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Many managers in asset-intensive industries like energy, utilities or process manufacturing, perform a delicate high-wire act when managing inventory. Finding the right balance becomes crucial for helping ensure the success of maintenance, repair and operations (MRO) initiatives, specifically the spare parts that support them.
What’s at stake?
Whether MRO processes address preventive maintenance, service failures or shutdown overhauls, the wanted results are the same: deliver increased service levels, function safely and sustainably, operate efficiently and reduce unplanned and costly downtime.
A recent report shows a significant increase in the cost of manufacturing downtime from 2021 to 2022, with Fortune Global 500 companies now losing 11% of their yearly turnover which amounts to nearly USD 1.5 trillion, up from USD 864 billion in 2019 to 2020. 1
Another study revealed:
The swinging pendulum
The MRO spare parts inventory varies depending on the industry and equipment, ranging from specific items to encompassing more basic supplies. These supplies include everything from large infrastructure items such as turbines, generators, transformers and heating, ventilation and air conditioning systems to smaller items like gears, grease and mops. Many asset-intensive businesses are prioritizing inventory optimization due to the pressures of complying with growing industry 4.0 regulations, undergoing digital transformation and the need for cost-cutting.
Over time, inventory managers have tested different approaches to determine the best fit for their organizations.
For many years, businesses favored just-in-time operations as the most logical approach for managing inventory and minimizing holding costs. However, recent disruptions in the global supply chain, due to the pandemic and geo-specific issues, have caught many off guard.
If Operations needed a spare part that wasn’t readily available, it often resulted in equipment downtime or costly stockouts. Even in the past, this method frequently led to additional expenses for expediting or shipping, along with concerns about the quality of parts.
Considering that IDC surveyed 37% of companies that manage spare parts inventory by using spreadsheets, email, shared folders or an uncertain approach, it becomes evident that this practice carries more risk than it might seem.2 Unless your demand forecasting is accurate, adopting a reactive approach might prove less efficient.
Now, consider the just-in-case approach. Some managers choose to stock excess spare parts due to past encounters with delays and other negative consequences. Maintaining safety stock is beneficial but excessive inventory incurs costs and demands significant time for management. When assets lack criticality and priority assignments, there is a risk of accumulating unnecessary parts that might become obsolete on the shelves. This, in turn, initiates a continuous cycle of spending on inventory reduction efforts.
The benefits of finding the right balance
So, when considering the drawbacks of both just-in-time and just-in-case approaches, the goal becomes finding the ideal balance that helps ensure you have the right materials to sustain business operations while providing your teams with what they need at the right time.
This isn’t purely theoretical. There are quantifiable benefits to balancing the dynamics of MRO spares and material demand. Many organizations lack the in-house resources or knowledge to run these necessary procedures but those capable of doing so report:3
- A 50% reduction in unplanned downtime associated with parts.
- A 40% reduction in inventory costs.
- A 35% decrease in maintenance budgets.
- A 25% increase in service levels.
How to achieve the right balance
The short answer: collect, analyze and act on data in real-time to unlock immediate value across your operations. Is it easier said than done? It can be if you rely on a spreadsheet, physical asset counts or solely on condition monitoring.
Consider these questions:
- Do you have a platform that combines statistical analyses, prescriptive analytics and optimization algorithms?
- Can you segment data from all your systems like enterprise asset management, enterprise resource planning (ERP), customer relationship management and sensor technology, by using key parameters like cost, criticality, usage, actual lead times and more?
- If you rely on transactional ERP systems, are you missing the critical analytics and reporting capabilities you need and recognized gaps in SAP for asset-intensive industries?
- Can you review historical data modules?
- Do you perform baseline analyses that look at inventory value based on average price, inherited items and other criteria?
- Can you conduct what-if scenarios to visualize your options?
- Do you have purpose-built algorithms to improve intermittent and variable demand forecasting?
- Can you group and prioritize work by using work queues and monitor progress by organizational areas and data sets?
While artificial intelligence (AI) already factors into many inventory managers’ plans, it’s worth keeping an eye on the latest iteration of the technology. Generative AI has the potential to deliver powerful support in key data areas:
- Master data cleansing to reduce duplications and flag outliers.
- Master data enrichment to enhance categorization and materials attributes.
- Master data quality to improve scoring, prioritization and automated validation of data.
Explore optimization
IBM® MRO Inventory Optimization can help optimize your MRO inventory by providing an accurate, detailed picture of performance. The flexible, scalable solution is a fully managed cloud inventory platform designed to collect, store and analyze vast amounts of MRO inventory stock data by using an array of advanced algorithms and analytics to intelligently optimize MRO inventories.
IBM supply chain consulting services can also strengthen supply chain management, helping clients build resilient, agile and sustainable end-to-end supply chains for the future.
- Siemens, The True Cost of Downtime 2022
- June 2022, IDC SaaS Path Survey, #US49286022
- Based on IBM internal analysis of client data. Results may vary.
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