In the time I’ve spent working at the intersection of supply chain and technology, I’ve found digital transformation projects heavily focused on the availability and quality of data – too often – at the cost of under-investing in other contributors to success.
Don’t get me wrong, data is critical.
Making high-quality data digitally accessible to the people and processes who need it, when they need it, creates a virtuous cycle that empowers teams, fuels growth, and increases resilience.
Using data effectively provides a massive competitive advantage. However, having the data on hand is only one part of the story.
Clearly defining the problem and subsequently driving the change required to maximize the value created with the data is another, bigger part of the story.
Technology is not a magic bullet.
Data alone cannot deliver the value organizations seek. It is a force multiplier that can deliver outsized results by magnifying the efficiency and effectiveness of well-defined processes and teams, but can’t act as a proxy for them.
Here are three steps to maximizing the value of supply chain technology.
1. Define measurable outcomes
Outcomes establish focus by clearly identifying the desired change.
Without the focus created through measurable and time-bound outcomes, organizations are going to waste energy, time, and money pursuing avenues that don’t contribute to the value they want to create. Ultimately, the lack of focus is going to leave teams deflated, and they’re going to regress back to the old way of working.
The goal of any business is to make money.
In that respect, the metrics that matter are revenue, margins, and cash flow.
The outcomes defined in your business case for supply chain technology investments need to have line-of-sight to one or more of these metrics to prevent your teams from chasing functional efficiencies at the cost of not improving the effectiveness of the company as a whole.
This line-of-sight acts as a forcing function for your company to evaluate the benefits of these investments holistically.
For example, transportation may focus on freight rates and capacity utilization; procurement may focus on material acquisition costs and payment terms. Tracking these functional targets in siloes affects the velocity of product flow through the network and impacts other functions like production, warehousing, and distribution.
I don’t mean functional metrics aren’t important or shouldn’t be measured.
They are, and they should.
But revenue, margin, and cash flow are the only ones that ultimately matter when measuring an investment’s impact to the health of your business.
I recommend taking the time to assess the tradeoffs between functional metrics. Create outcomes that find the right balance between responsiveness, i.e., the velocity of product flow, and efficiency, i.e., the cost of product flow, that creates the most value for your customer.
2. Improve current systems
By systems, I don’t mean technology.
I mean the processes tasked with achieving your desired outcomes. While outcomes drive focus by defining the ‘what’, systems establish direction by defining the ‘how’.
Setting up systems to effectively utilize available data is a major contributor to the success of digital transformation projects. Can your systems leverage data to enable workflow efficiency? Ask better questions? Improve decision velocity? Drive continuous improvement?
Identifying and eliminating bottlenecks in existing systems is an essential step towards unlocking the potential of high-quality data.
Here are few things to consider:
Understand how new solutions will integrate with your tech ecosystem.
When introducing a new solution, it’s important to understand how the solution will integrate with the other applications in your technology ecosystem.
How will the various solutions work together to achieve the desired outcomes? Expecting near real-time insights when dependent solutions only support batch updates isn’t realistic. Neither is expecting unified, global analytics without a robust master data strategy.
Start by defining the decisions that influence each outcome then work backwards to understand a) the questions that need to be answered to make those decisions; and b) the data needed to answer those questions.
Doing this will help you determine what master, transactional, and metadata is needed and where they sit; how the solutions in your tech ecosystem will bring the data together to surface insights in a cohesive and comprehensive way; and the required levels of data quality to drive decision clarity.
Once the insights required to inform decisions have been defined, invest in business process re-engineering to effectively integrate the new technology and adapt existing systems to the new way of working.
Establish a well-defined decision framework.
Creating a strong decision framework – framing the problem, asking the right questions, documenting assumptions – will equip your teams with the skills they need to make decisions using data as a guide, instead of assuming data will provide both the questions and the answers.
Random variability will result in unpredictability, causing good decisions to lead to bad outcomes and vice versa.
Leverage a strong decision framework and incorporate continuous improvement practices to fine-tune decision making, and avoid penalizing teams for good decisions which lead to unsatisfactory results.
I recommend Decisions Over Decimals by C. Frank, P. Magnone, and O. Netzer if you’re looking for a decision framework that balances data with experience and intuition.
Embrace a culture of continuous improvement.
I’ve found the best way to do this is by a) candidly communicating desired outcomes, team expectations, and the value created for your customer; and b) enabling teams to evaluate systems and identify opportunities to create better solutions.
Understanding the intent of the change – the value created for individual teams, and ultimately, for the customer – promotes divergent thinking and empowers team members to find ways to improve system effectiveness instead of simply trying to improve workflow efficiency.
Instituting procedures is helpful, but mandating changes though rules without communicating intent only results in compliance and won’t unlock the discretionary effort needed to build a culture of continuous improvement.
The goal is to do both.
Establish rules to reduce variability in process execution, and encourage dissent by creating a safe space for teams to call out inefficiencies in existing systems. This should be promoted at all stages of an engagement: planning, execution, and evaluation.
3. Drive lasting change
Organizational inertia makes it hard to establish new systems and drive adoption.
Sustaining the desired change is even harder.
There are several successful change management models like ADKAR, 7S, and Kotter’s 8 step model which roughly focus on the same building blocks for effective change:
- Aligning the change with company goals and values
- Promoting candid communication to deepen understanding and conviction
- Establishing a training and development program
- Leading by example through role-modeling
- Enabling positive reinforcement through well-defined systems and incentives.
Regardless of the model you choose, it’s important to realize driving lasting change is all about building and sustaining momentum.
It starts with people.
People make systems work, not the other way around. Empowering teams leads to effective processes which maximize the value of technology to drive growth.
Anything different creates a vicious cycle, where workflows degrade over time if not evaluated and continuously improved by teams who are committed to outcomes instead of simply complying with rules.
In the new year how will you work to maximize the value of the technology you rely on every day?