Demand forecasting is one of the most valuable planning tools available to manufacturers – when it is done accurately. Manufacturers that can correctly predict and prepare for what lies ahead are able to ensure their supply chains run smoothly by securing raw materials at optimal prices, meeting demand and avoiding shortages or excess inventory.
Business intelligence enables companies to predict the consequences of unavoidable economic volatility and unplanned events that impact supply chains. However, executives are struggling to effectively connect the insights derived by traditional business analysis to the decisions it is intended to assist. As a result, companies do not act on the insights produced by their own data teams.
To capitalize on the real-time, actionable insights available today, manufacturers must update their business intelligence approach.
Alignment from the start is essential.
An effective business intelligence approach requires that data teams and executives are aligned on big picture goals from the start. While it seems like an obvious first step, failing to ask the right questions can lead to a vicious cycle of organizations not having enough time or resources to tackle the problems manufacturing executives need to address. Since traditional analytics processes have difficulty delivering insights at the speed most executives need, it is critical that data teams ask the right questions upfront to avoid wasting time fulfilling ad hoc, point-in-time requests that cannot be leveraged for the future.
Machine learning identifies the factors driving supply chain performance.
Once data teams and executives are on the same page, machine learning and cloud computing can be leveraged to enhance analytics, making the data to decision process much more efficient.
Machine learning facilitates the mining and analysis of hundreds of internal and external supply chain drivers, which is key to making smarter decisions and gaining a competitive edge. Since economic volatility and changing consumer behaviors are top concerns for supply chain leaders, according to a PwC study, smart manufacturers must leverage machine learning to help predict and prepare for these unplanned events accordingly. Unfortunately, many manufacturers continue to rely on traditional demand forecasting techniques that are based on internal and historical data, keeping them focused on the past. In fact, only 20 percent of work produced by data teams focuses on foresight and truly predictive insights.
Accurate predictions require manufacturers to step outside of their four walls.
In order to gain a holistic view of what truly drives supply chain performance, manufacturers must incorporate external information in their analytics and business intelligence processes. Luckily, machine learning can be used to identify the best external information to use, reducing the possible choices from millions to a few dozen. Armed with relevant information and greater efficiency, data teams can then spend more time dedicating their skills to deliver timely, future-focused insights that drive value.
Understanding the future is the future of business intelligence.
Companies may never gain 100 percent perfect foresight, but they can make more accurate predictions now than they ever could in the past by updating their business intelligence approach. By mimicking the methodology of industry experts, data scientists and economists, AI empowers manufacturers to efficiently and accurately determine the best data for their supply chains, for the time frame and the executive decision at hand.
Modernizing the data to decisions approach with the appropriate external information and AI advancements allows data teams to eliminate pain points in the analytics process and become more strategic.
By closing the data to decisions gap, manufacturers can accurately predict the future, which is key to gaining a competitive edge.
