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HOW TO ACHIEVE THE OPTIMAL STOCK LEVEL TO MEET SEASONAL FLUCTUATIONS IN DEMAND
4 good tactics and strategies to
ensure optimal stock before, during,
and after a season
if the item was subjected to a sudden trend? Would you
have enough items in stock in case of an unexpected peak
in demand? And how do you cope with changes in demand
caused by item seasonality?
Imagine you have 150 trench coats in stock with an
average sales volume of 100 units per period and a
minimum order quantity of 20 units. The lead time is
7 days and given that you do not want to disappoint
your customers with stock-outs, you hold a safety
stock of 50 units. When is the right moment to place
a new order with your supplier and how much should
you order?
Seasonal demand
The order process in theory
The logical way of working out your requirement is as follows:
on average, you sell 100 units per period, which equates to
25 units per week. In 4 weeks you will reach your safety stock
level. Therefore, in order to avoid selling your safety stock,
you decide to place your order in week 3. Given that you are
expecting to sell another 25 units in week 4, you order twice
the Minimum Order Quantity (MOQ) and decide to order 40
new trench coats in week 3.
The order process in real life
The situation described above is easy to forecast. However,
this is not a realistic scenario. Demand patterns are rarely
as stable as this example. What would happen to demand
During the summer months, demand for puffer coats will be
low whereas demand for shorts drops off in winter. This is not
rocket science. You know that these articles have a seasonal
demand pattern. However, you do not know exactly when
the season will start as this is dependent on the weather.
Furthermore, the consumer’s mindset also plays a role: when
it is 20 degrees Celsius on a Saturday in March, many people
will want to go barbecuing in their new shorts as they feel
reassured that the weather is getting better after a cold
winter. However, three months later, they will perceive this
temperature as chilly and will stay inside wearing jeans on a
sunny day.
As a result of this behaviour, you need to understand both
the seasonal influence on demand as well as the short term
demand profile. For example, if the temperature reaches 30º
in early May, this is likely to boost sales of summer dresses.
However if the temperature drops to 15º towards the end of
August, this is likely to have a less significant impact as this
would be at the end of the seasonal peak.
With the end of the season in sight, you are likely to face
further uncertainty. When is a season really over? Take the
holiday season as an example: when should you start
phasing out products like flip flops and swimwear? Do you
want to save costs by phasing out seasonal products early?
Alternatively, do you risk holding excessive stock levels by
phasing items out later in order to avoid disappointing
customers?
maintaining such a high safety stock level is not optimal.
Therefore it is important to correct your historical sales data in
order to encompass seasonal demand patterns. This way you
will get a realistic reflection of the historical demand. When
looking at this reflection, you will discover that your safety
stock level can be decreased. When purchasing articles for the
low season, use a dynamic buffer for your seasonal pattern.
In order to ensure a high service level
at low costs during a season, it is very
important to phase products in and
out in a timely manner.
3. Cleaning seasonal patterns from promotions/events:
if you have held a promotion or an event for the past two
years in a row, in your forecast this might seem like a season.
Therefore it is important to separate seasonal patterns from
the influence of promotions and events. If you decide not to
hold the same event or promotion in the third year, you will
need to cleanse your data in order to avoid unnecessarily high
forecasts.
Good tactics and strategies to
ensure optimal stock before,
during, and after a season
1. Improve your forecast accuracy…
• ...by including more external influences in both your short
term and long term forecasts.
• ...by cleaning the historical data of events and promotions.
You can then calculate the deviation and, based on that,
create your forecast.
2. Develop a specific inventory strategy, such as
increasing your stock upfront in order to cope with an
expected peak sometime in the near future.
3. Have a good look at your Minimum Order Quantity
(MOQ) and Economic Order Quantity (EOQ): will you
need to increase or decrease your order quantities to cover
certain risks?
4. Change the level of your safety stock for certain
parts of the season: during the low season, your
customers will probably accept a lower service level
compared to the high season.
Points of attention
1. Assortment management: decide which articles sell
throughout the entire year and which are bound to their
season.
2. Deseasonalisation of demand: with seasonal articles
you will see a high deviation in demand in the past. This
in turn results in a high level of safety stock. However,
given that you are aware of these deviations in demand,
4. Local forecasting: when you want to forecast the demand
for umbrellas in one city but you only sell five pieces per year
in that city, your population is too small to make a reliable
forecast. In this case it is better to gather the data of a larger
area, providing you with more information on which to base
your decisions.
5. SKU versus aggregation: When introducing a new
article, you will have insufficient data available to make a
reliable forecast. In this case you can use the seasonal pattern
of a similar article or a comparable group of (aggregated)
articles. Therefore it is important to calculate the seasonal
forecast on various hierarchical levels. Please note: in practice,
aggregation is often based on incorrect product hierarchies.
Thus, it is important to have a good understanding of the
aggregate!
6. Forecasting events: the difference between a season and
an event is that an event cannot be put on a timeline. For
example: Christmas might be on the same calendar day, but
the fact that different weekdays are involved that drive the
shopping behaviour makes it non-repetitive. Easter also falls
on a different weekend every year. As a result, when creating
a forecast, the peak in demand should be shifted back
accordingly.
Timing is key!
By increasing and decreasing your forecast and buffer stock
for products with a seasonal demand in a timely manner, you
can maintain an optimal level of stock. This way you can keep
your costs low, whilst continuously delivering a high service
level to your customers.
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