. Armed with this intelligence, you’ll be in a better position to plan ahead to help minimize or prevent future disruptions.
6. Define specific actions: Based on the insights gleaned from the two-step data analytics approach, create clear protocols to help mitigate issues as they arise — while proactively addressing future ones.
7. Maintain and improve your plan: Periodically stress test your plan to help ensure it scales with your organization. As your plan evolves, train team members to keep them up to date. Continuously optimize your decision making using prescriptive analytics for current operations and predictive analytics for future planning.
To help design and implement your optimization playbook, consider engaging logistics experts with deep experience in your industry. Look for an organization that can meet you where you are on your optimization journey and can provide local support.
To maximize the potential value of data analytics, choose a logistics expert that can provide near real-time data integration via APIs or webhooks. Most importantly, this organization needs the expertise to turn raw data into actionable insights that can help improve your supply chain performance.
Resiliency is about much more than weathering the storm — it’s about predicting and preparing for it. The future belongs to organizations that know how to optimize decision making when things are going smoothly, while making more informed choices when disruptions inevitably occur. It’s about prioritizing proactive planning over reactive responses.
This is the value of creating an optimization playbook and the key to building customer trust no matter what happens next across your supply chain network.
