The Benefits of Category Management: What You Should Know


When you’re a manager at a large manufacturing enterprise, understanding the advantages of category management can help you make data-driven decisions that improve your company’s bottom line and build long-term success.  

In this article, we’ll explore the key principles of category management, its benefits, and how businesses can use it to their advantage.

How category management transforms procurement processes for spare parts and maintenance materials

Improved decision making

Category management promotes a data-driven approach to procurement processes and transforms the way businesses make decisions. This involves analyzing data and organizing spare parts and products into categories to create a comprehensive view of procurement spend. As a result, this enables procurement teams to optimize their resources, achieve better outcomes, and reduce costs.

One of the key benefits of category management is gaining insights into supplier performance. Coupled with Key Performance Indicators (KPIs), businesses can identify patterns and trends, enabling them to make better decisions about supplier selection and management. This approach can help businesses negotiate better deals, improve supplier relationships, and reduce supply chain risks.

Category management also facilitates cross-functional collaboration between procurement and operations departments within the organization. By involving stakeholders across departments and business units, procurement teams can work together closely and businesses can leverage their expertise to come up with innovative solutions and better outcomes.

Increased profitability

Category management can help businesses improve their MRO inventory management and reduce costs associated with excess stock. By analyzing data on spare part and MRO materials usage, manufacturers can identify slow-moving items and adjust their inventory levels and spend strategy accordingly. 

For instance, an organization may identify that they’ve ordered excess replacement parts for their assets. Instead of unnecessarily tying money to dead stock, excess materials can be shared to other facilities in their network. 

Streamlined, enterprise-wide processes

One way category management results in streamlined, enterprise-wide processes is by standardizing procurement procedures. By creating standardized processes for purchasing parts and scheduling services, manufacturers can ensure supply resiliency and reduce the risk of unplanned downtime 

For example, standardizing data collection reduces the risk of data silos and redundancies which would translate to costly mistakes and delays. Standardizing data also helps when it comes to data processing because it can be easily analyzed and compared to gauge valuable insights.  

Category management tools that elevate trusted practices

Natural language processing for advanced data analytics 

Natural language processing (NLP) enables software solutions to understand and interpret variances in human language often used for part descriptions. This makes it easy to analyze large amounts of data from different sources and identify patterns and trends that would be nearly impossible to catch with manual processes. 

By leveraging NLP during category management processes, supply chain operations and procurement professionals can gain more visibility into their inventory management, supplier relationships, and demand patterns, which can help them optimize their supply chain manufacturing operations.

One way that NLP can assist in category management is by analyzing part descriptions for strategic inventory management and procurement. For example, one supplier might describe a product as a “metal fastener,” while another supplier might describe the same product as a “metal clip.” NLP can be used to make these items searchable using fuzzy logic designed to identify similar terms, much in the same way a search engine works.

Automating these processes reduces the need for manual data entry while making processes more intuitive and accurate. 

Recommendations backed by machine learning algorithms

Companies can harness the power of artificial intelligence and machine learning algorithms to analyze large volumes of data related to supplier performance, inventory levels, and maintenance needs. This allows teams to uncover opportunities for optimization and proactive tail spend management. These algorithms can also detect patterns and trends in data that humans may not be able to see, allowing for more accurate and timely decision-making.

For instance, by analyzing historical usage data for spare parts, operations teams cna identify patterns for minimizing production downtime. Using this information, technology solutions can suggest a strategic procurement schedule that keeps inventory stocked at the right levels at the right times. As a result, supply chain professionals can take a proactive approach to asset management instead of a reactive one, saving operations time and money. 

It doesn’t just end there—machine learning algorithms can also pinpoint potential cost savings opportunities while recommending actionable insights. This may come in the form of alternate maintenance schedules, alternate purchasing plans, and material sharing across your network of facilities. 

Get more out of your category management processes

Category management is a powerful method for companies to streamline their MRO and spare part management processes, reduce costs, and improve profitability. However, undertaking this task manually can not only be overwhelming but can also lead to many roadblocks. 

A materials management intelligence platform backed with AI can help companies gain a holistic view of their supply network, gain valuable insights into their spending patterns, identify opportunities for cost saving, and make data-driven decisions. Learn how to stay competitive in an increasingly complex and fast-paced environment through intelligent materials management.

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