Supply chains are made up of so many interconnected moving parts that, even if the chain as a whole is functioning, there are always improvements available to make business outcomes a little better. Or a lot better. Here are four common areas for supply chain improvement organizations can achieve through data analytics.
Finding Better Ways to Manage Inventory
Managing inventory means constantly weighing the costs—mostly warehouse space and its associated expenses—with the benefits, like the ability to ship products to customers as soon as possible rather than having to wait on backorders.
But, as The Balance writes, “Inventory holding costs are a silent supply chain killer.”
The ideal balance involves stocking enough product at a time to satisfy immediate customer orders, but not enough to rack up extra inventory holding costs—or leave you with a surplus of leftover merchandise.
Improving supply chain analytics is helping organizations optimize their approaches to inventory management, among other developments. Rolls-Royce is using data analytics from ThoughtSpot to give project managers, supply chain analysts and buyers the ability to match up excess spare parts the company has sitting around with sales opportunities. This helps them reduce inventory excess while increasing revenues—made possible by speedier and more accessible analytics for key decision-makers.
Manufacturers do everything they can to ensure products come out right the first time around. Recalls are expensive and can cause long-lasting damage to any brand. But some slip-ups are inevitable, and supply chain participants need a way to identify product issues and trace products back to their origins.
Data analytics and artificial intelligence technology are boosting traceability through the supply chain, helping companies figure out which products need recalling and why—as soon as possible. Today’s supply chain is rife with Internet of Things (IoT) technology, all generating useful data about products and processes.
The food industry supply chain is reckoning with traceability in the face of recalls. As Food Safety Magazine notes, there’s data from shipping docks, retail distribution centers, post-harvest handling, carriers, etc. Analytics is useful in ramping up traceability because it can help identify “patterns that affect quality, profitability and safety.” When companies can bring together disparate data to understand the product path from point A to point Z, they can identify problem areas and work to fix them, reducing the chances of future recalls and miscommunications.
Reducing Order-to-Cycle Delivery Times
Delivery cycles are make-or-break in the supply chain industry. Part of what dictates whether a company remains competitive within its niche is the time between receiving a customer order and having the order shipped. And customer demand is changing, as people push for faster delivery options, often considering a day or two the new “standard.”
As Computerworld cites, embedding data analytics into supply chain operations can lead to a 4.25x improvement in order-to-cycle delivery times—in part because it can reduce the amount of time it takes organizations to identify supply chain issues and implement constructive solutions.
Savvy supply chain operations can identify and reduce risk before disaster strikes, rather than having to learn from past mistakes. AI-driven analytics can help in this arena. According to UPS, applying mathematical models and analytics tools—including machine-learning algorithms that keep improving over time—companies and their logistics partners “can glean more insights to better manage risk.”
These algorithms can dive deep into data, picking out patterns in seconds pertaining to disruptions, delays, failures and even natural disasters. The more information decision-makers in supply chains have, the better they can plan to prevent and respond effectively to these ever-present risks.
There are many ways in which businesses can use data to improve the supply chain, but inventory, delivery cycle timing, risk and traceability are great jumping-off points.