1. Inventory demand forecasting is the process of predicting customer demand for an inventory item over a defined period of time.
2. Accurate inventory demand forecasting enables a company to hold the right amount of stock without over or under-stocking, for optimum inventory control.
3. Historical data trends and market knowledge of how demand can fluctuate are often used to forecast inventory demand.
4. Forecasting inventory can be as basic or as complex as you make it.
5. As a simple rule, the more sophisticated your inventory forecasting techniques, the more accurate your predictions will be.
6. Accurate demand forecasts allow you to efficiently serve customers’ needs without investing capital in large amounts in stock, effectively helping you lower your overall operational costs.
7. However, inventory forecasting can be challenging to undertake without the right guidance, so starting with basic forecasting techniques is advisable.
TOP DEMAND FORECASTING TECHNIQUES
1. Use demand types
2. Identify trends
3. Adjust forecasts for seasonality
4. Include qualitative inputs
5. Remove ‘real’ demand outliers
6. Account for forecasting accuracy
7. Understand your demand forecasting periods
8. Consider demand forecasting software
INVENTORY FORECASTING - DEMAND TYPES
1.If you analysed the historical sales data of every product in your warehouse, you’d find that the demand for different items varies considerably.
2. Some will have consistently high demand over time, for others there could be sporadic or low demand.
3. In addition, as products move through their product lifecycle, from market entry, to maturity and decline, their demand types will keep changing.
4. An item’s demand type is important as it should be used to determine the type of calculation (or algorithm) you use for forecasting. It makes statistical sense to use a different equation to calculate the demand of a product with an erratic demand type, to one with slow demand.
5. Calculating your base demand is just the start of producing accurate demand forecasts. Below is an example of the different demand factors that can impact or inflate your normal base demand.
INVENTORY DEMAND TRENDS
1. The demand for your inventory items will change as fashions change, new technologies replace old and social, economic and legal factors influence demand.
2. Items will also follow demand trends as they move through the product cycle. For example, in the growth phase, the trend in demand will be upwards, whilst in the decline phase, the trend will reverse.
3. Make sure you look out for such trends in your historical demand data and adjust your inventory forecasts accordingly. There’s no point creating a forecast based solely on your base demand if items are following a specific trend.
FORECASTING DEMAND FOR SEASONAL ITEMS
1. Understanding how seasonal factors affect your customers’ purchasing habits will help you take advantage of peaks in demand and plan for the troughs.
2. Best practice is to keep seasonal demand factors separate from your base demand calculations. This keeps the data clean and easier to use for forecasting going forward.
QUALITATIVE INPUTS
1. Whilst historical data (quantitative demand forecasting) provides a great basis for achieving demand forecasting accuracy, sometimes you’ll also need to consider more qualitative factors.
2. Qualitative demand forecasting includes accounting for future events and external market factors, such as sales promotions and competitor activity.
3. Make sure you input any sales and marketing insights you have into your forecasts to make them as accurate as possible.
INVENTORY DEMAND FORECASTING OUTLIERS
1. Unusual demand outliers can be the result of known actions (sales promotions, large one-time orders, employee strikes etc) or unpredictable events (a competitor going out of business, natural disasters etc).
2. Take the time to analyse your inventory forecasting data to detect outliers, as they can significantly skew the accuracy of your forecasts.
3. Any demand data – high or low – outside of the reasonable standard deviation of average demand needs to be identified.
4. You then need to make a judgement call on whether it should be included in your demand forecasting calculations (if it’s part of a trend) or not (if it’s an anomaly in demand).
UNDERSTANDING DEMAND FORECASTING ACCURACY
1. Your demand forecasts are very unlikely to be 100% accurate. So, if you can calculate the level of error in your previous demand forecasts, you can factor this into future forecasts.
2. If you can determine how uncertain a forecast is for a given business period you can make the necessary adjustments to your inventory management rules, such as increasing safety stock levels to cover uncertain periods of demand.
3. There are many formulas to help you measure demand forecast accuracy, or forecast error. The Mean Absolute Percent Error (MAPE) will calculate the mean percentage difference between your actual and forecasted demand over a given period. Whilst the Mean Absolute Deviation (MAD) shows the deviation of forecasted demand from actual demand in units.
