Managing inventory of solar panels considering demand growth and the risk of obsolescence

Abstract

Background – As E.ON’s solar panel business has grown significantly,
they are interested in finding new methods of working with inventory
management. Currently, it is controlled manually and without any support
of structured analytical inventory control methods.

Purpose – The purpose of this Master’s Thesis Project is to investigate
and suggest a method suitable for inventory control of E.ON’s solar panels.
The model should minimize costs while fulfilling specified service
requirements. Additionally, the aim is that the model should be implementable for E.ON. This poses requirements on the method’s reliability
and user-friendliness.

Methodology/approach – An abductive and mainly quantitative research
approach has been applied, complemented with a few interviews of
a more qualitative nature. The overarching methodology is based on an
operations research modeling approach by Hillier and Lieberman (2015).
A literature search, in combination with course literature in inventory
control at LTH, was used as a basis for the theory chapter and to find
a suitable inventory control model. The performance of the inventory
control model was investigated using simulation.

Findings – The resulting inventory control model is a periodic review
reorder-point policy with fixed order quantities, assuming normally distributed demand. The order quantity is determined by the EOQ formula,
and adjusted to full container loads by comparing the expected costs
when rounding up or down. Demand is forecasted through exponential
smoothing with a linear trend. The simulation results show that the
model performs well in terms of avoiding obsolescence and achieving
providing service close to the desired service levels. The model is robust
and relatively insensitive to changes in the input parameters. The model performance was most sensitive to changes in supplier lead time and the
extent of advance information about customer demand.

Research limitations – The data analysis of customer demand was
limited by access to reliable data. Furthermore, the covid-19 crisis may
have affected the representability of the 2020 demand data.

Originality/value – This thesis proposes an inventory control model
which can be implemented in practice and is adapted to suit the specific
conditions of a solar panel solution provider, the case company E.ON.

Keywords – inventory control, solar panels, obsolescence, operations
research, supply chain

Popular Abstract

Managing inventory of solar panels considering demand growth and the risk of obsolescence

Managing inventories is a balancing act. On one hand you never want to run out of products to sell, in
order to have happy customers. But on the other hand, keeping too much in stock is costly. It is especially
difficult when it comes to products which frequently get succeeded by newer models, which is the case for
solar panels. With the use of a mathematical model, we present a way to approach this problem.

Let’s first examine an everyday problem highlighting the need for inventory control.

How much milk should we buy this week? This problem might seem like a simple problem to solve, same
as last week right? Your household should consume the same amount of milk the upcoming week as the
last week.

What if your children bring home more friends than usual after school? Then the usual amount of milk
will certainly not be enough to last until next week. But if you buy enough milk to make sure that it will
be more than enough, what happens if they stay over at their friends place instead? A lot of milk would
then go bad before it all is consumed and we do not want to throw out milk!

Now Imagine that the milk has to be ordered months in advance from when it will be consumed. Your
children have brought more and more friends over, consuming a larger and larger amount of milk each
week. It has hence become increasingly more difficult to determine how much milk should be ordered to
make sure it is enough while at the same time not having a lot of milk go bad.

The report addresses a common problem which occurs in everyday life but on an industrial scale and from
the perspective of a solar panel solutions provider. The milk is solar panels, the household is the company
and the children’s friends are customers.

The company applied a manual control of the inventory of solar panels and found their stock on hand
growing disproportionately in relation to the growth in demand. The increased demand in combination
with long lead times from their supplier and the frequently updated solar panels made manual control of
the inventory difficult.

An analytical model replacing the manual inventory control was created largely based on the theory
presented in a textbook by Axsäter (2015), an authority within the area of inventory control. This model
answers the questions

1. When should a replenishment order be triggered?
2. How large should this replenishment order be, i.e. what is the order quantity?

With the analytical model designed, how do we know it will perform well at the company? By simulating
the environment of the company we saw that our model performs well in terms of avoiding “the milk
going bad” as well as coming close to the desired service level. The model is relatively insensitive to
changes in input parameters, with the exception of the supplier lead time, and the extent of advance
information regarding the future need for solar panels.

The model is adapted to the specific company setting but could probably be used to control inventories
under similar circumstances.

Reference:
Axsäter, S., 2015. Inventory control, International Series in Operations Research & Management Science.
Springer Science+Business Media, New York, NY.

• author: Mårtensson Qvistberg, Philip and Carlsson, Markus
• supervisor: Johan Marklund
• organization: Production Management
• course: MIOM05 20211
• year: 2021
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• keywords: inventory control, solar panels, obsolescence, operations research, supply chain
• report number: 21/5674
• language: English
• id: 9061967
• date added to LUP: 2021-07-23 11:28:02
• date last changed: 2021-07-23 11:28:02