What are the key factors that influence your maintenance inventory demand forecasting?

Focusing on historical data as a key factor in maintenance inventory demand forecasting, analyzing past consumption of parts provides a clear view of future trends. By reviewing previous usage records, companies can identify frequently used parts, estimate average consumption rates, and recognize periods when demand for certain parts increases due to seasonal changes or operational factors. Leveraging historical data helps identify recurring patterns in failures or consumption, enabling accurate inventory planning and preventing shortages of critical parts during peak times. Additionally, this data serves as a foundation for performance gap analysis and identifying opportunities for improvements in logistics and parts storage operations.

Si bien se podrían utilizar los datos históricos como referencia, tal como se?alo en mi Curso de Gestión de Almacenes de Repuestos para Mantenimiento, se debería estimar la demanda de Repuestos, desde las Estrategias y Planes de Mantenimiento que se han definido y la Planificación de Mantenimiento de las actividades prevista para determinado período.

Historical consumption is useful for many items that "churn" relatively quickly, but of little help for what could be called, "insurance" items. Those are items that have very low demand and may never be used, but they are needed as "insurance" against downtime. Typically these are high value and long lead time items that would result in excessive downtime on the assets they serve, if they were unavailable. These items may also be highly engineered and have only one application, but without them the downtime risk is considered excessive. Probabilistic forecasts should be used for these items and they should also be protected from periodic clean up of non-moving inventory.

Based on my experience; here are the main factors: 1 - Maintenance strategies: A company with a predominantly reactive maintenance approach may experience higher variability in demand due to unexpected breakdowns 2 - Equipment standardization: Lack of standardization across equipment fleets 3 - Failure patterns: Recurrent failures that are not adequately addressed can skew demand forecasts 4 - Data quality & Historical Usage: Poor data quality and inaccurate historical usage records 5 - Supply Chain variability: Variability in supplier lead times and reliability can affect the availability of parts 6 - Operational Changes: Changes in operational context and ops intensity, can lead to shifts in demand for MRO materials

You can forecasting you spare part demand by analyzing the following key factors: 1.Equipment Criticality: High criticality equipment requires more attention to spare parts inventory 2.Historical Data: Provides insights into past usage patterns and frequency of breakdowns. 3.Equipment Age and Reliability: Older equipment typically requires more frequent maintenance and replacement parts. 4.Maintenance Strategies: The chosen maintenance strategy affects inventory demand. 5.Lead Times and Supplier Performance: Longer lead times may need higher inventory levels 6.Technological Changes: Upgrades, modifications, or changes in technology may give certain spare parts obsolete or introduce new parts into the inventory.

Equipment Criticality assessment is paramount here. You can do predictive but you need three factors to even be in control of the data you’re extracting from the sensors. What’s your PM compliance? What’s your OEE? What’s your maintenance Schedule Compliance? If any of those three factors are less than 60%, other things need to happen before throwing predictive into the mix because you’re probably reactive on a day to day basis. RapidRCS systems help you get there, but not until you’re in control of your asset management processes can you control your inventory that supports it. The only area that would help would be to understand the lead-time stock value to reduce your wait time for parts.

El estado del equipo incide en la demanda real de Repuestos, no sólo por el desgaste, sino también, por fallas que no se anticiparon y por problemas repetitivos que no han sido bien resueltos y corregidos. Cuando no se han definido y ejecutado las estrategias y planes de Mantenimiento adecuados, la Gestión de Repuestos se verá afectada, también. Sin duda, se debe realizar un seguimiento de las intervenciones de Mantenimiento, y estudiar las frecuencias reales de recambio de las partes, ajustando la planificación de la demanda en caso de ser necesario.

