factory workflow redesign for covid vaccine production
date:
oct 2022
timeline:
1 month
industry:
manufacturing
role:
lead supply chain analyst
team:
sarah frisat, sophia martinez, tiffany ng
tools:
ms suite
this group project was part of our supply chain analytics course, in reference to the previous year of association of supply chain management (ascm) case competition.
medicrystals co. is a leading german pharmaceutical glass manufacturer who faces a sudden increase in demand for their products in the united states due to covid-19. they were concerned about their production capacity to meet the rising demand for their new chicago facilities.
problem
medicrystals co.’s new chicago facility faced severe capacity constraints as covid 19 drove an unexpected surge in demand for pharmaceutical glass.
with limited and incomplete operational data, conflicting leadership perspectives, and unclear workflow bottlenecks, we needed to determine whether the current production could meet rising demand, and if not, how to redesign the workflow to increase capacity.
goals & metrics
what does success look like?
to evaluate the production capacity, we focused on implied utilization (iu) by stations across quarters, percentage of unmet demand under each shutdown scenario, and capacity gains from proposed workflow changes.
role
lead supply chain analyst
i organized and executed the excel workflow for our capacity analysis, and suggested assumption-based scenario approach to address limited and inconsistent data.
throughout the project, i partnered with teammates closely to develop insights and proactively led troubleshooting discussions with our professor to validate our approach.
approach
how are we tackling this problem?
given the supplier, inventory, production, and utilization data, our team have approached the problem through the following three assumptions:
assuming no planned or unplanned shutdowns,
assuming only planned shutdowns, and
assuming shutdowns happen as projected
after analyzing demand and cycle time for each quarter per our assumptions, we have identified bottlenecks at various stations and concluded that the current capacity indeed cannot meet the rising demand across quarters.
considering the problem at hand, we have thus redesigned the production workflow to improve production efficiency and capacity.
analysis
can we meet the rising demand?
[current process]
[excel set up: current quarter by product]
first, we identify the demand coming in to each station by guesstimating their respective ratios.
then, we aggregate our estimates in to an overview table, where we calculate our implied utilization (iu) based on work hours required and available.
[2021q2 projection: assume projected shutdowns]
the three assumptions:
assume 0 planned or unplanned shutdowns
demand that could not be satisfied increased from around 7% in q4 2020 to about 24% in q2 2021
assume only planned shutdowns
with shutdowns, demand could not be satisfied in any quarter
assume projected shutdowns
factoring in the projected shutdowns, demand far outweighs the capacity in most stations, with iu over 385%
on differing views
the vp of operations believed capacity was sufficient, likely overlooking workflow structure and shutdown impacts.
in contrast, the plant controller argued capacity was inadequate. our scenario results and iu analysis supported this assessment.
how can we improve the production capacity to meet requirements?
based on our analysis, we recommend adding a third station to the tubing and forming processes to alleviate their strain and increase capacity, assuming a 12-hour workday.
moreover, adjust the product lines so the washing stations share the demand equally, since washing #1 and #3 are under-utilized and have the capacity to support the demand of other products.
[new workflow at a glance]
** blue = vials; red = syringes; green = ampoules **
impact
what does this look like?
by reallocating demand, adding critical stations, and smoothing workload distribution, the new process increases throughput, minimizes unsatisfied demand, and better positions the facility to meet rising vaccine-related requirements.
[new workflow projection]
conclusion
we have we learnt?
our solutions were grounded in data-driven approaches, navigating limitations with manageable scenarios. throughout the project, we sharpened our critical thinking and creative problem-solving skills, gaining valuable experience in applying classroom knowledge to real-world problems.
takeaway
additional implications
we recognize that this case study is a simplified model. in a real factory setting, factors such as physical layout constraints, equipment changeover times, labor availability, safety requirements, and capital investment feasibility would need to be evaluated before committing to any workflow redesign.
additionally, external factors such as supplier reliability, transportation delays, and regulatory requirements would also influence actual capacity.
despite these constraints, our scenario-based approach effectively highlighted key bottlenecks and demonstrated how data-driven modeling can guide smarter and more resilient operational decisions.