Monetizing Data in Maintenance: Data-driven Spare Parts Management
Data-driven Spare Parts Management
Organizations today maintain huge amounts of data, structured or unstructured. However, from research of renowned organizations like Gartner, we know that industrial firms today are not able to use 70—90 percent of data that are collected and stored. This paradox is described in this article, and various generic models of big data monetization are proposed. Some of these models are presented as examples from spare parts management.
Spare parts inventory can lock in significant amounts of workingcapital. This article summarizes recommendations for effectivespare parts inventory management and spare parts optimizationusing various sets of data and statistical analytical methods. In Maintworld 3/2020 -magazine we will continue on the topic.
Management of spare parts and other materials needed for realization of maintenance processes is one of the key functions in physical asset management. Especially in power generation, oil and gas and heavy chemical industries, spare parts inventories can easily add up to tens of thousands of various items, in a value of hundreds of millions of euros.
It is obvious that efficient spare parts inventory management can have significant impact on the financial performance of the company. Better spare parts management can lead to improvement of financial performance of the company. In previous research we discussed several recommendations for spare parts inventory management. Using these recommendations, companies can achieve better financial performance in different parts of the spare parts lifecycle. In some of these recommended practices, various data can be employed and analysed – especially in areas like portfolio segmentation, criticality assessment, forecasting, improving spare parts naming and identification, or cleaning and rectifying master data.
Eight Rules of Good Spare Parts Management
- In our previous research, we refined the following eight rules – best practices – for good spare parts management:
- Focus on preventative maintenance – for preventative maintenance no inventories of spare parts need to be held.
- Solve problems in spare parts processes.
- Segment your spare parts portfolio.
- Evaluate spare parts criticality.
- Use suitable forecasting methods and verify their accuracy and reliability.
- Use special methods for intermittent demand items.
- Consider the whole lifecycle of your assets while making decisions related to spare parts.
- Implement a good information system for spare parts management so all above stated rules are supported and/or automated.
- Some of these rules are described in detail in the following chapters.
Each Item is Different: Segment your Spare Parts Portfolio
In almost any inventory, different groups or segments of items can be identified. The primary objective of segmentation is to effectively divide an extensive portfolio of items on stock into separate groups requiring a different inventory management system, approach in planning, or specific optimization methodology. A good spare parts management information system allows for carrying out such analyses and portfolio segmentation quickly and easily, including visualization of outputs.
For inventory segmentation, several methods and criteria can be applied:
ABC analysis of inventory based on quantity and value available on stock (Fig. 1) and other criteria
ABC analysis according to item consumption (Fig. 2)
Segmentation based on frequency of consumption (identification of slow-moving inventory) in quantity or value (Fig. 3)
ABC analysis according to item criticality
Categorization based on item accessibility (common, special, made-to-order)
Identification of intermittent items (special test of intermittent demand)
Segmentation based on suppliers’ lead-times
For ABC analyses, in case of spare parts, the prevalence of categories C and D (items with low or zero consumption in long-term history) is very typical.
Using segmentation based on consumption frequency, slow-moving inventory (SMI, items with minimum turnover, including “dead stock”) items can be promptly identified (Fig. 3). For spare parts, the 0 segment is usually the most important.
This segment covers items with no consumption record in the past 12 months. Segment 0 is generally the most significant both in quantity and in value. It includes items of strategically important and critical spare parts – items with the highest value in the portfolio. Other segments with low frequency of demand are also significant (segments 1, 2, etc.). Segments with frequent consumption (segments 10, 11, 12) contain items of fasteners with relatively low value (Fig. 3).
The segmentation may also include specification of links between spare parts and appropriate production equipment (technical site). Bills of material, obtained in this way, make it possible to closely trace spare parts consumption for individual parts of production equipment, measure costs in each stage of the production equipment lifecycle, and identify critical spare parts in relation to the criticality of production equipment.
For each identified inventory segment (or for each individual item, if possible), the required level of availability (service level) needs to be specified. The desired logistic service level is closely related to the spare part’s criticality: for highly critical items a service level of, for instance, 99.7 percent will be required. Obviously, there is a trade-off involved: the higher service level that is required, the higher minimum level of inventory is needed.
Today, the emerging digital technologies empowered by Artificial Intelligence (AI) are transforming the Swedish mining industry where failure is not an option owing to severe downtime costs. Such costs can be as high as 30-40 percent of the total equipment operating costs