Case Studies

How Chef Middle East achieved visibility over the entire operation through Dynamic Zoning and Auto-Demand Aggregation


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.

Brief on Chef Middle East Logistics Operation

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.

Logistics Process behind Robotic Process Automation (RPA) Scope and Value Engineering

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.

  1. Process Prior to RPA
    • Time-consuming manual sorting, planning, and scheduling of orders.
    • Manual efforts to meet the efficiency KPIs and reduce the number of errors.
    • No calculations were used on constraints such as vehicle compartments distribution (chiller, ambient, freezer)
  2. Process after RPA (RPA Modules deployed - Fleet Management, Trip Planning, Milestone Management, Dispatch Coordination, Document Management)
    • Dynamic zoning to plan assets/zone as per seasonality, the trend of Chef Middle East customer’s demand varying across locations and time (Machine Learning based)
    • Auto-demand aggregation and trip planning as per multiple projects (B2B/B2C)
    • Compartmentalized trip planning as per best vehicle-driver-customers-order-time- warehouse constraints
  3. Results
    • Reduction of total Kms traveled by the fleet
    • Expected to free up Annual 600+ Manhours of manual planning and scheduling
    • Capturing “hand-over time” for all customers, location-wise and day-wise, for the ideal plan
    • Informed decision-making was possible with real-time visibility, chat, proof of delivery (POD), document capture, and reporting.
Journey of RPA Deployment

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.