Massive Excel spreadsheets. Customer complaints. Out-of-stock notifications. Backorders. Those were the analytics traditionally used to determine how much product to buy, make, and deliver. But managing today’s supply chain requires more diverse data, from multiple sources, to meet complex operational requirements and heightened consumer demand.
Predictive analytics methods encompass a robust, data-intensive, proactive approach to managing and delivering products to market. Such analytics are now being used at every point in the supply chain. Let’s take a look at what it takes to successfully produce and apply these results.
Machine Learning in Predictive Analytics Processes
Rather than requiring multiple sources of data, today’s predictive analytics processes make use of a machine learning approach that includes algorithms to analyze diverse datasets. In a recent Gallup study centered on predicting consumer demand, data was provided on NASDAQ, product and brand searches, underemployment, and standard-of-living indices. By combining these data sources, Gallup was able to create a predictive model that outperformed their client’s previous consumer demand model by more than 150%.
Machine learning tools offer a powerful way to combine and analyze massive amounts of data. These tools allow predictive analytics methods to be used not only for demand forecasts that can smooth out inventory levels but also for pricing and maintenance.
Machine learning tools can combine multiple data sources to provide improved pricing models, and using machine learning for predictive analytics provides a holistic approach to setting prices. Often, local business units or individual departments will set the price for products, limiting visibility throughout the company. Predictive analytics provides a company-wide view to tailor prices according to markets, geographic regions, and other variables. The result? Increased sales and profits.
Maintenance and operations are another area where machine learning and predictive analytics go hand in hand. While sensors and the Internet of Things (IoT) provide critical information for preventive maintenance, additional data regarding machine utilization can help predict the need for repairs and downtime, as well as the resulting labor impact. Machine learning tools can take this one step further by incorporating raw materials usage and supplier contracts to predict raw material consumption and optimize operations.
Start Where You Are
Optimizing the supply chain using predictive analytics and machine learning can certainly create a competitive advantage, but may seem like an overwhelming project to undertake. If you’re still using outdated tools, how do you move from a reactive approach, with limited historical data, to a proactive approach, using predictive data?
Experts agree that you must start where you are. You don’t need to throw out your current systems and invest in tools that will break your budget. Instead, you can keep what’s working and make investments where you have gaps in order to streamline operations and improve profitability.
And if you think machine learning and predictive analytics tools are just for big companies, think again. Multiple tools are now available for even small- and medium-sized firms, allowing them to take advantage of readily available data that will move their business forward. These tools will allow you to:
- Combine your current internal data with publicly available data in order to gauge predictive consumer behavior
- Analyze social media trends to improve online sales
- Extract data from multiple tools that you already use, including Salesforce, QuickBooks, and Google Analytics
Predictive analytics, combined with machine learning tools, are the next step in improving and optimizing supply chain operations, and domestic and international companies alike are investing in technology that allows them to take a proactive approach to serving customers — all while enhancing the bottom line.
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