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Solving Bottlenecks in Dairy Production Facilities with System Dynamics

Apr 23, 2024 | Agriculture and Food, Business, Cases

EXECUTIVE Summary

  • FrieslandCampina faced potential bottlenecks in production due to the merging of two factories. They hired SD&Co which employed system dynamics simulation models to predict and manage these issues effectively, ensuring smooth operational integration.

  • The project led to strategic changes, such as optimizing algorithms for pallet selection and adding a conveyor belt system, thereby enhancing efficiency without the need for extensive physical expansion of facilities.

  • The implemented changes have prepared FrieslandCampina to handle increased production volumes and maintain efficiency even in scenarios of partial factory downtime, demonstrating a successful adaptation to the merger’s demands.

#FriedslandCampina #SD&Co #Diary #Netherlands

The Problem

FrieslandCampina, a Dutch cooperative, specializes in transforming milk from dairy farmers into a wide array of dairy products. They were undertaking a significant project: merging two of their production facilities. This merger was anticipated to introduce new challenges, particularly in the filling and palletizing stages, as well as in the operations of their fully automated warehouse. The project was handled by SD&Co and their primary goal was to foresee potential bottlenecks under various post-merger production scenarios. Identifying these potential bottlenecks was crucial to ensure a smooth transition and maintain efficiency. Additionally, SD&Co was tasked with devising strategies to address these bottlenecks in the most effective and efficient manner possible, keeping in mind the operational workflow and the increased scale of production due to the merger.

Figure 1 – FrieslandCampina’s production facility

The Solution

FrieslandCampina faced a significant challenge: implementing changes to their factory could take up to two years. This long time frame posed a risk of reduced production output or the possibility of overinvesting in capacity expansion. The complexity of the situation was heightened by the interconnected nature of the factory and warehouse processes, which made it challenging to accurately predict outcomes using traditional tools like spreadsheets.

To navigate these complexities, SD&Co employed a comprehensive approach by developing four distinct simulation models. Each model varied in scope and level of detail, enabling a thorough analysis of a wide range of production scenarios and potential physical modifications to both the factory and the warehouse. These simulations were instrumental in testing the effects of various changes and understanding their impact on the overall operations.

Thanks to these sophisticated simulation models, FrieslandCampina and its suppliers were able to pinpoint the most effective and efficient solutions. They could identify adjustments to the factory and warehouse that would best accommodate the increased volumes resulting from the merger of the factories. This strategic approach allowed for a well-informed decision-making process, ensuring that the adjustments made were optimally aligned with the new operational requirements.

Figure 2 – Examples of sectors of the system dynamics model developed by SD&Co.

Figure 3 – Overview of simulation dashboards.

Outcomes

In this pivotal project, SD&Co identified the most efficient modifications necessary for the factory and warehouse to handle the increased volumes post-merger. FrieslandCampina, in collaboration with its suppliers, is actively implementing these recommended changes. The key deliverables of this project were the innovative simulation models and a comprehensive presentation detailing the recommended changes. These system dynamics tools provided valuable insights, leading to strategic adjustments within the operational framework of FrieslandCampina. 

One significant change was the refinement of the algorithm that manages the selection of pallets for outbound elevators. This adjustment negated the need for physical expansion of the outbound elevators and staging lanes, thereby optimizing existing resources.

Additionally, they introduced a small conveyor belt behind two palletizers to avoid the need for a more extensive and costly redesign of the internal transport system around the palletizers.

Based on the simulation, FrieslandCampina also made a strategic decision to refrain from further investments.  The model demonstrated that the new capacity would be adequate across a variety of production scenarios. This included scenarios where parts of the factory might temporarily break down or require maintenance. The project’s outcomes have significantly contributed to the robustness and efficiency of FrieslandCampina’s operations. The implemented changes ensured that the factory and warehouse can smoothly handle increased volumes, while providing a buffer for unforeseen production challenges. 

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One thought on “Solving Bottlenecks in Dairy Production Facilities with System Dynamics

  1. Remarkable model. I was surprised that you would try this with “mere” system dynamics. We did so many years ago for a couple of clients and the moment processes are competing for resources the formulas became quite sophisticated (if-then formula covering a whole page). Our solution was to enhance our system dynamics software to feature process and resource factors and to indicate constraints over time (applying the Theory of Constraints). Nevermind, nice modeling!

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