As the biggest dairy company in South Africa, Clover’s core business is the processing, marketing and selling, and distribution of dairy, dairy-related, and other food products. The company’s product range is marketed under national brand names and includes short and long life products.
Clover distributes over 1000 product lines that are grouped into product categories. Clover’s distribution is done by a fleet of refrigerated trucks. Its central distribution centre is in Johannesburg from where products are transported to regional Distribution Centres (DCs). Clover management wanted to determine if, by following a certain delivery policy, enough products could be delivered to DCs at Pietersburg in the Northern Province and Kimberley in the Northern Cape in time to satisfy demand. They also wanted to determine the minimum stock levels at the two DCs to ensure minimum sales losses.
Þ Short life and fresh products have to be delivered within a certain time window.
Þ Trucks have to be full when going to Pietersburg and Kimberley.
Þ Orders are fixed on a daily basis.
Þ Fresh dairy products have priority over other products.
Rules regarding the loading of certain products in a prescribed manner added to the complexity of the problem.
Arena® simulation software was used to analyze the viability of Clover’s new distribution concept and to establish the minimum stock levels for different product categories at the DCs. The number of docking bays at all DCs was taken into account as a possible constraint.
To determine whether distribution would be successful, the model measured such outputs as maximum stock at the DCs that indicated what storage space would be required; sales lost at each DC per product category; truck load utilization; and time products spent in the system. (The latter was necessary to determine whether products could be distributed within their required time window.)
The minimum stock levels of the different product categories at the Pietersburg and Kimberley DCs were first determined by assuming infinite stock levels at the Central DC in Johannesburg. By taking into account real life constraints (e.g. actual sales figures), the Arena model determed minimum stock levels, avoiding sales losses at the two DCs and establish- ing the stock levels at the Central DC. Additionally, the Pietersburg DC was planning to expand its facility, so the model’s calculation of available storage space was especially valuable. More importantly, simulation assessed the viability of a new distribution concept before a costly implementation
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