The supply chain used to be more opaque than it is now – each “link” in the chain fulfilled its role and passed the product off to the next. Now, it’s entirely possible to gain a transparent view from product development to delivery and beyond, as well as leverage that transparency into better decision-making.
Data analytics can reduce costs, mitigate risk and improve profit margins. How? By providing speedy data insights to decision makers at every step. Armed with this newfound knowledge, employees can troubleshoot inefficiencies and optimize processes for better outcomes.
Here are four areas in which data analytics can improve supply chain management.
Reducing Equipment Downtime
When equipment goes down, progress stalls out – and hefty bills for machine repairs and replacements are triggered.
Data analytics platforms can help monitor performance over time and reduce unanticipated downtime. Machine learning algorithms can even predict pending machine failures ahead of time so teams can intervene before losses occur.
Increasing Visibility and Transparency
Visibility and transparency in the supply chain help decision makers identify problem areas faster, as well as implement improvements to smooth the flow from Point A to B to C and beyond.
Using advanced supply chain analytics, engineers can pinpoint bottlenecks in the manufacturing process. Product developers can monitor project completion times and product quality results. Order fulfillment specialists can find insights related to inventory levels, logistics and financial metrics. Sales teams can analyze sales forecasts and consider new revenue streams.
How an individual or team utilizes analytics for the supply chain depends on their role, but modern search-driven analytics tools allow everyone to glean instant insights simply by asking questions and further analyzing the resulting data insights.
Artificial intelligence-driven analytics tools take this one step further, sending insight-detection algorithms deep into data to find patterns, outliers and business drivers. These AI tools can push these insights directly to decision makers so they can act accordingly.
Adapting to Customer Expectations
Customers expect increasingly convenient, expedient and transparent service when it comes to acquiring, well, just about anything. This means supply chains need to adapt by minimizing wasted time and effort, as well as mitigating any errors that might occur. Data analytics can help decision makers understand more deeply what customers want and identify areas ripe for optimization.
A Forbes manufacturing contributor outlines one example of how a company is using GPS information and AI to determine the condition of various delivery routes. That data helps route planners and drivers avoid impassable roads and avoid wasting time and gas – in other words, helping them get to the customer more efficiently.
Forecasting Demand While Reducing Risk
Regardless of how smoothly your supply chain is running, the last thing you want to do is inaccurately predict demand – whether by overshooting it and ending up with dead stock or undershooting it and experiencing shortages.
Demand forecasting used to involve primarily looking at historical data and making an updated best guess. But data analytics is helping companies forecast more accurately and farther into the future, minimizing the risk of lost revenue. As one expert notes for Supply Chain Digital, companies can harness advanced analytics to plan “all the way out to 12-18 months… without taking too many risks.” How? Analytics can provide insight into downstream customer demand and buying behavior, among many other metrics integral to accurate forecasting.
Data analytics can do a lot for supply chain management, mostly by increasing visibility at every step so decision makers can get an immediate feel for current performance and make decisions to optimize it further.
