The Advantages of Probability Forecasting
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The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Most demand forecasts are partial or incomplete: They provide only one single number: the most likely value of future demand. This is called a point forecast. Usually, the point forecast estimates the average value of future demand.

Much more useful is a forecast of full probability distribution of demand at any future time. This is more commonly referred to as probability forecasting and is much more useful.

The Average is Not the Answer

 

The one advantage of a point forecast is its simplicity. If your ERP system is also simple, the point forecast fills in the one number needed by the ERP system to do workforce scheduling or raw material purchases.

The disadvantage of a point forecast is that it is too simple. It ignores additional information in an item’s demand history that can give you a more complete picture of how demand might unfold: a probability forecast.

Going Beyond the Average: Probability Forecasting

 

While the point forecast provides limited information, e.g., “The most likely demand next month is 15 units”, the probability forecast adds crucial information, e.g., “There is a 20% chance that demand will exceed 28 units and a 10% chance that it will be less than 5 units”.

This information lets you do risk assessment and contingency planning. Contingency planning is necessary because the point forecast usually has only a small chance of actually being correct. A probability forecast may also say “The chance of demand being 15 units is only 10%, even though it is the single most likely value.” In other words, there is a 90% chance that the point forecast is wrong. This kind of error is not a mistake in the forecasting calculations: it is the reality of dealing with demand volatility. It might better be called an “uncertainty” than an “error”.

An operations manager can use the extra information in a probability forecast in both informal and formal ways. Informally, even if an ERP system requires a single-number forecast as input, a wise manager will want to have some clue about the risks associated with that point forecast, i.e., its margin of error. So a forecast of 15 ± 1 unit is a lot safer than a forecast of 15 ± 10. The ± part is a compression of a probabilistic forecast. Figure 1 below shows an item’s demand history (red line), point forecasts for the next 12 months (green line) and their margins of error (cyan lines). The lowest forecast of about 3,300 units occurs in June, but the actual demand might be as much as 800 units higher or lower.

Bonus: Application to Inventory Management

 

Inventory management requires that you balance item availability against the inventory cost. It turns out that knowing the full probability distribution of demand over a replenishment lead time is essential for setting reorder points (also called mins) on a rational, scientific basis. Figure 2 shows a probability forecast of total demand during the 33 week replenishment lead time for a certain spare part. While the average lead time demand is 3 units, the most likely demand is zero, and a reorder point of 14 is needed to insure that the chance of stocking out is only 1%. Once again, the average is not the answer.

Knowing more is always better than knowing less and the probability forecast provides that extra bit of crucial information. Software has been able to supply a point forecast for over 40 years, but modern software can do better and provide the whole picture.

 

 

Figure 1: The red line shows the demand history of a finished good. The green line shows the point forecasts for the next 12 months. The blue lines indicate the margins of error in the 12 point forecasts.

 

 

Figure 2: A probabilistic forecast of demand for a spare part over a 33 week replenishment lead time. The most likely demand is zero, the average demand is 3, but a reorder point of 14 units is required to have only a 1% chance of stock out.

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Too Much or Too Little Inventory?

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Do you know which items have too much or too little inventory? What if you knew? How would you go about cutting overstocks while still ensuring a competitive service level? Would you be able to reduce stockouts without incurring a prohibitively expensive inventory increase? How would these changes impact service levels, costs and turns—for individual items, groups of items and overall?

Most companies know they have too much or too little inventory but lack a key ingredient for optimizing inventory: Service Level-Driven Demand Planning. To take action, you must know how much inventory is needed to satisfy the service level you require. More fundamentally, you need to know the specific service level that will result from your current inventory policies, the gap to be addressed and its financial implications.

Many organizations, especially those with intermittent demand, find this to be an exceptionally challenging trial and error process.

Moving to a service level-driven approach will overcome this challenge and ensure that rebalancing inventory improves service level performance at a lower cost. Start with the most accurate demand forecast possible, calibrate for forecast risk and then determine your optimal inventory position. In a recent webinar, I demonstrated Service Level-Driven Demand Planning and how SmartForecasts can be used to drive this process:

1. Measure the service levels that will be achieved at current inventory levels and with your current inventory policy.
2. Identify items that will achieve high service levels (98%+) but at prohibitively high cost.
3. Identify items that are at high risk of stockout (service levels < 75%).
4. Run multiple what-if scenarios based on a different prioritization of service levels by item or item groups. Choose the scenario that optimizes financial constraints with service objectives.
5. Quantify cash savings from reducing overstocks and the costs to increase inventory when service levels are unacceptably low.
6. Take action to establish new service level-driven reorder points, order quantities and inventory levels to meet your service targets and budget.

To view the webinar replay, please click here and complete the registration request.

Gregory Hartunian serves as President of Smart Software and as a member of the Board of Directors. A graduate of The F.W. Olin School for Business at Babson College, he formerly served as Vice President, Sales and Operations.

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Forecasting With the Right Data

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

In order to reap the efficiency benefits of forecasting, you need the most accurate forecasts—forecasts built on the most appropriate historical data. Most discussions of this issue tend to focus on the merits of using demand vs. shipment history—and I’ll comment on this later. But first, let’s talk about the use of net vs. gross data.

Net vs. Gross History

Many planners are inclined to use net sales data to create their forecasts. Systems that track sales capture transactions as they occur and aggregate results into weekly or monthly periodic totals. In some cases, sales records account for returned purchases as negative sales and compute a net total. These net figures, which often mask real sales patterns, are fed into the forecasting system. The historical data used actually presents a false sense of what the customer wanted, and when they wanted it. This will carry forward into the forecast, with less than optimal results.

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