Evaluating modeling techniques for quantifying production risk in contact lens manufacturing

Abstract

Johnson & Johnson Vision (JJV) is a leading manufacturer of contact lenses offering a variety of vision correction products. They have a strong commitment to innovation--defining new product categories and improving existing ones--to better patient outcomes, but this poses a challenge for machine production capacity and long-term planning. As medical devices, contact lenses must first be qualified and validated to run on lines. Additionally, capital equipment has a multi-year lead time from design and order to onsite implementation. Taken together, these constraints add great complexity to JJV's supply chain. The JJV team has a strong capability in aggregate demand planning but determining the right product mix can be difficult as consumer tastes change, new products are uncertain, and the future cannot be predicted.

This complexity faced in manufacturing contact lenses along with forecasting product mix highlights the importance of having the right capacity at the right time in maintaining high customer service standards. Strategic capacity planning, looking out 3-5 years, is currently viewed deterministically, meaning that a single number is decided upon for each product line for both demand and supply. An aggregate production plan using the various machines is then built around this deterministic forecast. This thesis attempts to address strategic capacity planning through quantification of risk relative to a plan of record using various techniques, specifically looking at risk factors as inputs to demand and production planning. The focus of this research is to probabilistically model the risk in manufacturing line variability as an input to production capability and planning at JJV.

A proof-of-concept was developed for each technique with a focus of Monte Carlo simulation as a model for uncertainty in production, which was then expanded to all other lines where appropriate conditions were met. Under the analysis assumptions, 75% of the fleet across both manufacturing sites was able to be analyzed and able to be identified as high risk in their current plan for both over- and under-production, which helps to inform capital needs. Ultimately, the results of this project intend to smooth out Long Range Financial Planning and challenge existing forecasting methods and metrics.