We start from a business need with a tangible value. We then work with your people to simplify the processes, automate what we can using AI technologies, and create analytics-embedded technology solutions that convert data into decision recommendations. Now you are making decisions in a timely manner, enabled by processes that happen at the speed of data.
And we do this in a way that your next need can be addressed faster and cheaper because the process and technology are designed to leverage what has already been built.
HOW WE HELPED SOLVE (REAL CASES)
Dynamic Lead Generation
Instead of analyzing select customer segments, we developed a dynamic lead generation process that uses AI to identify high-propensity leads. We enabled and sustained the process using an application using Big Data technologies that combined Salesforce.com, D&B, internal orders, and external industry data – solving the problems of duplicate records, dirty customer information using machine learning techniques. This enables timely targeting using all available data, where the users focus daily on reviewing the recommended leads before handing them off to sales. The immediate impact on bookings and market share.
We flipped the script on the sales process. Instead of an account executive spending 80-90% preparing, we developed a process that allowed them to spend 80-90% in front of customers and prospects. Using AI to create a recommended list of weekly meeting targets and using AI to capture the sales meeting notes, we helped the account executives to prepare quickly and eliminated the need to spend time updating the CRM system. Immediate impact on pipeline and bookings.
Instead of ad hoc review of guidelines and losing margin because they are not tuned, we developed an exception-based process that uses AI to identify when guidelines are out of control at a product and customer segment level. We enabled and sustained the process with an application using Big Data technologies that track guidelines by product, customer and deal. The algorithms identify quickly when discount variances emerge and the causality. Variances are reviewed as and when they pop up and guidelines are adjusted before they lead to lost margin. Guidelines are applied automatically to new quotes and only exceptions are reviewed by analysts for further analysis and approval.
We didn’t change the process, but we built a Big Data based AI application that tracked shipments in real-time and predicted the ETA. The application monitors the flow of every shipment and identifies bottlenecks along the way that can derail it. It then recommends to the logistics manager cost-optimal alternate routes that may still be possible for recovery.
Real-Time Supply Chain Sense and Response
We segmented the supply chain and tuned the configure-to-order process and metrics using AI to proactively manage the orders by predicting exceptions and recommending the appropriate responses. We then built an AI-based application to stream all the supply chain data from SAP, MES, and supply chain partners to create a real-time digital twin of the supply chain that monitored and predicted order flow and material consumption. This enabled real-time visibility and advanced notification to customers, and a high-velocity, high-predictability supply chain. Like Amazon, the customer promised, tracked and delivered on-time, in-full, no errors.
We believe that S&OP should be a primary collaborative decision-making process within the enterprise that shapes demand and supply to meet financial goals. So, Finance, Sales and Operations Planning (FS&OP). We re-engineered the textbook S&OP process that is about preparation and meetings, to one that is about moving demand and supply levers to meet financial plans. We built an AI-based application that integrated real-time data from CRM, ERP, and SCM to identify demand-supply mismatches. In the meeting, the team could then test available demand and supply shaping options to evaluate financial impact on revenue, share, margin and assets. Walking out of the meeting every week, they knew what decisions they were going to implement that would shape the business beyond lead time. No more fire-fighting and finger-pointing. Actual collaborative business management.
We took real-time telematics data and using AI-techniques developed a predictive maintenance application. We then extended it to monitor driver and equipment behavior. And then, we combined that information with financial data to predict new equipment purchases to help sales and identify service requirements to help the supply chain have the right parts inventory. Created links between processes that didn’t exist and captured value that was known to exist but could never previously be targeted.
Instead of post-mortem reports, we built an Executive Value Dashboard that integrated targeting, pricing and supply data to show executives in real-time how their business was doing. What the gap to AOP was predicted to be, and what was hot and what was not, so they could have their organization take advantage of opportunities and minimize downside. They are now able to steer the business looking out through the front-windshield. Not BI, but AI to help them “Control” their business and keep the commitments they made.