George Ninikas


As the Senior VP for Retail and Wholesale at ORTEC, George Ninikas holds a leadership position responsible for driving revenue growth and managing retail & wholesale accounts in Europe. He also plays a pivotal role in shaping the company’s strategy and market presence in the retail and wholesale sectors. By harnessing the power of cutting-edge technology and advanced analytics, he aims to improve the efficiency, profitability, and customer experience of ORTEC’s clients. His primary objective is to translate technical solutions into a language that customers in the market can understand, promoting the company’s expertise and expanding its reach. Alongside his career, George pursued his academic interests by completing a Ph.D. in Operations Research and teaching for a series of years.


Expert view:

Deploying machine learning to improve efficiency in e-grocery delivery operations

In the bustling realm of e-grocery delivery, where timely and efficient operations are paramount, the integration of advanced analytics and algorithms has emerged as a game-changer. By delving into historical data and employing sophisticated tools, e-grocery providers can optimize their delivery operations with unparalleled precision, thereby elevating customer satisfaction and streamlining logistics.

One of the primary applications of these cutting-edge technologies lies in refining estimates for delivery stop duration. Through meticulous analysis facilitated by machine learning algorithms, factors influencing stop times are identified and quantified. Whether it’s the complexity of the delivery location, the nature of the items being delivered, or external variables like traffic conditions, each aspect is assessed. By assigning a certain duration to each influencing factor, the system dynamically calculates and updates stop durations for every delivery order, ensuring a bespoke approach tailored to individual delivery order.

Moreover, the utilization of realization data from vehicles offers invaluable insights into the driving styles and behaviors of drivers. By scrutinizing this data, e-grocery companies can gain a comprehensive understanding of driver performance, allowing for the definition of more accurate travel times that are continuously and automatically being update. It has been demonstrated that it can improve the accuracy of planned ETAs by 50% and even allow the planning of >2% more stops per route (on average).

Furthermore, historical data serves as a treasure trove for estimating the potential cost of an order and selecting optimal delivery timeslots. By leveraging past order data and associated costs, e-grocery providers can predict the financial implications of various delivery options. This information empowers companies to nudge customers towards selecting more efficient delivery timeslots, thereby optimizing route planning and resource allocation. By aligning customer preferences with operational efficiency, this approach ensures a win-win scenario for both parties involved.

The dynamic nature of these analytics-driven strategies enables adaptability and precision in e-grocery logistics. Instead of relying on static models and assumptions, companies can continuously refine and update their processes based on real-time insights and evolving trends. This iterative approach not only enhances operational efficiency but also fosters agility in responding to changing market dynamics and customer demands.

In conclusion, the integration of advanced analytics and algorithms represents a pivotal advancement in the realm of e-grocery delivery. By harnessing the power of historical data and machine learning algorithms, companies can fine-tune their operations, optimize delivery routes, and elevate the overall customer experience. Through a dynamic and data-driven approach, e-grocery providers can navigate the complexities of logistics with unparalleled precision, setting new standards for efficiency and reliability in the digital age.

Harnessing Optimisation Technology for Profitable and Sustainable Last-Mile Delivery

During the presentation, we delve into the critical role of optimization technology in revolutionizing last-mile delivery operations. Beginning with an exploration of the challenges faced in the final leg of the delivery process, we highlight the significance of adopting innovative solutions. Through the lens of optimization technology, we uncover how algorithms and data-driven strategies can streamline delivery routes, enhance vehicle utilization, minimize carbon footprint and increase satisfaction of both customers and drivers. By applying optimization technology in timeslot booking, route planning and real-time re-optimization, businesses can achieve cost savings, improve customer satisfaction, and contribute to sustainability goals.