In fact, over 74% of S&P 500 companies mentioned the phrase “supply chain,” in their Q3, 2021 earnings report, up from 30% in Q3, 2019. Altogether, executives at these companies mentioned “supply chain” over 3,000 times in 2021.
And the reason for this was immediately apparent to anyone following the news: COVID-19 disruptions shifted consumer demand that outpaced supply – leading to higher container prices, congestion at the ports, and persistent out-of-stocks.
Now, as the bullwhip effect continues to work its way through the supply chain, traditional approaches, heavily reliant on human judgment, are no longer sufficient to address the complexities and uncertainties of modern supply chains.
Fortunately, advancements in data analytics have paved the way for more efficient and informed decision-making processes. Predictive and prescriptive analytics—analytics used to predict and optimize business strategy respectively— can unify the supply chain, enabling organizations to overcome critical challenges and achieve superior operational performance.
Here are three ways predictive and prescriptive analytics can unify your supply chain today:
1. Get a Holistic View of Your On-Shelf Availability
There were over 2 billion instances of a product being out of stock online at the height of the pandemic according to Adobe Analytics. Availability is especially impactful for eCommerce businesses, where it can mean a precipitous decline in organic ranking on marketplaces like Amazon and Walmart.
For brands taking an omnichannel approach, it can be difficult to stitch together availability across each channel. Each team might be making informed decisions in regards to the specific channel they manage – say, how to maintain availability in Walmart – but without a holistic brand, or product, level view to make larger, more strategic decisions, brands can easily get pulled in too many directions.
In a capital-constrained environment where brands need to be disciplined and make intelligent investment decisions, this can mean death by a thousand cuts.
Here’s how you should think about solving these issues:
Collect, connect, and visualize data from every channel
As we highlighted above, if each channel is making decisions without a holistic, omnichannel view, then your brand is flying blind.
Prioritize real-time data
eCommerce already provides fairly accurate real-time data but look into ways to bring that level of visibility into more channels. Retailers can increasingly help here, but brands should also consider shelf-monitoring sensors or AI-powered cameras that can unify availability metrics across channels.
Sophisticated demand forecasting can’t just use historical data
Forecasting historical sales data can’t prepare you for the unexpected. Sophisticated demand forecasting must take into account market trends, forward-looking consumer sentiment, external factors like weather and promotions, etc.
Orchestrate your Supply Network:
Using GraphML, teams can anticipate supply network risks, identify supply chain breakdowns and chart the next-best course of action to address on-shelf availability. The opportunity for brands to build a connected supply chain network with all suppliers and vendors relies on having in-depth data about on-shelf product availability across all retail stores and fulfillment centers. It’s then about orchestrating that data to optimize the larger supply network.
2. Gather Data Beyond Tier 1 Suppliers
The term "Tier N supplier" refers to suppliers that are further down the supply chain hierarchy beyond the primary, or Tier 1, suppliers. These might include the suppliers of Tier 1 suppliers, as well as subsequent tiers of suppliers in the extended supply chain network.
The challenge with Tier N suppliers is that they may have less visibility and control compared to Tier 1 suppliers. It becomes increasingly difficult to gather data and information from these suppliers. For example, in 2017 Elm Analytics estimated that while there were around 5,000 Tier 1 automotive suppliers in the U.S., the total number of U.S. suppliers including Tier N was over 285,000.
However, as supply chain resilience and risk management become more critical, organizations are recognizing the importance of gaining visibility and insights into their Tier N suppliers to enhance overall supply chain performance and mitigate risks.
Here are some critical ways to use data to strategically evaluate your supply chain:
Audit your entire supply chain
If you don’t know where a top supplier is sourcing a key material, you not only don’t understand your compliance risks, but you’ll be caught flat-footed when disruptions inevitably arise. But don’t stop with a simple audit, collect and connect as much data as possible to create an evergreen resource.
Map your full supply chain to visualize strategic decision-making
Supply chain resilience is key in today’s economy. Create a source of truth to evaluate where you are overly reliant and where you might be spread thin.
Connect this data to external sources such as traffic, weather, etc.
One step beyond a simple map is to enrich that map with contextual data to get a real-time picture of vulnerabilities that can inform how you might reroute shipments or order in different quantities in different locations to mitigate weather or traffic disruptions.
Identify disruptions and unify your supplier network
Analyzing for risks and anomalies in the supply network based on external signals brings visibility to the cascading impact of the disruptions through your supply network. With a connected supply chain network that leverages graph technologies like GraphML, teams can define and execute the right actions to address component availability for manufacturing and assembly.
3. Collect, Connect and Communicate
I’ve said it a few times in this article, but if you don’t collect and connect diverse data sources, you’ll get fragmented solutions.
Supply chain journeys involve multiple stakeholders, each using disparate systems that do not communicate with each other effectively. This siloed approach leads to data getting lost in the shuffle, hindering decision-making and limiting insights. By unifying data from various stakeholders and systems, organizations can bridge the gaps and enable better-informed decisions.
This requires a fundamental change in the way internal stakeholders communicate, fostering collaboration and information sharing. Improved data accessibility and transparency empower stakeholders to make data-driven decisions that optimize the supply chain's performance.
Predict, Don’t React
The short-term view looks at today’s supply chain environment and says, “Container prices are lower, I can afford to focus on other things.” But this is the wrong approach.
Now is the time to use insights and analytics to unlock greater productivity, efficient allocation of resources, better in-stock rates and strategic decision-making BEFORE issues arise. The glut of inventory sitting in warehouses throughout 2022 and 2023 shows that many organizations didn’t really learn their lesson during the height of the shipping crisis, merely reacting to demand as it arose. Connect ALL your data and use predictive analytics to get tighter inventory turns, more accurate forecasting, and a better view of risk.