DEMAND FORECASTING PERIODS AND REVIEWS
1. The time period you choose for your demand forecasting has a direct impact on the accuracy of your forecast. For example, a forecast looking at your inventory’s demand over the next two weeks is much more likely to be accurate than a forecast that looks 12 months out.
2. In addition, if markets are volatile, or an item’s demand pattern is erratic, you’ll need to review your forecasts on a much more regular basis than in slow markets or for slow moving products. If you begin to experience stock outs or see cases of excess stock, then you may need to adjust your forecasting intervals.
LAW OF BIG NUMBERS
1. One problem that some companies have with sales forecasting is the sheer number of products, or variants of products, that they sell.
2. Generating useful forecasts for potentially thousands of products is a task that, for many, would range from the impractical to the impossible and the fact that some items only sell in small numbers adds to the problem.
3. Facilitating useful forecasts in these circumstances is something that ERP, and more-specifically planning bills of materials, can help with.
4. “The Law of Big Numbers” says that big numbers are easier to estimate. As an example, imagine that a car maker has a particular model and that it comes with a number of choices. Customers can have:
a 1.6 or 2.0-liter petrol or 1.9-liter diesel engine
a 2 or 4 door, or a station wagon body shell
in silver, white, red or black paint
with upholstery in black, grey or brown.
5. Few car makers are likely to know how many 1.6 liter, four-door cars in red with gray upholstery that they are going to sell next week. But they might have a reasonable idea of how many cars in total that they are going to sell and experience might tell them that overall:
20% of the cars that they sell use the 1.6-liter engine, 45% the 2.0, and 35% the diesel,
20% will be the 2-door, 65% the 4-door, and 15% the station wagon,
etc, working down the choices list.
6. If they create a composite (planning) bill of material that reflects those percentages, and then feed in a forecast at the model level of, say 10,000 cars, the MRP system will tell them that they need:
10,000 x 20% = 2,000 1.6 litre petrol engines
10,000 x 65% = 6,500 2.0 litre petrol engines
10,000 x 15% = 1,500 1.9 litre diesel engines
10,000 x 20% = 2,000 2-door body shells
10,000 x 65% = 6,500 4-door body shells
10,000 x 15% = 1,500 station wagon body shells,
7. They will never build a car with 0.2 of a 1.6 litre petrol engine, 0.65 of a 2.0 litre petrol engine etc, but the point is that they will have provisioned, reasonably accurately, for actual orders when they come in.
8. From that perspective, it doesn’t matter how all of the options go together on any individual car because that is not important from a planning perspective. But they will know that the items that they have procured are likely to satisfy production demand. By moving the forecast to a higher level, they are dealing with bigger numbers and getting better, safer and more reliable results.
9. This example has used a car with multiple options as being something easily understood but the basic idea has been applied in manufacturing industries as different as furniture and PC manufacturing. Any company that has subassemblies or intermediates that are used in several end products may gain benefit from it. The key to successful demand forecasting for manufacturing can often be in realizing that it is not always necessary to forecast at the finished item level.
FORECASTING AT INTERMEDIATE, SUBASSEMBLY LEVELS
1. Some manufacturing companies can go further than this because forecasting sales at an intermediate, or subassembly, level opens up the possibility of stocking at that level also and that potentially offers two enormous advantages.
2. Firstly, for make-to-order companies, holding stocks of subassemblies rather than raw materials can significantly reduce manufacturing lead times and that allows some companies to reduce their delivery lead times to customers substantially as well (the difference between production lead time and delivery lead time will be discussed in a future article).
3. For those that make-to-stock it can remove the frustration of looking in the finished goods warehouse and seeing urgently needed subassemblies that have been built into items for which there are no orders.
4. It's not always physically possible to disassemble unwanted stock to rob components for high priority orders, even if the required labor can be made available.
Source:
https://www.eazystock.com/uk/blog-uk/8-best-inventory-demand-forecasting-techniques/
https://www.erpfocus.com/sales-demand-forecasting-erp.html
