As a supply chain leader, you increasingly find yourself pulled into making IT recommendations and decisions. Thanks to rapid moves toward Industry 4.0, technology and data have come to play an integral part of supply chain management. The ability to collect and respond to data has become especially important, and it's not uncommon for supply chain managers to struggle with how to handle the sheer volume of data, let alone leverage it effectively. The key is to integrate advanced analytics throughout the supply chain, an approach that has multiple key benefits.
1. Capacity for True E2E Supply Chain Visibility
True end-to-end (E2E) supply chain visibility requires the sort of transparency that comes only with advanced analytics. When advanced analytics are shared and integrated across the supply chain in real time, managers can make more informed decisions every step of the way. Furthermore, because advanced analytics also allow robust data visualization, supply chain leaders can manage vast amounts of data more effectively — and that enhanced decision making leads to a better bottom line.
"Advanced analytics is characterized by simplified integration processes."
Meanwhile, as we enter the era of Industry 4.0, organizations are looking beyond their own walls and boundaries. Advanced analytics also allow organizations to extend E2E visibility upstream to suppliers and downstream to end users. As advanced analytics are shared beyond the boundaries of your organization, the supply chain achieves a new level of E2E visibility.
2. Ability to Optimize Manual vs. Automatic Decision-Making and Execution
Processes in the optimized supply chain generally rely on a carefully balanced combination of automation and human intervention, both in terms of decision-making and execution. In some cases, such as selecting a supplier, human judgment and execution are preferable, with advanced analytics to support decision making. However, in a high-tech environment where products are configured to order, automated decision-making and execution often will yield the best results.
Advanced analytics allow supply chain leaders to take advantage of the complete continuums from human judgment to advanced analytics and from manual to automated execution. Without advanced analytics, the machine learning that enables full automation is impossible.
3. Increased Speed to Market
A supply chain backed by advanced analytics is characterized by simplified integration processes that remove bottlenecks and eliminate duplicative data modeling. Furthermore, advanced analytics mean that real-time or near real-time data availability becomes the rule, rather than the exception. The natural result: increased speed to market. As the supply chain "learns" how to predict and respond to problems more quickly, modification and delivery times shorten. Meanwhile, advanced analytics enable supply chain managers to identify issues and replicate successful deployments across different parts of the organization.
"Advanced analytics offer greater production control across the supply chain and beyond."
Increased speed to market is especially important given the industry's move toward additive manufacturing. More commonly called 3D printing, additive manufacturing is a supply chain trend that's here to stay because it allows incredibly rapid customization. Advanced analytics are necessary to truly scale any additive manufacturing enterprise because they offer the foundation for proactive processes that shorten lead time and resolve problems faster — thus accelerating successful product launches.
4. Support for a Hybrid "Agile-Lean" Model
For years supply chain managers strived for an agile supply chain, one that could adapt to sudden changes in the supply of raw materials, shifts in product demand and everything in between. While that model certainly has advantages, supply chain leaders realized that the agile approach is simply not a silver bullet or one-size-fits-all solution to all manufacturing challenges. The industry has since embraced the idea of a lean supply chain, free of redundant processes. But that approach, too, has its limitations: for example, eliminating all redundancies can also eliminate important systematic checks and balances.
An ideal model blends agile and lean mindsets, resulting in a supply chain that can quickly adapt to changing conditions while also incorporating adequate (but not excessive) checks and balances for critical manufacturing processes. Advanced analytics are a vital underpinning for this model. They bring the predictive functionality necessary for supply chain agility, while also supporting process simplification. Furthermore, advanced analytics can be used to provide their own checks and balances, often with much more limited involvement from humans.
5. Lower Inventory Costs and Higher Revenue
Perhaps the most compelling advantage of advanced analytics is that they improve your organization's bottom line. According to recent Gartner surveys, companies with a more mature use of advanced analytics also often report lower costs and higher revenues. This is because advanced analytics directly improve demand capacity management by allowing real-time insights into the demand/capacity curve. Additionally, predictive insights provided by advanced analytics enable more accurate and consistent forecasting across the organization, generally leading to more effective sales and operations planning (S&OP) processes.
While these functions are easy to measure and directly relate to cost and revenue, advanced analytics can improve revenue in more subjective ways. One example is improved customer service. As advanced analytics eliminate problems from the supply chain, customers enjoy more reliable and consistent delivery times and product quality. And finally, advanced analytics enables additive manufacturing, which generally yields higher margins and faster speed to market.
Ultimately advanced analytics offer greater production control across the supply chain and beyond. As the manufacturing industry evolves, they will undoubtedly become an integral part of every enterprise.