1. The email discusses improving inventory management of slow moving products that make up 30% of inventory but less than 1% of sales per period. 2. It provides three ideas to review: re-forecasting too often for low demand products, failing to identify seasonal or market trends, and using the wrong data for forecasting regular demand. 3. Improving demand forecasting through accounting for different types of demand like regular, promotional, and closeout sales can help reduce out-of-stock issues compared to just sales forecasting.

1. De: "Stuart Dunkin at Data Profits" <sdunkin@data-profits.com>
Para: Humberto <humberto.galasso@grupoversia.com>
Fecha: 11/06/2013 02:02 PM
Asunto: How I improved 30% of my inventory in 30 days
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Data Profits Inc.
Dear Sir orMadam,
80% of your product makes up only 20% of sales. Almost 30% of your product sells
less than 1 per period. Managing the inventory for the 80% product mix is
critical to raising profits and beating the competition. Retailers that continue
to use top down replenishment strategies will not survive the new marketplace with
the hyper-informed customer. Below are three ideas you can review in your business
to help determine your opportunities for improvement with slow and intermittent
demand products.
OOPS! 3 Expensive Out-of-Stock Mistakes and How to Fix Them
The Truth about Forecasting
Slow Moving Products
Most people think they need multiple forecast
algorithms or methods for managing slow and
intermittent demand. In reality, there are two
forecast methods that work very effectively:
Croston's and Poisson. A third method called
'bootstrapping' is being discussed in more circles as
a possibility. Out-of-Stocks and overstocks occur
more often due to poor practices by the retailer, not
the forecast algorithm used. The origin for problems
with any forecast method are very basic.
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2. Incorrect use of the forecast method
Lack of accounting for seasonality
Sounds simple right? Here is the catch: many
people don’t do either of these steps well, and the
results are always out-of-stocks and overstocks,
often in the same business lines at the same time.
Bad data and mis-use of a formula can take on
many variations.
Below are three ideas you can review in your business now. Three mistakes to avoid are:
Re-forecast too often: use monthly, not weekly, when products have average
or low demand.
1.
Failure to identify when a product has seasonal or market
trends occurring.
2.
Wrong data used for forecasting regular demand3.
Re Forecast Frequency: Think about a slow mover that sells 1 unit every 4 weeks. If you run a forecast
accuracy on 3 of the weeks, then the forecast is 100% wrong. This simple example is why forecasting across
a broader period (4, 8, or 13 weeks) is very useful for slow moving products. A good forecast engine will use
the zeros in the forecast math. If you measure the forecast weekly, then the system will look each week and
say the forecast is 100% wrong in 3 of the 4 weeks of our example. That math will translate into more safety
stock and greater error margins.
Seasonal & Market Trends: Often a product has seasonal or market trends that the human eye fails to
catch. A index or trend is actually just a multiplier to the base forecast. The best way to determine an index is
to use three years of regular sales data. Remember regular sales data must be devoid of promotions, events,
and closeout sales. The sales should include regular sales and lost sales. Graph the three years by week
and see if a pattern exits. If a pattern exists build the index (call us for help) and apply the index to your base
forecast. This will flatten out what appears to be slow or intermittent demand in many cases.
Review your slow and intermittent demand products and determine if you have the right service goal,
accurate forecast, and best re-forecast setting. If you need help, then call us and we are glad to step through
some of your examples to help you learn best practices.
Wrong Data for Replenishment Creates Out-of-Stocks Forecast by type of demand: Break your sales into
types and forecast each type of demand. For example, sales could rise rapidly (intermittent demand) but were
due to a promotion. A sales forecasting system will raise the forecast, while a demand forecasting system will
not because the regular type sales were the same; the promotion accounted for the sales rise.
80% of replenishment systems today use a sales forecast methodology, and, yes, some call themselves
demand forecasting. (see our blog Differences in Demand and Sales Forecasting methods).
Imagine you have a product that regularly sells one a month. At the end of the month, you have a BOGO sale
resulting in 4 sales for the month. This seemingly intermittent demand spike causes the sales forecast engine
to raise the forecast and the safety stock. Reviewing sales next month and next year, the total sales is not a
representation of regular demand. The total sales are representative of regular demand PLUS promotional
demand. This gets more complicated when you include closeout sales, seasonal sales patterns, and lost
sales. Demand Forecasting by sales type (regular, lost, promo, closeout) will flatten out many intermittent
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3. demand products.
Most buyers will fail to adjust the root cause, a bad forecast. Buyers will rely of their instincts and change a
PO which results in overstocks or out-of-stocks. A human cannot effectively track the hundred or more
products they manage through the supply chain with reports.
Demand Forecasting will deliver a more accurate forecast than a sales forecasting method due to better use
of the data. The result is fewer out-of-stocks.
Copyright © Data Profits, Inc. 2013 All Rights Reserved.
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Accuracy Sound
Interesting to You?
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about true demand forecasting.
Leadership in Retail, Wholesale, and Software
"Data Profits iKIS was developed after years of inventory consulting
with over 200 customers. Working with C level executives at many
retail and wholesale establishments, we developed our unique and
highly configurable BI dashboard, collaboration, and analysis software
platform which provides demand forecasting, replenishment, lead
time forecasting, optimization, and order management."
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