The Four Stages of Supply Chain Transformation

Digital transformation has become a central theme in the supply chain and logistics industry. Again and again, digitization and data are the most discussed topics at industry events, panels and networking conversations. Amongst the optimism and excitement, we are challenged to find transformation success stories.

For digital transformation to be successful, key foundational elements must be in place - specifically access to substantial data and data quality. Without this, advanced analytics and risk management decisions will fall short.

The Hackett Group provides a useful visual for describing the maturity stages that supply chain is moving through as the industry strives to make sense of their data and effectively leverage it for business intelligence.

The Supply Chain Maturity Model


The model describes the condition of data in each of the four stages, how it is handled and the supply chain and risk management questions it informs.  

  • Stage 1 - Descriptive Analytics (What happened?)
  • Stage 2 - Diagnostic Analytics (Why did it happen)
  • Stage 3 - Predictive Analytics (What will happen?)
  • Stage 4 - Prescriptive Analytics (How can we improve based on what happened?)

How does the Maturity Model help with Transformation?

At ClearMetal, we observed that a majority of companies are in Stage 1 or Stage 2. A small number within the industry are beginning to build towards Stage 3. To understand why this was, we talked to a multitude of supply chain executives and found that all of the failed initiatives stemmed from the same root cause; their data was undependable and full of holes - an issue that’s not new, but has continually hamstrung even the largest, most powerful companies.  

So, how do companies overcome this and mature into an analytics-driven business? Each stage of supply chain maturity confronts unique challenges in collecting, managing, and leveraging data to accomplish the business objectives. The first step is to understand those challenges and make a strategic plan to overcome them one by one.

Data Challenges by Stage

STAGE 1: Consistent Data Collection

In the first maturity stage, Descriptive Analytics, organizations determine what has happened in the supply chain.

The Challenge

Companies lack access to data and a way to consistently collect data. The data they do collect is often spread across internal silos, both known and unknown, as well as third party providers who limit access to maintain control. As a result, companies are uncertain of what data is collected and where it is stored.

The Solution

To overcome Stage 1 challenges, establishing an automated and consistent process to audit and manage the collection and storage of data is required. This will help in identifying what data you have, what’s missing, and where it’s stored - enabling teams to manually run their own analysis, track KPIs, and report on past activities.

STAGE 2: Data Canonicalization

The second stage, Diagnostic Analytics, allows organizations to analyze results and pinpoint why issues or inefficiencies occurred across functions, departments, and operations.

The Challenge

Supply Chains are a network of interdependencies. Analyzing data across functions requires there to be a common language in the data, a reference-able canon. An event in one function or system needs to have an equivalent, translation, or handling rule in another function or system to be understood. You can think of the canon as a data translator, without it systems have no way to speak to each other and make sense of the events that are happening.

The Solution

Currently, most companies do this by outsourcing data cleansing and ETL mappings to offshore teams who do it manually. A more effective way to canonicalize data is to use automation tools in tandem with any necessary manual data entry.  The canonicalized data will improve data accuracy, which in turn will improve the visualizations and analyses generated by the business intelligence tools and persistent dashboards that can now be effectively leveraged.

Stage 3: Data Validation

In Stage 3, Predictive Analytics, organizations begin to benefit from predictive intelligence and probabilistic decision making that help in designing agile supply chains. Predictive analytics learns the techniques of high-performing team member and turns those skills into algorithmic detection, insights, and alerting. It provides the most value to operations teams, allowing them to make proactive risk management decisions for scenarios that could occur in the future.

The Challenge

The level of data quality required to unlock Predictive Analytics is significantly higher than the previous two stages. In the previous stages, data could be entered or disregarded by team members who knew how to make calls on questionable or bad data based off experience and analysis.  For predictive analytics to work, those nuances must be captured in the algorithm that is fed by data.

The Solution

Validating data is key to accessing the value of predictive analytics which requires data that represents operational realities. Depending on the type of company, the data validation process will differ. Currently, companies that have started to implement predictive analytics are using manual data-cleansing processes to accumulate higher quality historical datasets to power algorithms or have begun experimenting with machine learning.

Stage 4: Real-Time Data Validation

Stage 4, Prescriptive Analytics, offers assisted decision making in current market conditions. It upholds the value of analytics but first understanding what happened, anticipating what will happen, and finally providing actionable insights and suggestions on what to do.

The Challenge

For companies that are not already prioritizing digitization and IT resourcing, this will be very difficult. Data science approaches to cleaning and instantly analyzing granular events at a global scale unlock access to the value of Prescriptive Analytics.

The Solution

Invest in validating your data. Software utilizing machine learning and AI are the most effective in validating and correcting incoming data in real-time. They are able to enrich it with additional contextual data, disregard invalid data, statistically fill in gaps of missing information, and manage the canonicalization of data across a network of partners. Reaching Stage 4 requires strategic planning and long term commitment.

Why Invest in Transformation?

The goal of transformation is to provide the best intel possible to make smart, effective decisions when designing, managing, and operating supply chains. At the highest level of maturity operations teams will be able to manage supply chain design, procurement, and transportation with precision and respond to changing market conditions in real-time.

Taking the long road and investing in your data today will propagate a high-performing supply chain that maintains its competitive edge and yield large dividend payoffs. That said, transformation is a long term journey and each step you take will uncover value for your org. The ClearMetal platform can help you at any stage within your journey with little, to no effort on your part through its sophisticated AI and machine learning capabilities. Learn more about us in our company overview.

For a more in-depth analysis of the Supply Chain Maturity Model, download our whitepaper.

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Nandini Nallasivan