Excited to share insights from my recent Midterm Operational Management exam where I delved into the complexities of Zesla Companies. Join me for an in-depth analysis that explores operational challenges, innovative solutions, and the strategic decisions crucial for success in today's dynamic business landscape. In this presentation, I'll uncover the intricacies of challenges faced by Zesla Companies and how these align with operational management principles. We'll delve into the analysis of innovative solutions proposed to enhance operational efficiency and overall performance. Additionally, we'll examine the strategic decisions made by Zesla Companies and their impact on operational outcomes. Whether you're a student eager to expand your knowledge or a professional navigating the operational realm, this presentation offers valuable insights into real-world operational management scenarios.

1. By Team 9
Araya Suryanto | Fathur Rahman | Fransiskus Chris
Midterm OM

2. 🔑Case Summary | Overview

3. 🔑Case Summary | Database Breakdown

4. Customer attributes and bundles of CAs
For a Most Preferences Features
Primary Secondary Tertiary
COLOR VARIANTS
OPEN SUNROOF
Functional Pre-
ferences
Aesthetics Pre-
ferences
FUEL EFFICIENT
MORE SEAT CAPACITY
SPEED
Vehicles with longer distances covered on a single tank.
Options with lower environmental impact through fuel efficiency.
Prioritizing vehicles that offer savings on fuel costs.
Opting for classic or timeless colors that are Young & trendy style.
Using color to express personal style or mood.
Vehicles with additional seating for family or group travel.
Vehicles with seating that can be adapted for extra cargo space.
Desiring quick acceleration for safe overtaking on the road.
Vehicles with responsive and precise handling.
Preferring sunroofs that are easy to open, close, and operate.

5. Relative Preferences Weights of Customer
Attributes
________Zoro Cars
________Competitors

6. Zoro Car Product Development
In House of Quality
• The customer's most important
requirement is fuel e
ffi
ciency. This is
re
fl
ected in the fact that fuel e
ffi
ciency
has the highest customer preference
rating and is the most important technical
characteristic for fuel e
ffi
ciency.
• The customer also places a high value on
color variants. This is re
fl
ected in the fact
that color variants has the second
highest customer preference rating and
is the most important technical
characteristic for color variants.
• The customer is willing to pay more for a
car with more seat capacity. This is
re
fl
ected in the fact that the cost of
production is the most important
technical characteristic for more seat
capacity.

7. Zesla Production Flow Type
Intermittent or Batch Processes
The hypothesis of intermittent or batch processes is drawn from the
observed
fl
uctuations in unit sales and considerations of urgency and
variable storage capacity in the production summary.
Fluctuations in sales
Intermittent processes align with
market conditions that exhibit
fl
uctuations in demand. This
theoretical understanding
connects the observed sales
data with the type of production
fl
ow.

8. Zesla Operations theory Alligment
The alignment with operations
theory is rooted in the adaptability
and
fl
exibility required in Zesla's
production processes. The
observed characteristics of
variable demand, considerations of
urgency, unique design features,
and potential delays in spare parts
availability all point towards an
intermittent or batch production
fl
ow. This type of
fl
ow is well-
aligned with the theoretical
principles that emphasize
adaptability,
fl
exibility, and the
ability to handle diverse production
requirements.

9. Zoro Gen 2 Sales Forecasting
Analyzing sales data & Trends
• An overall increasing trend
from June to December,
peaking in December with
358 units.
• Seasonal variations with
higher sales in December,
indicating potential holiday-
related demand.

10. Zoro Gen 2 Sales Forecasting
Weighted January Sales
To estimate production related
to sales in January 2023, we
can using a forecasting method
based on previous sales data.
One method A simple method
that can be used is the moving
average method. We can use 3-
month moving average to
forecast sales in January 2023.

11. thankyou