Manufacturing PredictiveMaintenanceUse of ML can predict when maintenance should be performed and thereby, preventsunexpected equipment failures. The underlying principle is to performmaintenance at a scheduled point in time when the maintenance activity is mostcost-effective and before the equipment loses performance within a threshold. Condition MonitoringManufacturing systems and processes are becoming increasingly complex, making decision-makingin process control a necessity.
With the use of Condition monitoring, maintenanceand other actions can be scheduled beforehand to prevent failure and avoid consequences.Using ML, predictions can be made in advance for those conditions which shortena normal lifespan of an activity.Process OptimizationWhen optimizing aprocess, it is important to maximize one or more of the process specifications whilekeeping other constraints same as before. The goal to be achieved is to minimizethe cost and maximize the efficiency.Demand ForecastingIt is easier to predictthe future demand for a product or a service on the basis of past events andcurrent trends using ML algorithms.
This can be applied to inventory management,production planning or in assessing the future capacity requirements. Banking andFinancial Services Fraud DetectionMachine learning algorithms are able to detect and recognize thousands ofpatterns on a user’s purchasing journey and thus, is more useful in preventingfraud detection. Automated fraud screening systems powered by machine learningcan help businesses in reducing fraud.Risk AnalyticsPredictions aboutrisk scores for individual customers can be made with certainty using MLalgorithms. It can also help to accelerate and streamline risk processes toreduce costs from credit losses and manage operational risk.
Credit Worthiness EvaluationWhen a business applies for a loan, the lender must evaluate whether thebusiness can reliably repay the loan principal and interest. Lenders commonly usemeasures of profitability and leverage to assess credit risk. Machine learningcontributes significantly to modeling these applications. CustomerSegmentationThere are differentalgorithms available which can help in segmenting the customers based on theirpurchasing habits. It helps to identify the likelihood of future purchases.RetailMarket Segmentationand TargetingWith the help oflarge datasets available, it is now possible for marketers to improvetargeting, response rates, and overall marketing ROI by studying and analyzingthe consumer spending habits and purchasing behavior.
Product RecommendationsE-Commerce websites are an excellent example in this category. Based oncustomer purchases, it is possible to predict and recommend similar products orproduct accessories. In this way, it provides a personalized experience to eachcustomer.Inventory PlanningRapid changes in business requirements and the complexity of factorsinfluencing demand are making it difficult to accurately model the causes ofdemand variation. Machine learning can help companies overcome this challenge.They help in forecasting the demands and thus, in planning the inventoryeffectively.
Energy and UtilitiesSmart GridManagementIt is now possible to connect sensors, smart meters, and various soft wares to forecastthe consumption and monitor assets to improve efficiency. Machine Learninghelps in building an energy grid system with smart solutions which helps inreducing outrages.Power UsageAnalyticsApplying machine learning algorithms to large datasets available for utilities datacan help in uncovering consumer consumption patterns, managing energyconstraints and, detecting and preventing fraud.
Energy Utilization and OptimizationSignificant energy savings can be achieved in many manufacturing industriesthrough process integration. Energy utilization can be monitored and optimizedbased on consumption patterns and seasonal variations. Travel andHospitality Dynamic PricingWithout the actual need to manually define complex pricing rules, it is nowpossible to fix the prices dynamically. Thanks to machine learning by which theprices get optimized every time whenever a user is performing any action on it.It keeps on learning itself and predicts whether to display the original priceor the discounted price.
Aircraft SchedulingMany airlines use different techniques to create robust and reusable predictivemodels to provide a holistic view of operations and enhance business value. UsingML algorithms, it is possible to predict the flight delays, monitor, andimprove flight operations.Traffic patterns and congestionmanagementAdvanced machine learning algorithms can be augmented with real-time datavisualization to predict the traffic patterns so as to manage congestion. It isnow possible to identify the relationships and gain meaningful insights to takeappropriate actions to improve the traffic flow and minimize delays.