Simulating Ice Cream Production: Recognizing Constraints and Manufacturing Capacity Planning

Simulating Ice Cream Production: Recognizing Constraints and Manufacturing Capacity Planning

Problem

Conaprole, the biggest dairy production company in Uruguay, produces more than 150 SKUs in their ice cream plant, using five production lines, and up to five different packaging configurations for each line.

The company plans ice cream manufacturing capacity on a 12-month rolling basis as part of the Sales & Operations Planning process, and the demand plan varies a lot due to seasonality. The factory management needs to prepare the production lines for the peak season during the low season, taking into account product shelf life and warehouse freezing capacity and costs.

The factory was often unable to meet the high season demand which generated stock-outs, and the management found it very difficult to quickly reschedule their detailed plans due to the challenges they faced.

Manufacturing Capacity Planning – Model Logic

Manufacturing Capacity Planning — Model Logic

Other factors, including bottlenecks and constraints in the manufacturing processes and variations in human resource availability that can occur randomly, made capacity planning analyses even more difficult.

The management’s challenge was to be able to reformulate their plans in order to balance supply and demand and make sure they would avoid stock-outs in key products. They also sought ways to optimize the use of their manufacturing capacities.

ITE Consult found simulation modeling to be the best tool to carry out manufacturing optimization and provide Conaprole with the solution to these problems.

The objectives of the manufacturing simulation model were:

Model Statistics

Manufacturing Capacity Planning — Model Statistics

Solution

Using AnyLogic’s discrete-event modeling capabilities, the consultants designed and developed a manufacturing optimization model. It was integrated with the company’s S&OP platform and SAP Material Management and Production Planning.

The created solution included three experiments with the model of the production system. Each of them addressed one of the objectives above and helped solve the business problems questioned.

Learn also how ITE Consult integrated AnyLogic with SAP Analytics Cloud to create a powerful S&OP platform for their client's business project.

The first expeiment

The model examined the initial manufacturing capacity plan, detecting stock-outs and backorders that could be expected if production followed this plan. It allowed the management to explore production needs based on demand and initial inventory.

This experiment also gave users the ability to find out, by manually modifying parameters, how different situations could impact performance, for instance: the need to close lines during certain periods, the necessity to modify equipment efficiency, extend resource availability, or change human resources’ schedules.

Users could manually change the priorities of SKUs and analyze the expected impact of such actions on revenue (costs associated with stock-outs differed by SKU). Additionally, they could define minimal used manufacturing capacity and some other policies.

The second expeiment

The parameter variation experiment ran the model of the system 100 times and searched for the solution to fulfill the demand and keep products’ shelf life as long as possible while minimizing warehouse costs.

The third expeiment

The last experiment optimized the use of lines by freeing production capacity in peak periods. Manufacturing was scheduled as close to the beginning of planning periods as possible in order to leave free capacity in all manufacturing lines as a buffer.

The input data included:

The system considered the following:

Outcome

All simulation results segmented by month and SKU were exported to Excel. Additionally, the model presented changes in demand and inventory levels by month in histograms. It also gave information about stock-outs, in case they happened.

By using the model, the Conaprole management was able to:

The simulation model provided the management with the insight to choose the solution that would increase revenue and minimize the risk of stock-outs.

Check out more case studies from ITE Consult: Pet Food Production Optimization and Maximizing Push Boat Fleet’s Net Voyage Revenue.

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