We've all heard the arguments about forecasting in a maintenance environment. The common question is "Who are you kidding? No one will be able to predict maintenance requirements. How will a forecast be useful when no one has any idea of tomorrow's workload?"
In reality, a forecast greatly simplifies deciding what, when and how much to order. Forecasting provides an opportunity to respond proactively and resolve material and scheduling problems. In fact, improved material planning, scheduling and supplier coordination raises department utilization, shrinks inventory levels and enhances customer service.
APICS, the Educational Society for Resource Management, defines forecasting as "the business function that attempts to predict the use of products so they can be purchased or manufactured in appropriate quantities in advance." A forecast is "an estimate of future demand. A forecast can be determined by mathematical means using historical data, it can be created subjectively by using estimates from informal sources, or it can represent a combination of both techniques."
A company derives benefit from a maintenance forecast, especially companies operating in a run-to-failure maintenance mode, waiting for a breakdown before procuring material. The goal of maintenance forecasting is to have the right material available at the right time, in the right place, in the right quantity without tying up inventory dollars or tolerating material shortages. A maintenance department with a forecast better serves internal customers by anticipating maintenance requirements and having material available to meet these needs. The forecast helps to respond to emergencies and other scheduled maintenance activities quickly.
Maintenance forecasting is essential to maintaining a high level of operational readiness. Forecasting can help balance the cost of carrying high inventory levels on every item against numerous rush orders to support maintenance activities and its potential for causing downtime.
Two basic forecast principles are there's no such thing as a reliable forecast and one should expect the forecast to be wrong. Tracking and using forecast bias (the difference between forecast and actual) to calculate a standard deviation for the error improves forecast effectiveness. Use the standard deviation to calculate safety stock levels that cushion demand variability. Forecast principles also state that current-period forecasts are more accurate than later periods. A general understanding of these basic principles should help users improve their ability to forecast.
The maintenance department forecast should help predict repair frequency on the basis of qualitative or quantitative measures. Qualitative forecasts reflect a person's intuition, make use of informed opinions and tend to be subjective. Qualitative forecasts are used in the absence of historical data. For example, a new machine won't have a repair history. The forecast will be based on the manufacturers' suggested maintenance cycle and parts list. The maintenance department also will forecast other material critical to the operation performance. Qualitative techniques include:
- Delphi method
- Market research
- Panel consensus
- Historical analogy
The quantitative technique uses historical demand data to calculate a future forecast. This technique assumes the demand pattern will be repeated in the future. For example, if the maintenance department used 1,000 gallons of oil each month, a quantitative forecast will project a demand similar to past demand. This logic remains valid as long as no structural changes appear. For example, replacing a machine with a new model alters the underlying structure of the forecast.
Quantitative techniques include:
- Historical time series
- Causal studies
- Simulation models
This quantitative technique will help to identify characteristics such as trend (demand change), seasonality (use varying regularly over time), randomness (one-time occurrences), or cyclicality (time interval).
The first step in producing a forecast is to capture monthly or weekly usage data going back one to three years. Forecast accuracy improves with the number of periods for which data is captured. Collect records such as maintenance work order number, material and quantity used; issue date, and reason code. In addition, demand activity may require user-specified adjustments or modifications to accommodate material substitutions, resource constraints, equipment replacement, cancelled work orders and the like. Failure to validate and correct demand data skews forecasted quantities.
The second step is to determine the number of maintenance items to include in the forecast. Large numbers are not practical. Use a stratification process to identify items to be forecasted based on cost, frequency of use or lead-time.
The third step is to produce a bill of material for repairs to identify the parent item, along with the components required to perform a preventive or scheduled repair. For example, the engine repair kit defines the material used historically to make the repair (see Table 1 below). Place these items on a repair bill-of-material, along with a defined replacement factor (percent of time the repair used the part). The engine repair kit then will be forecasted based on historical use.
|Level||Item #||Description||Qty||UOM|| Replacement|
|0||A12345||Engine Repair Kit|
|1||D77654||Spray Paint Can||1||Ea||1.00|
The fourth step is to control non-forecasted items through the explosion of the repair bill-of-material or, for items of limited use, on a min/max basis. Base this decision on the material's cost and the impact of not having material available. Other stocking considerations include long lead-times, critical need, or the desire to stock just-in-case.
The fifth step is to generate the forecast item record, including item number, description, planning data, ordering data, cost data, 36 or more periods of demand history, and so forth. Although it's quite laborious and time consuming, analyze, massage and clean item data records before executing the forecast.
The sixth step is to run the forecast system and order material based on the forecasted quantity and period required.
The seventh step is to monitor the forecast error. Take time for periodic reviews to validate the basic assumptions and data used in developing the forecast to ensure the forecast process remains on target. Failure to perform this function leads to a poor forecast and a subsequent increase in inventory level.
The final step is to have management review and adjust the forecast to ensure the results meet strategic business plans and objectives. A computerized maintenance management system (CMMS) assists in forecast development by providing information that helps analyze and monitor the need for repairs, their planning and scheduling, repair orders, and to track demand. The CMMS output enhances the forecast process.
Forecasting can assist in material planning (see Table 2 below).
| Maintenance |
|Ability to Forecast|| Inventory |
|Setup Supplier |
|RepairBills of Material|
For example, predictive maintenance (PdM) seeks to forecast quantities for materials identified through nondestructive testing or statistical analysis. Materials to support these efforts need to be forecasted on the basis of historical use.
Preventive maintenance requirements, such as replacement machine parts and cleaning supplies, are forecasted on the basis of a user-defined, time-based, meter-based, or calendar-based schedule. Integration to/from enterprise resource planning, manufacturing execution systems or CMMS systems helps to define the maintenance time frame.
Scheduled maintenance activities are forecasted on the basis of management input. For example, management dictates the maintenance operations during a plant shutdown by specifying the repair activities, developing bills of material and identifying material requirements to support the plan.
Unplanned maintenance is required whenever something fails unexpectedly. Even so, limited material use forecasting is possible on the basis of historical demand for motors, gauges or manufacturers' suggested parts. However, most unplanned requirements require placing orders immediately. Establishing a minimum quantity for selected items is the best way to control these inventories but the uncertainty involved makes the holding cost expensive.
Forecasting won't resolve every material-related problem in the maintenance area, but it is a proactive approach. Understanding demand patterns adds place and time value, which results in improved material planning and workload scheduling. Forecasting improves supplier negotiations and setting up a supplier stocking program. This effort reduces equipment downtime, reduces maintenance inventory levels, increases equipment utilization and permits better scheduling of maintenance workers.
As president of PIM Associates Inc., Howard Forman specializes in supply chain process improvements, inventory reduction and education. He can be reached at PIM Associates, 42 Hilltop Terrace, Bloomingdale, NJ 07403; phone: (973) 838-5946; e-mail: firstname.lastname@example.org.