If you lead, work within, or support an aspect of the supply chain, you know the value of forward-looking analytics.
Where challenges vary are in the details ─ the time required to obtain the right data and develop an insight, make the process repeatable, measure and communicate results, then find time to iterate while testing new ideas for additional use cases.
Whether you work in the business, or as an analyst or data scientist, your job has been pressure tested since the early days of 2020. Hopefully, your organization’s analytics kept pace with customer demand throughout the pandemic.
Even as global supply chains experience periodic disruption, Gartner reports CEOs are bullish on economic recovery and prioritizing growth for the remainder of the year and into 2022. As you might expect, to achieve this goal, CEOs plan first to invest in Artificial Intelligence (AI).
Implications for Supply Chain?
Supply chains bruised by COVID seem unlikely growth engine candidates, but Accenture research discovered that 10% of companies achieve 13% financial gains over peers through “customer-centric” supply chain practices. Among the unique features of these “masters” is their approach to analytics:
“Analytics enables a company to look at all dimensions of its products, customers, and channels to understand how to segment customers by common characteristics and needs — and then configure the right supply chain activities to meet those needs.”
Traditionally, you expect machine learning applied to activities such as demand forecasting to provide CEOs the bump in performance they are looking for; up to a 2% lift in sales and .5% improvement in margin.
Those figures, however, are quoted from a time when sudden disruption was the exception, not the rule, and today are not guaranteed if the approach lacks a complete view of the customer. As McKinsey recently wrote:
“In a few short months (of COVID), consumer purchasing habits, activities, and digital behavior changed dramatically, making pre-existing consumer research, forecasts, and predictive models obsolete. Moreover, as organizations scrambled to understand these changing patterns, they discovered little of use in their internal data. Meanwhile, a wealth of external data could — and still can — help organizations plan and respond at a granular level.”
With few exceptions, customer-centric supply chains leverage external data to see through the eyes of their customers to uniquely deliver on their brand, service, and experience promises. There are just too many potential indicators of customer needs, wants, and demands worth consideration. Now, unexpected disruptive events in the supply chain and constantly shifting consumer behaviors make external data essential just to maintain a competitive pulse on demand.
Unfortunately, in the pecking order of prioritization, sorting out core internal data issues wins over external data opportunities. An overall data strategy is a barrier for most organizations: “data is one of the biggest challenges that executives face in building intelligent customer-centric supply chains.”
No matter, CEOs and their boards have already seen or been told by others about information described in this post about the $15 trillion economic value potential McKinsey estimates for AI across all industries, business functions, and use cases. The supply chain will be in the crosshairs for examination, if not already.
Cloud, Access, Variety to the Rescue
External data from sources such as syndicated providers and retail point-of-sale have long been staples in the CPG industry, for example, and used in trade promotion and shopper marketing analytics as well as consumer and customer insights.
Whichever segment you work in, as you expand the lens to consider potential data sources that might improve analytical outcomes, hurdles start to accumulate; different providers requiring unique contractual terms, how to technically access, obtain, and manage the data relative to your internal data, and how your people perform the work while also institutionalizing the capability. The cost, time, and complexity become prohibitive with too many one-off relationships.
This is one reason 90% of supply chains are not partaking in the growth benefits of customer-centric practices. There are many factors in play of course ─ not all data and technology related ─ but certainly at the foundation is knowing your customer at a deeper level and aligning supply chain processes accordingly.
“Data scientists can enrich models with seamless access to almost-unlimited data on any topic, including real-time and evolving circumstances.”
─“Top 6 Data Science and Analytics Trends for 2021”, Snowflake
Snowflake’s Data Marketplace (shown below) is a handy resource for those seeking the growth benefits of a customer-centric supply chain.
A recent article by Harvard Business Review articulates how Snowflake customer, Kraft Heinz, is able to utilize these data sets.
“Snowflake Data Cloud has easy access to external data sets that are hosted on Snowflake’s platform. Kraft Heinz has been using Covid-19 data from Johns Hopkins University, allowing it to see instantly which areas of its business are most impacted. It uses this data to build predictive models that allow it to get products into the supply chain exactly where (and when) they’re needed, ensuring its partners and customers can keep supermarket shelves stocked.”
Removing the cost and complexity barriers to multiple data providers ─ in the same cloud space as other enterprise data ─ makes it easier to experiment with different factors influencing demand even before customers express intent. There are many possibilities, from weather, demographics, mobility, census, travel, traffic, construction, legal, and much more.
CEOs seeking growth should sponsor the exploration of supply chain analytics using external data sources for customer centric signals that foretell shocks to the system. Those same signals can lend precision to insights into customer needs and wants and become reflected in processes throughout the company ─ not only within the supply chain ─ but in marketing, product development, and service to collectively improve a customer experience that leads to growth.
A key benefit of this relationship is the ability to leverage the scale and performance of Snowflake in combination with the ease of use of Alteryx to examine a larger portfolio of harder problems that yield more business value.