Production Network Capacity Modeling for Strategic Network Planning
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simons-psimons-mba-mgt-2022-thesis.pdf
Description
Thesis PDF
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855.5 KB
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Adobe PDF
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9fb74900b64db13f8af358dc1e7a47a7
Author(s)
Simons, Philipp
Advisor(s)
Willems, Sean P.; Cima, Michael J.
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
Strategic planning of manufacturing capacity requires data-based approaches to determine current and future constraints in a manufacturing network. While oftentimes, the desire to improve decision-making in strategic planning is strong among decision makers, and data on capacity generally exists in some form, there can be a lack of centrally coordinated efforts to harvest existing data as well as high degrees of inconsistency. In addition, modeling manufacturing capacity is an inherently complex problem due to varying modes of production, unclear units of measure, and complex global manufacturing networks.
In this thesis, a capacity model design is proposed for a global medical device manufacturer, and key aspects of the model functionality are demonstrated in a case study. At the core of the capacity model is a database structure using standardized data fields for capacity and demand data, including cycle times, shift structure, and space. The logic of the capacity model is developed, with the goal to capture supply chain complexities such as mixed model lines or various degree of automation. In short, the logic determines the required production time for the product portfolio under consideration, and assesses the available capacity by comparing this required production time with the total available time.
The logic is tested on a prototype product with a focus on mixed model lines. It is found that naming and product grouping inconsistencies require significant manual data manipulation, which – in combination with a lack of standardized, centrally available data – will form the biggest bottleneck in the implementation of the capacity model. Finally, an implementation roadmap is presented to offer guidance on converting the logic presented here into a functional model for decision makers in a supply chain strategy organization.
MIT Department
Sloan School of Management
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