Business forecasting 101
Difficult Forecasts 1: Spare parts forecasting
Regardless of the difficulty, achieving the optimal size and configuration of spare-parts inventory is critical. Too little and you run the risk of costly or even fatal operational delays. Too much and you tie up cash in spare-parts safety stock — carrying costs kill too.
The most accurate forecasting methods for spare parts are those able to handle intermittent demand, which is characterized by randomness and long periods of zero demand. Indeed, spare-part usage often vacillates between erratic and zero. There are many ways to get around this lack of obvious recurring patterns. One popular method is Croston’s intermittent demand model. A more advanced approach is Monte Carlo simulation, which uses probability theory to model the time between orders, as well as order sizes. Other approaches include increasing the forecast time scales from daily to weekly to monthly and so on, to reveal more stable and predictable patterns.
In all cases, optimizing spare parts inventory should take into account a number of key factors — Is the part expensive? Is it critical? What is your storage capacity? What is the lead time for purchase/manufacturing of this part? Which parts should be ordered on demand, versus kept in stock?
Difficult Forecasts 2: New product forecasting
New product forecasting is one of the greatest challenges that forecasters face. With limited historical data, domain knowledge becomes vital. The following describes methods forecasters can use to augment their own judgment.
All of the following methods are available in Vanguard Predictive Planning.
Used when there is a new product that is similar to another established product. The comparable product’s historical patterns and trends help shape the new product’s forecast. Comparable Forecasting, however, does not capture new product launch effects.
In Vanguard Predictive Planning: Users can select comparable products by any attribute, such as product family, SKU code, region, and more.
A spread curve takes the pattern of a demand factor (e.g. seasonality) and applies it to the forecast of a new product.
In Vanguard Predictive Planning: A user with domain expertise can select the likely demand effects that will impact a new product and apply them to the forecast. Effects can include seasonality, decay, launch time, life cycle, region, and more. Vanguard works with a client’s domain experts to discover, test, and isolate the effects of key demand factors.
Used when a new product is likely to cannibalize or replace an existing product or set of products. Supersession is especially useful for technology products or certain seasonal items that have multiple iterations.
Used to capture new product launch effects. New products garner increasing sales at first. Sales then slow as products mature, moving from the early adopter phase toward market saturation. Adoption modeling is typically used in long-range forecasting, or in cases where product-life-cycle effects are prominent. It does not capture effects such as seasonality or cannibalization.
Combination of methods– Unique to Vanguard Predictive Planning
Vanguard Predictive Planning can combine all the above new product forecasting methods so that a single new product forecast can finally include new product adoption curves, comparable products, demand effects via spread curves, and cannibalization effects.
Difficult Forecasts 3: Using comparables forecasting
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