Data Insight June 7, 2024

Apparel & Footwear Hire Analytics into Supply Chain. Here’s Why You Should Too

Delivering on your digital roadmap means bringing key digital capabilities, like analytics, into supply chain. As the apparel and footwear industry leads the charge on this, we zoom into how they're doing it and the benefits it can bring.

Greta O'Marah Avatar
Greta O'Marah
People

The Data

  1. 1

    Analytics talent and roles requiring more technical proficiency, such as data engineers or data scientists, are more likely to be hired into other functions rather than supply chain.

  2. 2

    However, apparel and footwear companies like Nike are more likely to hire analytics roles into supply chain when compared to other industries.

It’s Not Who Owns Data Powerhouse Teams but Who Should  

Analytics can greatly improve decision-making across an enterprise, which is why so many supply chain leaders are building this skill into their workforce. In fact, analytics is the most mentioned digital skill across supply chain job posts.  

Companies looking to accelerate this capability are particularly investing in dedicated roles for analytics, such as: 

  • Data Engineers, who build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret.   
  • Data Scientists, who apply statistical analysis, machine learning, and predictive modeling to extract insights and knowledge from structured and unstructured data.   
  • Business or Supply Chain Analysts, who interpret business data, often through statistical analysis, to provide actionable insights and recommendations to the business.   

But a key question arises: who within an organization should own these data powerhouse teams? 

In a Zero100 analysis, we found that there is a more robust analytics operation outside of the supply chain function rather than within it. And jobs requiring more technical proficiency, like data engineering or data science, are even more likely to be hired outside of supply chain. 

Dot chart showing density of job posts for technical jobs comparing outside of and inside supply chain by industry.
Source: Zero100 analysis of LinkedIn data

This isn’t surprising given that corporate IT has traditionally had a monopoly on data and analytics roles, and there can be synergistic benefits for keeping IT-focused roles close to other IT roles. However, this current setup presents risks for supply chain organizations looking to move quickly on AI. Without supply chain domain knowledge, crucial data is often overlooked and isn’t included in model training, sub-optimizing machine learning predictions or outputs from genAI tools.    

Some organizations are removing these risks by hiring dedicated IT talent that can both apply data and analytics skills and understand the supply chain operating landscape. One industry stands out here: apparel and footwear. Here, a greater density of analytics, insights, and business analytics roles are being hired into supply chain rather than outside of it.  

Nike Is Just Doing It 

A prime example is Nike, who are pulling analytics roles into supply chain to a greater degree than other functions. Zooming in one level deeper, more than 60% of these roles support work in demand planning and forecasting as well as logistics. It's paying off. Nike’s shift to a digital-first supply chain has tripled its capacity to serve consumers in North America and EMEA. 

Like Nike, supply chain leaders who ramp up hiring for analytics in their organizations will gain a fast-mover advantage on digital capabilities. 

The Takeaway  

If you’re contemplating your own analytics resources, take a quick pulse of the roles that exist both within and outside of supply chain today. Then, determine the work required to deliver your digital roadmap and what resource gaps might arise. For example, if AI/ML is a key component for success, consider the limitations of your data infrastructure and analytics framework and whether a dedicated analytics headcount could speed up execution. 

To see a different data cut or to dig deeper into this topic, reach out to our Head of Research Analytics, Cody Stack, at Cody.Stack@zero100.com.    

Methodology    

Zero100’s proprietary data and analytics are a combined effort between our data scientists and research analysts. We provide data-first insights matched with our own research-backed points of view and bring this analysis to life via real-world case examples being led by supply chain practitioners today.     

For this study, we looked at 122k LinkedIn job posts from 64 companies. Our analysis split specific job titles (eg, data engineer) into a supply chain or non-supply chain group and calculated the share of that role out of the total number of hires.