So, you have the right data foundation and you trust its quality. Your objective is to act and not re-act. Advanced analytics is the epitome of “measure tomorrow”: The ability to use your data and see into the future, bringing about, cost optimization, additional revenue, optimized processes, enhanced customer experience.
We are living in an era of high market vulnerability in challenging events. Those events are not anymore considered as “Black Swans” as they occur more and more frequently having a tremendous impact on the world’s economies. CCOs and COOs are heavily dependent on accurate forecasts to serve demand, produce with sufficient raw material quantities purchased at the best possible price. CFOs need all those estimates to be reflected in the cash flow projections.
As a result, companies are trying to answer the below questions:
WITSIDE offers end-to-end forecasting solutions combining historical (internal) data, with exogenous data that are improving the accuracy of the forecasting models. We have developed a unique framework to address our clients’ needs, consisting of 4 steps:
We deploy AI-driven software solutions to enable our customers perform effortless forecasting, embedding their personal experience or calendar events that affect its accuracy. We assist them to measure their accuracy and continuously improve it under a solid S&OP process.
In a globalized and highly competitive business environment, supply chain optimization has become more prominent than ever before, because the competitiveness of a company is strongly dependent on the performance of its supply chain (SC). In a broad sense, SC comprises all the activities from purchasing of raw materials, to the manufacturing and distribution of the final products and reverse logistics. Since the SC activities cannot be designed in a stand-alone and isolated way, every single optimization solution should be integrated into company-wide systems and processes.
We answer supply chain questions by evaluating and comparing hundreds of scenarios side-by-side. We can help you make strategic decisions faster and build a SC capability to rapidly respond to planned and unplanned supply chain disruptions (such as the recent COVID-19 crisis). We leverage sensitivity analysis to identify tipping points in your supply chain, so you can determine the best strategy to implement and proactively select alternatives before an event even occurs.
Examples of interest areas we could help you with:
WITSIDE’s supply chain optimization solutions put the power into your hands to optimize for the right variables – cost, service, speed. Integration with your operational applications (ERP, MRP, last mile, CRM, etc.) puts our solutions into an operating mode for maximum adoption and achievement of business objectives.
Machine learning (ML) is a widely (if not abusively) used term to describe an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
In WITSIDE we believe that manual tasks performed during traditional Data Science (DS) processes could be replaced by automated machine learning (auto-ML). Auto-ML takes advantage of the strengths of both humans and computers. Humans excel at communication, engagement, context and general knowledge, as well as creativity and empathy. Computers and software systems are ideal for repetitive tasks, mathematics, data manipulation, and parallel processing — providing the power and speed to master complex solutions.
The tasks that auto-ML automates are shown in the below graph:
In a nutshell, our approach is based on the context and needs of our clients during a project. We deploy auto-ML solutions when:
We deploy classic ML when:
ML or auto-ML is can be used standalone or combined with other techniques. For instance, we can “train” a model based on drivers’ behavior to predict delivery time of an order. Or we can “sniff” social media content to identify posts about specific products, services, companies, or individuals and get a sense of their meaning. In production, ML models can identify quality issues in output images. In insurance, to spot fraud patterns in claims’ processing or predict customer churn and in retail to segment customers and maximize their lifetime value.