Chef Middle East is the leading importer and distributor of the finest quality food and beverages from all over the globe, supplying to the hotels, restaurants, and airline industries in the Middle East. They offer their clients an exceptional range of premium quality ingredients through a sophisticated logistics network.
The company caters to over 3000+ customers in the UAE, Qatar, and Oman, offering 8 different food categories: Meat & Poultry, Seafood, Pastry & Bakery, Dairy, Chef’s Larder, Gourmet, Asian, and Beverages. Thus a large product variety comes with a high number of SKUs (4k+) and contingency planning, which adds additional challenges for the operation team. As the business and market demand grew, Chef Middle East had a requirement to scale its operations optimally.
Everyday Chef Middle East logistics team has to keep track of thousands of customers' incoming orders via multiple demand channels and handle the number of steps to fulfil them.
Fero team worked with Chef Middle East Management team, Logistics team, IT team, and 3rd party vendors throughout the development and deployment process addressing specific requirements and providing suitable recommendations.
Automated order scheduling became possible with Machine Learning (ML) enabled recommendations. In the past, The Chef Middle East operation team had to spend hours meticulously planning order delivery. Now, orders are automatically categorized based on multiple parameters and placed in a particular order. The operation coordinator can schedule the order by simply confirming the system-generated recommendation.
Real-time tracking allowed Chef Middle East operation team real-time visibility of all deliveries and direct control of their vehicles while en route, in case the driver went off the planned route. Previously, the operation team had to confirm the delivery with the drivers by phone to ensure that all deliveries would be made on time.
Automated order scheduling allowed timely planning of the deliveries, positively affecting customers. On-time delivery ensured customers’ production process was running smoothly without a shortage in the supply of ingredients and products.
Automated trip planning and route optimization ensure that fleets are utilized to their full potential. RPA system suggests the most efficient order allocation for a vehicle based on constraints such as loading time, customer delivery window, delivery locations, and traffic data. This solution is expected to increase fleet utilization, significantly saving operational costs.
Chef Middle East delivery vehicles have multi-temperature bodies with a combination of separate compartments with different temperatures: chilled compartments with a 0°C to +12°C temperature, deep freeze compartments with a −18°C to −22°C temperature, and ambient temperature compartments. Multiple temperature bodies allow for the simultaneous transporting of different temperature products.
The RPA algorithms plan the stocking of each compartment, providing recommendations on the maximum percentage of the utilization of each compartment, considering numerous constraints across vehicles, drivers, locations, and customers. The automated compartmentalization planning will help Chef Middle East to optimize the utilization of different temperature food compartments.
Learn more about FERO RPA in action