Equipment condition must include analysis of Medium time to repair (MTTR) and Medium Time Before Failure (MTBF), MTTR is a metric to see the maintenance efficiency but MTBF give you a idea about the reliability of the equipment. -You can't make decisions in a complete equipment fleet of 20 truck′s (for example) if 10 of them have 10k hours and other 10 are with around 20k operation hours. -Tries to standardize maintenance and high rotation components/parts -Collects and stores information about important individual components of each machine

Equipment Reliability and Maintenance Requirements: The reliability and maintenance requirements of equipment play a significant role in forecasting inventory demand. Equipment with higher failure rates or more frequent maintenance needs may require larger quantities of spare parts and materials to support timely repairs and minimize downtime. Equipment Criticality and Spare Parts Availability: The criticality of equipment and the availability of critical spare parts influence inventory demand forecasting. Essential equipment requiring specialized or unique spare parts may require higher inventory levels to ensure readiness for unexpected failures.

Condition is a challenging criteria to use in a dynamic sense. For items that "wear out" (deteriorate with age or use) we know that condition will degrade at a known rate and this can be used to forecast demand. However, most items that are needed have demand that arises randomly at any time. Condition monitoring is used for those, but the timing of when the condition will be at a point that requires replacement is seldom known with much confidence. As a rule of thumb I expect that with condition monitoring I'll find a problem on average at the historic "mean time between failure" for the item. Be careful however - that number is easy to calculate, but the required input data is often flawed.

Repair methods for each equipment (equipment maintenance strategy) matters. Many older plants with pump and valve repairs shops, now rely much more on repair by replacement of the equipment with an outsourced refurbishment provider. The spare parts in the warehouse are taking up space. An important step in preparing new capital asset projects is for maintenance to conduct strategy plans before spare parts planning. Buying spare part the OEM recommends that don't fit your maintenance approach is a waste.

The maintenance strategy adopted by an organization greatly influences its inventory demand forecasting. Depending on objectives, approaches such as preventive, corrective, or proactive maintenance dictate the type, quantity, and timing of required parts and materials. Preventive maintenance necessitates regular replacement of consumables and wear items, while corrective maintenance focuses on spare parts for unexpected failures. In contrast, proactive maintenance involves acquiring parts for improvement projects and upgrades. By aligning inventory forecasting with the chosen maintenance approach, organizations can effectively manage their parts inventory, reduce downtime, and enhance the reliability and performance of their assets.

Toyota's regular maintenance scheduling exemplifies strategic inventory demand forecasting. Their approach, rooted in the Just-In-Time system and Total Productive Maintenance philosophy, allows precise prediction of when specific parts are needed. This planned maintenance minimizes excess inventory and prevents stockouts by aligning parts availability with scheduled service intervals. For instance, knowing the mileage when brake pads typically need replacement helps forecast demand accurately. This proactive strategy ensures part availability for maintenance, enhancing vehicle reliability and performance, demonstrating Toyota's mastery in integrating maintenance planning with inventory management for efficiency and dependability.

Sin duda, que las estrategias, los tipos y planes de Mantenimiento que se han definido para los equipos e instalaciones, van a determinar la demanda de Repuestos a lo largo del tiempo. Según la Planificación de Mantenimiento elaborada, teniendo en cuenta los Planes de Mantenimiento con sus respectivas frecuencias de intervención, así como las tareas derivadas de los mismos, y la proyección de operación en el período.

It should be obvious, but for large, complex facilities, the PM schedule should provide a lot of the demand forecast. Take a large campus with multiple buildings, each with their own HVAC systems. If filter changes are scheduled in an EAM system, and they are relatively frequent, say every 3 or 6 months, it should be straightforward to calculate how many filters, and what type, are needed, and when. This can also drive standardization - why do we have 15 different filter sizes in one building?

Inventory policy should be tailored to the risk tolerance of the organization. Individual items will have impact on downtime if they are "out of stock" and that downtime has a cost that is often ignored by inventory managers. Maintainers, who ask for the spares, often don't pay much attention to it either. Greater collaboration than we usually see between most maintenance and inventory management departments is needed. To achieve the right balance of cost vs. risk.

