Predictive Analytics: Why It Matters in Service Supply Chains

Today, the term “predictive” gets thrown around a lot, but what does it actually mean for the Service Supply Chain (SSC)? Afterall, you cannot materialize information out of thin air, even with the help of powerful tools like AI. How do you work within the confines of reality, using the data you have, to improve your operation with “predictive” analytics?

Baxter’s Vice President of Solutions Strategy, Jeff Nieze, recently did a podcast with Service Council to decode this very topic.

Hindsight Is 20/20… If You Have the Right Perspective 

Still relying on historical data to predict your supply demand? You are not alone. Over fifty percent of SSC leaders say they do too, according to a recent Service Council report. Until we can predict part failure with complete accuracy, the SSC industry needs to rely on historical data.

That said, the key to leveraging your existing data so it gives you an accurate prediction comes down to a few factors: 

Data accuracy: Is your data clean enough to provide value?

Supplemental information: install base, service contracts, technician skills/tools, equipment usage, causal indicators

Not what, but how and where: Once you have determined whether to stock a part, deciding how to procure and transport it, as well as where to stock it, is equally critical.

These three initiatives can pose considerable challenges. There is a reason SSC organizations lean on historical data: they have lots of it, coming from various sources. SSC leaders face the challenge of sifting through massive data lakes to determine which pieces of information provide value. Manual data entry further muddles this process, as it’s associated with human errors, inconsistent data standards, and a lack of data governance. 

Due to data silos, companies also struggle to access and weave together the data available to them across disparate buckets. To augment your historical data, you must have the tools to synthesize information about your install base, service contracts, technician skills/tools, equipment usage, causal indicators, and any other pertinent bits to help provide context and illustrate the most meaningful points. 

A bare-bones data story can usually help clue you into whether you should stock a part, but a more comprehensive narrative will guide your strategic stocking strategy. How can you organize your inventory across your network to meet SLAs in the most cost-effective way possible? This concept is especially impactful when it comes to Spare Parts Planning (SPP), as it has to account for more rapid change and dynamic variables. While it takes more thought and effort to optimize your SPP, it can transform your business: 

“BMW’s average profit margin on vehicles manufactured is between twelve and fourteen percent; Service Parts accounts for sixty-five percent.” – John Carroll 

Until we reach a point where we no longer need parts to complete a repair, service parts remain critical for ensuring optimal first-time fix rates and customer satisfaction. It is not just enough to have a ‘plan’ to have the right part in the right place at the right time and at the right cost, it is making sure you execute to it.

To maximize the opportunity around SPP, you must get creative and employ tools like AI and machine learning to create the best plan and predict potential pitfalls in your execution.

Where Are You in Your Predictive Analytics Journey? 

Buzzwords like “digital transformation,” “AI,” and “machine learning” can overwhelm organizations when they really need to take an honest look at their current progress. Where are you on your journey? Are you working to transition away from spreadsheets, integrate your digital tools, or disseminate information in a better way across your organization?

With the help of the right partner, simple, yet focused, improvements to your existing process can yield massive results. Our customer Beckman Coulter saw a twenty-percent improvement in their first-time fix rate after we worked with them to optimize technician trunk stock.

Once companies have implemented the tools they need to move away from manual processes, it is time for them to focus on how well their employees and partners can access the data they have honed. Here is where another buzzword comes into play: “visibility.” It has so much buzz that some companies wonder if they are too focused on it. 

We say “no,” it is not a waste of time to focus on visibility, as it is a precursor to predictability. You cannot predict what you cannot see. But do not stop there. You must cross the bridge from visibility to execution. For example, good visibility helps technicians locate the parts they need in real time; it does not always tell them the most strategic stocking location to source from or how to best get it where it needs to go.

One way is to incorporate demand sourcing logic into your order process to automatically source a part order from the optimal location. Getting apart from the nearest location only to replenish it from a DC later or, at worst, miss a demand for a customer at that location is inefficient and expensive.

It makes more sense to evaluate inventory at all locations and source from a location with excess while still meeting your customer’s SLA. Most companies attempt to manage these tasks manually, which adds unnecessary costs to the process and often leads to missed customer expectations. 

Extending Your Scope of Visibility

Most OEMs rely on partners to support their SSCs, but few have a way to track partner performance, which affects their ability to perform as well. Think of your operation in terms of steps and all the parties that impact each one. For example:

Your supply network: How do you get parts into your supply network and how much visibility do you have into your partners at this stage? Do you know enough about their operations to arrive at an accurate lead time? Many organizations produce fixed lead times based on estimates that do not align with reality, so they serve very little purpose in meeting customer expectations. The better and faster you understand if a partner-related delay will impact your goals, the easier you can remediate the issue and set new expectations with your customers. This principle also applies to internal and partner operations concerning replenishment and repair.

Outbound to the customer technician: Issues can arise at any point along your supply chain journey, including once the parts are on a truck en route to your customer technician. Do you have a way to keep track of what is going on with the players at this end of the journey? It takes a village to meet SLAs and keep your customers happy; members of this village include your customers themselves. Implement a way to stay on top of exceptions in case the person who is set to receive your delivery runs into an issue.

Bottom line: Having 360-degree visibility across your partner network that also drills into transaction-level insights, as well as a way to track partner KPIs, helps you develop more collaborative relationships. In doing so, you increase your resilience because you can respond to inevitable hiccups and communicate with your customers early on.

A Predictive Supply Chain Starts from the Top

Fostering a predictive supply chain involves your entire organization as well as your partner ecosystem, so it starts with forward-thinking executives. The SSC leaders who do make an effort to break free of traditions and embrace digital transformation will outpace their competition: Seventy-one percent of supply chain leaders say they do not have predictive tools in place, according to a recent Service Council report. 

Based on what Baxter has learned from working with SSC leaders who strive to marshal their company’s predictive pursuits, we say it is key to let value be your guide. There are tons of technological solutions out there with shiny capabilities, but if those capabilities do not truly serve your overarching goals or integrate with your ecosystem, they are next to useless.

Start by developing a value roadmap that includes an honest assessment of where you are today and where you want to be. Consider your company’s big-picture, long-term goals and how your digital investments can scale to help achieve them. Improving today’s first-time fix rates is great, but how can you set yourself up to continue achieving performance indicators for years to come? Additionally, KPIs evolve over time and only give you insight to pieces of the puzzle, not the entire picture. 

Consider the driving mission of your company as well as that of your customers. If a technician who works on critical-care medical tools cannot access a key part when they need it, the real issue is not that they have failed to meet SLAs, it is that someone’s life saving treatment may be interrupted.

This anecdote illustrates the kind of perspective that leads to positive outcomes while investing in a predictive service supply chain. Think big to think ahead. 

Interested in learning more on this topic? Check out the full recording of the podcast here. 