Technologies like artificial intelligence (AI) and machine learning (ML) will continue to change the Service Supply Chain at an unprecedented rate, so what moves can you make now to capitalize on tomorrow’s opportunities?
Look at the common denominator of every technological advancement: data. Having robust, reliable data is already the bedrock of your service strategy, and this will only ring truer in years to come.
Modern Service Supply Chains
Since data cleanliness and ownership is vast and complex, we consulted some of our industry’s data experts and shared their wisdom in our recent whitepaper, Data’s Role in Digital Transformation for Modern Service Supply Chains.
We distilled this insight even further into a couple of blog posts. Part one of this series zooms in on the pitfalls of dirty data. The post you’re reading now will share how to keep your data clean. For a comprehensive rundown on data cleanliness, data ownership, and how data drives digital transformation, download our whitepaper here.
Supply Chain Data Challenges
To use your data wisely, you need control over it. Not only do Service Supply Chains deal with tons of moving parts, they deal with lots of moving partners. Who are your collaborators and how many systems source and store your data?
Whether it’s the third-party logistics company (3PL) you work with or your customer relationship management (CRM) platform, multiple environments meet your data. This can lead to broader insights, but it can also get messy. You’ll want to maintain a single, centralized source of truth for your data, ensure your company has easy and complete access to it, and keep it separate from inaccurate or duplicate information.
Tips to protect the sanctity of your data:
- Run your data through an in-house system before it enters a third-party system.
- Keep a data trail for yourself and do not rely on your partners’ records of it.
- Know where your data is coming from and how it was collected.
- Collect your data firsthand from your customers rather than through a third-party system.
- Do not give unauthorized users access to your data.
Building a strong data governance policy is easier said than done, but luckily you can lean on things like automation. The right technology allows you to bake functions into your system that automatically stop invalid data at the door, as well as identify or improve inaccurate data. You can also rely on built-in checkpoints that do not allow certain fields to remain blank or that require validation.
How to Manage Supply Chain Data
Obsess Over Data Quality
In addition to shielding your data streams from pollutants, automation and technology can improve your entire business strategy. Most data problems result from business processes that were not designed to provide the level of data that companies need today. That’s why it’s a game changer to make every first move with data in mind.
In practice, this starts by having more control over your data. When you can segment materials, products, and sites within your data, you can implement clear and intelligent data groupings.
You’ll also want to rely on a system that can capture mandatory data attributes such as “manufacture date” for parts, as most IT systems only capture certain business processes. This causes problems when you retrospectively decide you need data that was not captured from the start.
With the rise of AI and ML, every industry will shift more towards a data-centric model. Get ahead of the curve by obsessing over data quality early and often, store it with longevity in mind, and make it easy to access. Systems for ingesting data may change, but this is a crucial area to have locked down for the coming years.
Field Service Operations Data
Every section of the Service Supply Chain world lives on quality data, but this can get especially nuanced for those of you in Field Service Management. Consider what pieces of information your technicians regularly debrief on.
To keep track of inventory levels, do they simply report which parts they used in any given day, week, or month? For the mature digital field service operation, capturing data about parts alone may not suffice.
We also suggest technicians keep track of which service calls and equipment the parts were associated with. Here’s why:
- A quality assurance group could use your debrief data to determine if a particular product or model has a quality issue causing failures.
- When you have a solid track record of which parts went to fix which issues, you can really make use of the expensive tools you may have invested in, such as those that apply ML to anticipate the parts technicians will need for future jobs.
- To rely on “as-maintained” bills of material to understand if customer equipment has been updated based on past maintenance, you need to have a clear idea of which parts went into this past maintenance.
- Effective Spare Parts Planning often involves insights into the current configurations of installed bases versus how they were originally installed so the right parts are available to support future service calls.
In short, organizations that strive for more sophisticated predictions need more robust and detailed data.
Optimize Your Service Parts Management with Data
To capture and make use of more detailed information, predictive Service Supply Chains need solutions that can analyze and report on data, as well as identify gaps within it. There are key pieces of data that enable a fully optimized Service Parts Plan, including:
- Part and product master information
- Installed base data
- Service bills of material
- Part chaining and supersession
Using a solution that combines data points such as the ones above helps you attain a holistic view of your service operations and allows you to get much closer to target stock levels. This leads to less excess inventory, which means less wasted cost for you.
Ready to learn more? Check out our full whitepaper, (Data’s Role in Digital Transformation for Modern Service Supply Chains), which is full of helpful details as well as real-life examples related to this topic.