Las Políticas de inventario que debe definir una Organización, están muy vinculadas a la planificación de la demanda de Repuestos, para que no exista riesgo de rotura de stock y así evitar sus perjuicios. Tal como abordo en mi Curso de Gestión de Almacenes de Repuestos, definir el nivel de Servicio es fundamental para la Planificación de la Gestión de Inventario, ya que implica el riesgo que la Organización está dispuesta a asumir.

Consider component similarities across assets and criticality requirements across the business. Constantly focus on creatin Bills of Material on all critical asserts.

Technology and Data Analytics: Leveraging advanced technologies and data analytics tools, such as predictive maintenance algorithms or machine learning models, can enhance inventory demand forecasting accuracy. These tools can analyze large datasets and identify patterns or anomalies to improve forecast accuracy and reliability.

N?o adianta ter políticas de inventário de MRO, se n?o tivermos bem estruturado qual é frequência de uso de cada material, e isso é muitas vezes subestimado, mas que pode ter um grande impacto na demanda do estoque. Ativos que s?o mais demandados operacionalmente tendem a apresentar desgaste mais rápido, exigindo reposi??o mais frequente de pe?as. Por isso, é importante estar atento a esse aspecto e ajustar a gest?o do estoque de acordo com a demanda real.

La pandemia fue un claro ejemplo de cómo pueden afectar factores externos a la demanda, no sólo a la Gestión de Repuestos para Mantenimiento, sino a la propia operativa de toda la Organización. Es fundamental monitorear los factores, para reducir los riesgos del Negocio.

Minimum and maximum levels of inventory are supposed to be set so that they account for lead times and consumption rates, but many inventory systems appear to treat these as if they are fixed rates, when they are in fact variable. Consumption can change and rarely does maintenance inform inventory management (they find out in historical consumption data long after the fact). Lead times may be updated with each time an order is placed, but for many items, it should probably be updated more frequently. Covid and its impact on lead times taught some rather painful lessons about this.

Very interesting topic, and the key elements mentioned above are all critical. And great comments as well. It all boils down to your operational context, if your downtime is extremely expensive, you will for sure invest in "insurance" components which have long lead times. And due to this fact, your inventory turn may be compromise, or even your inventory value will have a minimum value higher than other operations that don't have that profile. Based on that, I would suggest that "Downtime cost per hour" should be used as a normalization factor in order to compare different industries. Above the factors mentioned above I would add a Strategic Asset Management Plan could incorporate most of the factors and positively affect the forecast.

Me ha resultado útil lo siguiente; 1. Condición de los equipos a detalle, monitoreo del deterioro. 2. Data histórica dé mantenimiento, historial de fallas , Análisis del modo y efecto de la falla. 3. Planificación del mantenimiento. 4. Plan de acción generados de inspecciones, pruebas , ensayos no destructivos. 5. Gestión de riesgos de la gestión de proveedores, compras, servicios, incluyendo condiciones de entrega y calidad.

Regulatory and Compliance Requirements: Regulatory and compliance requirements, such as safety regulations or environmental standards, may dictate specific inventory needs or usage restrictions. Forecasting should consider these requirements to ensure compliance and avoid potential penalties or disruptions. By considering these key factors and leveraging appropriate forecasting methodologies and tools, organizations can develop more accurate and effective maintenance inventory demand forecasts, leading to optimized inventory levels, reduced costs, and improved operational efficiency.

Los factores clave que influyen en la previsión de la demanda de inventario de mantenimiento incluyen la variabilidad de la demanda, el ciclo de vida de los equipos, la estacionalidad, la disponibilidad de piezas de repuesto y la precisión de los datos históricos. Es esencial considerar tanto los patrones de uso pasados como las tendencias futuras para garantizar un inventario óptimo y evitar tanto la escasez como el exceso de existencias. La estandarización de los procesos de previsión de la demanda y la recolección de datos precisos son fundamentales para mejorar la precisión de las predicciones y optimizar el inventario de mantenimiento.