Spares Analysis

How modla's platform can predict spares usage and inform stock holding levels. Background and common questions included.

What is a spares analysis?

In essence, spares analysis is an inventory optimisation problem. Inventory optimisation aims to order and hold the right quantities of the right product to ensure demand is met while minimising inventory holding costs. Similarly, spares analyses endeavours to ensure spares and materials required to support operations are always available without holding excess stock. A spares analysis consists of three key considerations: 

  1. Demand forecasting,
  2. Business policy setting, and
  3. Replenishment

The three main concepts can be are explained using a simple sawtooth diagram. The figure below highlights three important stock levels.

Simplified sawtooth inventory diagram

The maximum stock level is governed by the business policy and accounts for factors such as:

  1. Consumption rates (determined from demand forecasting)
  2. Working capital
  3. Standard order sizes
  4. Cost and availability of storage
  5. Insurance costs
  6. Shelf lives

Similarly, the minimum stock level or safety stock level shields a business against fluctuations in consumption rates and lead times while balancing inventory holding costs and unavailability costs. 

Stock replenishment involves calculating reorder levels. The reorder level is based on the rate of consumption, order lead time and safety stock level. Stock replenishments becomes increasingly complex with long supply chains and unreliable suppliers. 

What is demand forecasting?

In reality, the consumption rate varies over time, contracting or extending the order cycle time. The variability of demand means demand forecasting is a science in its own right. Demand forecasting aims to predict the demand for a specific product accounting for nuances such as product seasonality and life cycles. The same considerations apply to forecasting the demand for spares and materials.

The likelihood of failure of an asset informs the demand forecast. It also varies over the life cycle of an asset as it ages, and as new information comes to light e.g. updated condition assessments or updated operational parameters.

Arguably accurate demand forecasting is the most important part of a spares analysis. Both the policy setting and replenishment considerations are heavily influenced by the demand forecast. 

How can modla help my business?

Our platform offers an easy to use solution to perform advanced asset analytics. One of the key outputs of all our analyses is a demand forecast for spares and materials related to both reactive and proactive maintenance activities. 

Our platform derives an accurate demand forecast by accounting for the environmental conditions, operational attributes, and asset characteristics unique to your assets. Furthermore, you can link a Bill of Material (BOM) to any asset to get a detailed breakdown of spares and material consumption.

Our platform is not a warehouse management tool or an Enterprise Resource Planning Tool (ERP), and therefore does not facilitate ancillary analyses such as business policy setting or replenishment. 

However, our powerful asset analytics solves a key piece of the puzzle by producing tailored demand forecasts conscious of your business context and specific assets.