The Top 4 Issues in Spare Parts Management Today

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8 minutes

Spare parts are crucial to many business’ operations. However, as the Econometrics Institute highlights - and our own experience shows - they are also very expensive. Indeed, research shows that investment in spare parts can account for 5% to 10% of a company’s investment base. In traditional manufacturing, entire machines are produced in high volumes in order to lower overall costs. However, as spare parts are made separately and in smaller volumes, they are inevitably much more costly. Long lead times also mean that businesses often stock up on these expensive spare parts to avoid the high price of downtime. We’ve seen firsthand how this can be a lose-lose situation: companies rack up storage costs for parts that are rarely used or end up becoming obsolete.

Many machine operators find that their spare parts operations are nowhere near as optimised as they need to be, but, in our experience, most struggle to pinpoint exactly why. Of course, with an interconnected value chain of multiple actors and processes, it can be hard to identify what needs to change. But often, the truth is that companies aren’t managing their spare parts effectively, if at all. They are merely buying and stocking spare parts with no data-driven strategy to guide these decisions. In this article, we’ll use our knowledge of the industry to help you pinpoint issues within your spare part operations, so you can get your house in order and save money in the future.

4 Main Spare Parts Management Challenges

Issue #1: Low Data Accessibility and Quality

Our work with machine operators tells us that most spend a lot of time and resources on sourcing spare parts. The main reason for this is usually outside machine operators’ control. Suppliers frequently don't provide sufficient data — for example, how many of a spare part they have or how quickly it could be delivered at any given moment. When they do, machine operators don’t always have the right tools in place to process it.  Speaking with countless operators, we’ve seen that a hefty proportion of them are still not using sufficient digital tools to manage spare parts, resulting in messy excels and typically low update rates. Indeed, many are still even using half-manual processing, requiring operators to ingest many separate files. This means that even when the data is there, it’s very difficult to glean useful information from it. From mis-categorising or adding incomplete spare parts, to missing attributes and naming the same parts differently, any number of small errors can render a data set useless. 

On the other hand, lack of digital penetration is not always the problem. If managers are using too many tools, it can be hard to keep data consistent across different departments and sites. This is exacerbated if responsibility for managing parts is also split across various departments.

When it comes to data, it’s usually not a question of doing something wrong, but rather not knowing how to do it better. A 2017 report by Oliver Wyman notes that the right digital stock management tools can optimise traditional aftermarket processes like spare parts by analysing and continuously monitoring collected data. Picking the right kind of spare parts management tool can, therefore, go a long way to solving the data issue.

Issue #2: Low Penetration of Prediction Tools

Through our work with operators, we’ve seen that it’s difficult to know exactly when a machine will break and which parts will need to be replaced. To do so requires probabilistic modelling — in other words, mathematical models that predict spare parts usage. After years of research and the rise of big data and artificial intelligence, there is a whole range of effective predictive modelling tools out there, like BlockSim. They allow spare parts decision-making to be data-driven, rather than intuition-driven. This saves time and money, allowing spare parts managers to focus on the core aspects of the business. 

Yet, despite the proven benefits of these tools, there remains a significant gap between research and practice in spare parts management. Our experience has shown us that  estimating demand for spare parts is still commonly based on guesswork with machine operators using intuition instead of the right tools to predict demand. Without mathematical prediction models or access to quality data, some machine operators might not even be aware of vital information, such as the quantity of certain parts they have stored. Blind spots in data and an intuition-based prediction model result in inefficient operational processes and value losses.

Issue #3: The Time Old Problem of Silos

Silos are a problem faced by companies from any industry, but when it comes to spare parts management, in particular, they are the true enemy of efficient, centralised processes. Within spare parts management, we’ve been able to identify two kinds of silos through our work. The first: silos across machine operators’ different sites. In other words, spare parts management might be undertaken individually at each site, not across the whole organisation. The second: silos across the supply chain as a whole. In other words, machine operators struggle to gain an overview of the whole market in order to know the closest and quickest supplier from which to source a spare part. This means, firstly, that data cannot be easily shared or aggregated, meaning that spare parts managers cannot make accurate decisions about how many spare parts they need. In turn, this lack of centralisation and transparency means that they cannot optimise procurement procedures.

To tackle the first issue — and become more time and cost-efficient — spare parts managers should work together and look across their whole organisation to source potential spare parts. In other words, they could pool their parts through a cross-site or cross-organisational system. Combining this within a system, like Sparrow, that also contains external data about spare parts sources and intelligent data analytics to facilitate better planning, would allow them to carefully review the status of the supply chain at any given moment. In other words, they could solve the second issue and immediately find out where spare parts were located and how long they would take to arrive. 
We’ve seen time and time again that when spare parts managers work on a site-by-site basis, they often lack an integrated sourcing system. Machine operators thus remain unaware of potential spare parts located nearby, whether within the business or outside it. By sharing their data through one intelligent solution, spare parts managers can get rid of silos and make better, more time and cost-efficient spare parts decisions.

Issue #4: The Wrong Indicators

Numerous Sparrow projects have shown us that carefully balancing budget constraints and risk factors is key to optimizing spare part management. If you had an unlimited budget, you would simply buy all the spare parts you could ever need and pay to stock them in a big warehouse. Most companies do not have an unlimited budget, but yet they rarely leverage full economic modelling for spare parts stocking. 

This is, at its core, another silo problem, as performance indicators are measured differently across various departments. For example, operations will typically use machine uptime, whereas the finance department will use economic KPIs — if spare parts management is even on their horizon. One department is trying to save money, while the other is trying to stock as many spare parts as possible. In our experience, this leads to inefficient spare parts stocking and unnecessary overspending, ultimately squeezing machine operators’ profit margins.

Thus, spare parts management teams need to work with the finance department and take a holistic, predictive view. Essentially, machine operators need to start thinking in terms of the cost of uptime. They need to pool their skills and ask: what is the total budget that we have to keep a machine up and running? In other words, what are the risks, such as downtime, versus the cost of stocking spare parts?  This should lead machine operators to review their spare parts policy and create a single set of economic KPIs that look at the total costs of keeping the machine running. These KPIs should take into account the cost of downtime and the cost of buying and stocking the spare parts (total purchase value in a year or average stock value), as well as total capital. 

Fixing these spare parts management issues unlocks multiple benefits for SMEs

Spare parts management is a very complicated process without the right approach and the right kinds of tools. But the many existing gaps between the latest research and how spare parts are managed on the ground should not be cause for concern. We think they should be seen, instead, as a great opportunity. If spare parts are not, by and large, being managed today, this means that there is a huge swathe of machine operators who could digitally innovate and change the face of the industry tomorrow. Indeed, a recent study by the University of Valladolid, in Spain, found that the changes in business models brought about by the conversion to a more digital supply chain for spare parts have the potential to create significant benefits for both global SMEs (in terms of more effective logistic management), customers (in terms of response time), and the environment (in terms of reduced energy, emissions, raw materials, and waste). The issues noted in this article can thus be avoided using the right technology and data - more on this in our next article!