LLamasoft published the results of a global retail supply chain study, which revealed that 73% of retailers believe artificial intelligence (AI) and machine learning can add significant value to their demand forecasting processes. Meanwhile, over half say it will improve 8 other critical supply chain capabilities. The research also found that while 56% of overperforming retailers, also known as'retail winners', use technology to model contingency plans for severe supply chain interruptions, a mere 31% of retailers who are not overperforming do the same. Overall, 56% of retailers surveyed are struggling with the ability to respond to rapid shifts, and the lack of flexibility has cost them during the disruptions such as COVID-19, with many seeing a huge drop in revenue as a result. In addition, 73% of'retail winners' have the foresight and ability to monitor capacity, which allows them to prepare for sudden shifts in demand and supply, compared to 35% of'other' or'under-performing' retailers.
This year ushered in a period of unpredictability; individuals, businesses, industries and economies were suddenly battling Covid-19. Few were exempt from the challenges presented by this pandemic, and many are seeking solutions that can help. This moment in time has uncovered just how crucial AI solutions can be for the future of healthcare. Rapid changes have made it difficult to manage the pandemic's spread and determine what the industry will look like after coming through the other side. Regardless of complications, it's still the responsibility of leadership teams to use every tool at their disposal to manage the pandemic and be better prepared for the future.
Written by Alberto Cordoba--an expert on the topic of predictive analytics--this important resource explores the development of a successful predictive analytics initiative and reveals how to avoid the potential pitfalls. To bring the process to life, the book is filled with illustrative case studies across a range of international industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail. The examples of problem solving presented are made possible by using the progressive power of information technologies that were only recently mastered in the past twenty years. Filled with expert advice and a healthy splash of humor, Understanding the Predictive Analytics Lifecycle clarifies each phase of the predictive analytics cycle to offer a playbook for future projects. Throughout the book, Alberto Cordoba puts the spotlight on developing an understanding of business performance based on the extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as the foundation for human decision-making.
PointPredictive recently added to its roster of experts tasked with helping auto finance companies combat fraud and other risks to their operations. PointPredictive announced that Mike Kennedy has joined the company as vice president of analytics to spearhead the growing team of data scientists in its San Diego offices. Kennedy has more than 20 years of machine learning experience across credit card, retail banking, small business and auto financing with a focus on leveraging advanced data science to stream-line lending decisions while driving down fraud and misrepresentation risk. Kennedy previously held key leadership positions at Mulligan Funding, Opera Solutions and Fair Isaac Corp. (FICO). "I am excited to embark on this opportunity with PointPredictive," Kennedy said.
Artificial intelligence (AI) and machine learning are disrupting business as usual across industries. This is true from supply chain to customer experience and perhaps nowhere more so than in ecommerce and retail. Following the definition, AI is the process of machines carrying out smart tasks, and machine learning is an application of AI in which machines use data to learn for themselves. And today's leading retailers are reaping the benefits of applying machine learning and predictive algorithms; Amazon has reported that 35 percent of the company's sales come from recommendations made by machine learning algorithms and Target has reported 15-30 percent revenue growth with machine learning predictive models. "Bots can understand consumer needs to facilitate price negotiation around a specific product or the entire cart" How will AI and machine learning continue to play a role in today's changing ecommerce landscape?