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Monthly Archives

March 2013

Why Lead Time impacts your Inventory Optimization and How To Fix It

What is Lead Time? Why Is it Important?

Lead Time is the length of days between when an order is placed and the date the goods are available for use. The largest impact to lead time accuracy is found by comparing expected receipt date to actual receipt date for each purchase order. In simple terms, the variance is calculated as the absolute value of the difference [expected or requested receipt date – actual receipt date] for each line on the purchase order. These variances in days across multiple purchase orders establish the need for lead time accuracy testing and lead time forecasting.

What is the Impact When Supplier Lead Time is Not Accurate?

Suppliers provide an estimate of lead time, but these numbers are not always accurate. The differences between your expected receipt date and actual receipt date can become expensive from the resulting unplanned over stocks, out of stocks, and deflated consumer opinions. Lead time tracking and lead time forecasting are mission critical to the success of your supply chain. Lead Time Forecasting, like Demand Forecasting, should use a set of math algorithms to calculate the correct lead time days to use in planning purchase orders. Also, like Demand Forecasting, the Lead Time Forecast should move up and down according to changes in market, business influences and seasonality of product.
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Differences between Demand Forecasting and Sales Forecasting for Inventory Replenishment

Sales Forecasting is the wrong tool for inventory replenishment and inventory planning. Sales Forecasting, by its very name itself, is a measure of total sales. In our last article, we discussed that the key difference between sales forecasting and demand forecasting is whether (or not) sales data is broken out into type of sale, analyzed, and the results input into the forecasting algorithms. Sales type might include any or all of the following: regular, lost, promo, event, and close out sales. Without knowledge that sales went up or down due to market factors like out of stock and promotions, a sales forecasting system will forecast based only on the total sales. This may not be the intended goal for inventory replenishment or inventory management.

Key Limitations to Sales Forecasting

Sales Forecasting, by its very nature, doesn’t know why sales rise or fall and cannot connect events to sales behavior. For example, when sales were down 20% four weeks ago, you probably knew this was due to constrained supply which created out of stock issues. The sales forecasting system will react to the 20% drop by lowering the forecast. The resulting inventory replenishment orders from the new forecast will be low, creating a repeat scenario of lost sales again next month.
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Differences between Demand Forecasting and Sales Forecasting for Inventory Replenishment
Demand Forecasting and Sales Forecasting are different, and the results of each can have a dramatic impact on your profitability. Demand Forecasting and Sales Forecasting should be calculated with some similar and some different data points. While closely related, the two resulting forecast numbers will not be the same in most business situations. The forecast results will impact the inventory replenishment by impacting available inventory, expected inventory orders, and sales. An inventory replenishment system that is based on a demand forecast (demand driven) can reduce the risk of lost sales while improving service. This in turn delivers higher sales by connecting inventory levels with demand forecast.


What is Sales Forecasting

Sales Forecasting is the easier of the two choices: you load your sales history into the sales forecast engine and the system delivers a sales forecast. Sales Forecasting is critical for the retail business to create financial plans with the banks, plan sales growth, and plan resource strategies. Sales Forecasting systems have a ‘vanilla’ approach that is clean and simple, and it works without issues for the most basic of products. Legacy systems often will pair the sales forecasting with their demand planning tools to determine inventory replenishment for the business. Read More

Seasonal Index Lessons from History

Reviewing Seasonal Indexes is critical for an accurate demand forecast. Seasonal Indexes, also called seasonal multipliers, are used to adjust the demand forecast by multiplying the product base forecast by a multiplier. The effect will raise or lower the demand forecast for the time period, often a week or month. The results are often used to help calculate the inventory needed to support sales. Holidays like Easter, seasons like springtime, and events like the Super Bowl that repeat based on some factor of time are frequently better serviced with a seasonal index applied across the year.

Problems with Seasonality

The problem with seasonality is that it can change each year, and your current fiscal year may not map back to your seasonal index. Sales from the last seasonal event may impact your base demand forecast to create inaccuracy. Easter is in a different month and fiscal week this year. How did you account for the differences when purchasing Easter inventory for this year? Next year, Easter is again in a different month and will be several weeks different from this year. Thanksgiving is a holiday that based on the time of year can add or subtract a whole weekend of December holiday shopping. There are two issues that need to be reviewed and adjusted: the current year fiscal week seasonal index values and the base demand forecast. Read More