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What to Know: Gartner Supply Chain Planning Summit Takeaways
5 minute read
5 minute read
As businesses pour significant resources into digital transformation, the pressure to deliver tangible results intensifies. Yet, many leaders find it challenging to justify their spending, especially when it comes to implementing new systems.
According to PwC’s 2024 Digital Trends in Operations Survey, 69% of operations and supply chain officers say tech investments haven’t fully delivered expected results. Digital transformations are complex initiatives, and a variety of factors contribute to the difficulty in achieving a solid ROI.
In this article, we draw on insights from the Catena Solutions team to explore three reasons why technology initiatives may falter: lack of data strategy, inadequate implementation, and unrealistic expectations.
Data is the first major hurdle to system success.
Advanced technology, especially ones that use artificial intelligence (AI) and machine learning (ML), rely heavily on accurate historical data to produce valuable results.
However, in industries like food and beverage and CPG, collecting the right data is tricky for a variety of reasons:
“The end game for organizations when implementing new systems is to gain data-driven insight, but data-driven insight is pointless if it’s based off bad data,” said Stacy Johnson, Consultant Engagement Director, Supply Chain Practice.
For organizations to get the most out of new systems, they need to start by ensuring they have clean, organized, and accessible data. The challenge, however, is companies often find that even if they have useful data, it’s isolated in silos throughout the organization.
In fact, research into data issues for retail and CPG companies discovered that the top data challenge is breaking down silos. Ninety-six percent of survey respondents said breaking down silos is either a “very significant challenge” (63.5%) or a “somewhat significant challenge” (32.5%).
“This is where a data strategy comes into play,” said Geoff Olsen, Leader of the Supply Chain Practice. “Even if you have good data, it might not be the right data in the right location. You need to start with the end in mind, know what results you need insights into, and align your data strategy with that goal.”
“The end game for organizations when implementing new systems is to gain data-driven insight, but data-driven insight is pointless if it’s based off bad data.”
Stacy Johnson, Consultant Engagement Director, Supply Chain Practice
While new technology systems have endless possibilities, a lack of understanding by the humans interacting with these systems also renders them useless.
“What we’re seeing happen is companies spend a lot of money on a new tool, but then employees don’t use it,” said Johnson. “Usually this happens because the company didn’t have the right change management in place when introducing the technology.”
When it comes to change management, when’s the right time to integrate it into a project? From the beginning, research shows.
Prosci found that projects where change management initiatives are started early in the lifecycle are more likely to meet or exceed objectives than projects that bring it in later on. The consequences of starting change management too late include:
“The reluctance from employees and disappointment from project stakeholders comes when employees either can’t or won’t use a new tool to the best of its ability,” said Hannah Ennessy, Consultant Engagement Director, Human Capital Practice. “No matter how robust and helpful a system is, it’s not going to lead to a ROI if you implement it without change management for the entire duration of the project.”
Another consideration, especially in the food and beverage industry, is the nature of organizational setup. For example, many companies have corporate offices and plant locations. This creates additional challenges because employee skill levels and use cases will vary throughout the company.
“Training and communication should be adapted to the employee population at each location,” said Ennessy. “Someone sitting in a corporate planning department is going to have a completely different experience with a tool than a manager in a plant. Bringing in change professionals who can bridge the gap while upholding culture and morale will be crucial to making sure a project gets to the finish line.”
Lastly, having unrealistic expectations of new technology is bound to lead to disappointment among stakeholders.
“New systems, especially ones that use AI or predictive analytics, are meant to make you work more efficiently, but you’re still going to be doing work,” said Rich Medrano, Leader of the Revenue Growth Excellence Practice. “Systems may take over mundane tasks, but companies still need people to ensure data is usable and analyze system outputs. Companies that find technology isn’t producing helpful results may not understand that humans still play a key role.”
The stats back this up. A World Economic Forum report predicts that AI may replace around 85 million jobs by 2025, but 97 million new jobs will be created. The new jobs will be “more adapted to the new division of labor between humans, machines, and algorithms,” and will require skills that display critical thinking, analysis, and problem solving.
Another mismatch of expectations versus reality is that organizations often lack the experts needed to get the most out of new systems. Companies that try to save time and money by having their internal team lead product implementation will encounter various hurdles:
“New technology systems are not plug and play,” said Christine Bart, Senior Consultant Engagement Manager, Revenue Growth Excellence Practice. “Very rarely do companies have the internal talent on staff that can set the tool up and ensure it has the functionality needed. Another expectation check is ROI. Research shows it can take up to a year or two to see ROI on new systems, but leaders expect to be saving money and seeing greater efficiency right away, which is highly unlikely to happen.”
“Systems may take over mundane tasks, but companies still need people to ensure data is usable and analyze system outputs. Companies that find technology isn’t producing helpful results may not understand that humans still play a key role.”
Rich Medrano, Leader of the Revenue Growth Excellence Practice
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