For asset-intensive businesses, seeing truly is believing. When parts are critical to output, observability cannot be taken for granted – especially as processes and solutions become more digitalised. And when you add in that business processes sit on top of more complex cyber-physical systems (CPS), the need to embrace more well-established monitoring approaches is pressing, but it isn’t enough. New approaches are needed to address current and future complexities.
What does that look like? The most effective is an observability maturity model, a topic we covered in great depth in this whitepaper. These models create frameworks that help systems create their own definitions of observability. While the model’s name puts a great emphasis on visibility, it’s so much more than that. For warehouses and supply chains, it provides power to see and then use business-aligned insights to be proactive rather than reactive.
However, building an observability model relevant to your company’s needs requires strategic thinking. Here, we’ll explore the importance of an observability maturity model, and what is needed to create one that maximises your transparency and effectiveness.
The importance of observability maturity models
Observability is not merely about visibility into what has occurred within a supply chain – it is also about leveraging past events to anticipate and shape what may happen in the future. By incorporating predictive models and fostering proactive decision-making, observability maturity models enable asset-intensive organisations to establish strategic, efficient processes that are interconnected and forward-thinking.
Well-constructed observability models emphasise three interconnected elements that guide organisations in achieving proactive insights:
Insight - Structuring data for strategic visibility
Core to any observability model, insight structures raw data in a way that provides business leaders with better visibility. How that insight is achieved breaks down into four levels:
- Level 1: Telemetry identifies and collects raw asset data
- Level 2: Asset observability aggregates data and transform it into actionable metrics
- Level 3: Process observability empowers businesses with better visibility into their own workflows
- Level 4: Unified observability brings all process insights together to provide organisations a holistic view of their impact
As businesses address their workflows at each of these levels, the picture can come into better focus. When isolated metrics bring context to any system-wide trends, they can monitor what they’re doing and act more decisively and accurately.
Causality
With improved insight, companies get a clearer picture of the ‘how’ of their workflows. Causality allows business leaders to take that information and better understand why their systems behave the way they do and connect dots at these four levels:
- Level 1: Basic monitoring looks at data to see what’s happening currently
- Level 2: Passive observability offers a look into a system’s past and current operations
- Level 3: Causal observability automates the identification of cause-and-effect correlations
- Level 4: Proactive observability outfits organisations with predictive metrics so they can address any potential upcoming issues
Causality transforms observability into a proactive tool, enabling businesses to anticipate and address delays, backlogs and inefficiencies before they escalate.
Quality
Observability lives and breathes on data. The stronger information is, the more reliable a company’s monitoring strategy. Quality strengthens observability maturity through:
- Level 1: Accessible data is measured for consistency and timeliness
- Level 2: Refined data brings more relevant measurements to the forefront so companies can remain focused on the insights and needs that matter to them
- Level 3: Consistent formatting establishes a standard for data to be compared across multiple systems
- Level 4: Once formatting is established, the way data is segmented and defined needs to be addressed. Standardised ontology can create that consistency so there’s no misunderstanding on what quality data looks like and monitoring gets simpler
Ensuring high-quality data at every level involves removing noise and irrelevant information, which helps predictive models operate with greater accuracy. By discarding unnecessary data, organisations can not only enhance model precision but also manage large data volumes more efficiently, reducing system strain.
A partner that helps master observability
Observability is universal to asset-intensive businesses. But what warehousing and supply providers do with their findings is unique to their needs and objectives. A unified observability approach brings that information under one umbrella and analyses how a business has operated and what’s possible in the future. Through the interplay of insight, causality and quality, observability enables organisations to transform historical data into a predictive framework. This empowers them to foresee challenges, seize opportunities and build resilient supply chain processes that evolve with their needs.
At Endava, we are an experienced technology services expert and consultant that helps build an observability model with those goals in mind. Our industry, technology and business experts bring their knowledge to the table, creating solutions that keep your company’s trajectory priorities in mind. Visit our supply chain homepage to learn more.
Want to dive deeper into this subject? Sign up to download our observability for asset-intensive companies whitepaper here.