Business Skills

Seasonality: Apparently American’s Don’t Drink A Lot Of Water In The Winter.

When selling a new product sometimes it is important to consider seasonality when making both purchasing and supply chain decisions.

I am about to share a personal story about the first time I encountered the effects of seasonality online and explain how I was able to learn and grow from the experience by using forecasting tools.


What Happened?:

In the spring of 2017, we had began importing and selling a water bottle that greatly exceeding our sales expectations. We had sold out of our entire inventory in a few short weeks and instantly knew we had a winner. Soon we were bringing in 40ft shipping containers of this item regularly. Our sales climbed and climbed as we entered the Top 50 in Water Bottles on Amazon. We ordered a few other designs and began stocking for our first holiday season. We sent thousands of pieces of each bottle to Amazon FBA. Then came October, our water bottle sales began to rapidly decrease. Over the course of the holiday season we sold less than 20% of what we projected, paid thousands of dollars in long term storage fees, and eventually had to remove inventory at exorbitant financial and productivity costs. What we learned was that some items are affected by the time of year that you sell them. This is called seasonality.

Just In Time (JIT) Inventory Management:

Just in time inventory management is more than just an industry buzz word, it can add a lot of value to any business that relies on a supply chain. Send less more often and get hands on with your inventory. It might be more work, but it is worth it! I went from sending 3-6 months of inventory to only 1-2 months of inventory at a time. This allows me to adjust my inventory as needed and to react to changes in the market. This also minimizes the damage of overstocking when it does happen. It is a lot easier and cheaper to put a few hundred units on sale or remove them than a few thousand. It is also important to consider your lead time. The longer the lead time the further out you will have to project your sales and the more inventory you will have to send. The shorter your lead time the more reactive you can be and the lower your risk of carrying too much inventory.

Seasonality Factor And Rolling Averages:

If you have some sales data available to you, there are two mathematical methods I recommend for basic inventory management. If you work along with the examples below you too can master seasonality!

Rolling Average: A rolling average can be implemented as soon as you have a few months of sales data.

Step 1: To calculate a rolling average you would simply pick a period of time that you consider to be a good predictor of future sales. I personally like to use three multiples of my lead time, but any amount of time that contains multiple data points is fine:

2 Weeks Lead Time x 3 = 6 Weeks

Step 2: Next take your average daily, weekly, or monthly sales averages and write them out:

  • Week 1: 20 units
  • Week 2: 23 units
  • Week 3: 14 units
  • Week 4: 22 units
  • Week 5: 31 units
  • Week 6: 27 units

Step 3: Take an average of the data points within your chosen time period then round to full units:

(20+23+14+22+31+27) Units/6 Weeks = 23 Units

Step 4: The average of these historic averages would be what you would project to sell next week. It is important to use only the most recent pieces of data in your data set. This allows my inventory projections to change with time while still not being overly effected by sales slumps or bumps!

  • Week 1: 20 units
  • Week 2: 23 units
  • Week 3: 14 units
  • Week 4: 22 units
  • Week 5: 31 units
  • Week 6: 27 units
  • Week 7: 29 units

(23+14+22+31+27+29) Units/6 Weeks = 24 Units

Formula (For all you nerds):

moving average

Click here for a link to learn how to create a rolling average formula in excel!

Seasonality Factor: A more effective forecasting tool, although it requires much more data is the seasonality factor. For this I would recommend at least a year of data although the more years you have the better your forecasting will be!

Step 1: Gather at least one year worth of data

  • January: 212
  • February: 262
  • March: 242
  • April: 301
  • June: 313
  • July: 413
  • August: 337
  • September: 228
  • October: 274
  • November: 389
  • December: 404

Step 2: Next figure out what your average monthly sales are.

(212+262+242+301+313+413+337+228+274+389+404)/12 =281.25

Step 3: Calculate the difference for each month compared to an “average month” by dividing your actual sales by the “average month”.

  • January: 212/281.25 = 0.7538
  • February: 262/281.25 = 0.9316
  • March: 242/281.25 = 0.8604
  • April: 301/281.25 = 1.0702
  • June: 313/281.25 = 1.1129
  • July: 413/281.25 = 1.4684
  • August: 337/281.25 = 1.1982
  • September: 228/281.25 = 0.8106
  • October: 274/281.25 = 0.9742
  • November: 389/281.25 = 1.3831
  • December: 404/281.25 = 1.4364

Step 4: Apply this to your current sales averages to better predict your seasonal volume. This should let you see when you should expect more or less than your average sales volume.

  • Rolling average over the last 6 months: 303
  • Current moth is July
  • Expected Volume in August = Average Sales * Seasonality factor
  • 363 = 303 * 1.1982

Click here for a link to learn how to calculate the seasonality index in excel!

Understanding Seasonality is not as intimidating as it seems at first glance. Using the tools presented above will allow you to take back control of your inventory and better understand the mystery surrounding seasonality. Here at Shipmate we prioritize minimizing lead times and providing our customers with transparent inventory management!

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