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The time-consuming quality assessment process is not laminated from possible human errors, not to mention the cost-bearing any false alarms may have on the business. Tuple harnesses Artificial Intelligence, machine learning & advanced analytics to determine quality defects well in advance. Our AI smart platform employs advanced statistical algorithms to detect quality issues by analysing data from raw materials, machine maintenance records, production lines, & draws insight real-time. We enable businesses to device these smart insights to improve the quality of products under manufacture, lowering the costs of quality inspection and replacing manual dependency.
Needs of modern buyers are changing which has evolved the customer management practices in the manufacturing sector. Sales and marketing efforts need to align around one goal of meeting these needs in every interaction to adopt this change. Tuple helps sales & marketing teams by automating repeated tasks of following up and sustaining the lead & creating a custom quote for those leads. By applying predictive analytics to identify right leads from multiple sources, Tuple validates and segments them based on customer interactions to increase sales effectiveness, cross-sells/up-sells and deal conversions.
In the manufacturing process, the equipment performs repetitive tasks daily which leads to machine failure and downtime. Factories often schedule preventive maintenance, but that may incur additional costs for replacing the parts that are not worn-out. Overall, it decreases the manufacturing capacity & results in unexpected production delays. Tuple’s AI-powered CDP captures a variety of data from the machines connected with the Internet of Things (IoT) & analyses real-time insights, patterns with predictive analysis to determine any irregularities that may occur in the manufacturing process & suggests steps to resolve the issue. Tuple’s predictive intelligence engine builds models with advanced ML algorithms that can look for patterns in data & generate more accurate predictions of the lifespan for a component in the given environmental conditions.
Traditionally, supply chain managers relied on historical data like previous sales, inventory data, vendor incentives or manufacturing capacity to determine the future demands for resources as well as the production quantity. But today, the estimation calculation of production quantity for the coming month, quarter or year is largely dependent on customer demands which are variable of weather patterns, product reviews. Digitally smart and agile competitors make it virtually impossible to forecast demands with just historical data analysis. Tuple has built a platform that uses Artificial Intelligence and predictive analytics that unifies data from the company’s legacy systems. With advanced processing speeds, Tuple’s platform gives more accurate forecasts - the amount of inventory, labour, and resources needed to meet the production & consumer demands.
Tracking a variety of different workflows in manufacturing processes is difficult. Manually inserting data to every field, over multiple columns of excel sheets is also not scalable. Instead, it escalates human errors, decreases the accuracy of product reporting which turns into underselling. To address this inefficiency in the manufacturing industry, Tuple employs the Internet of Things (IoT) & RFID Technology to automate inventory management. Every relevant data is automated to a big data warehouse. Inventory managers will be able to save an average of 18 hours per month spent in taking stocks & compiling different reports. Manufacturers can track real-time inventory insights through a dashboard, which adds to the operational efficiency while meeting customer needs at the end of the supply chain.
You can download the PDF for free.