Supply chain analytics can benefit your business in a variety of ways. Over time, supply chains have become more complex – many people and processes are involved – and with that comes an overwhelming amount of data. Supply chain analytics turns that information into digestible reports, which you can use to make strategic decisions about your operations.
This guide looks at the different types of supply chain analytics, their benefits and key features.
What is supply chain analytics?
Supply chain analytics involves collecting, analysing, and interpreting data from multiple sources and turning it into insights that can improve the efficiency of your supply chain. The process uses advanced technologies and tools to track key metrics across procurement, processing and distribution.
Why is supply chain analytics important in business?
Supply chain analytics is important in business because it helps you make better, faster strategic decisions about your business operations. At its core, supply chain analytics gives you complete visibility of your supply chain, from raw materials to finished products. With these insights, you can identify and address process bottlenecks, optimise inventory levels, and improve customer service.
Types of supply chain analytics
There are five types of supply chain analytics: descriptive, diagnostic, prescriptive, predictive and cognitive.
Descriptive
Descriptive analytics looks at past performance – it uses historical and current data to tell you what's happened in your supply chain in the past week, month or year. It pulls information from both internal and external systems across suppliers, distributors, sales and customer service. For example, a descriptive analytics dashboard might highlight that half of your deliveries to distributors are running late.
Diagnostic
Diagnostic analytics focuses on the why – what's causing the underlying issues in your supply chain. It uses historical and current data to identify patterns and trends that can help explain why there are delivery delays to your distributors.
Prescriptive
Prescriptive analytics combines descriptive and diagnostic analytics to recommend ways you can hit your desired goals – what actions you should take and in what order to maximise the efficiency of your supply chain. For example, prescriptive analytics may suggest that one of your key suppliers is at risk of going under within the next year based on its history of late orders, reduced operational capacity or declining economic conditions in the area.
Predictive
Predictive analytics is a way to forecast what might happen in the future. This approach leverages the patterns identified through diagnostic analytics to predict, for example, product demand. With this information, you can anticipate stock level requirements with greater precision, reduce inventory costs, and improve inventory management.
Cognitive
Cognitive analytics uses artificial intelligence (AI) and machine learning to process vast amounts of data while 'keeping in mind' things like context when interpreting results. The result remains the same – generating actionable insights that help optimise supply chains – but this approach significantly reduces the labour involved. It means your team can spend less time analysing the data and more time implementing the recommendations.
Key features of supply chain analytics
The key features of supply chain analytics include data visualisation, real-time monitoring, demand forecasting and collaboration.
Real-time monitoring
Real-time monitoring is one of the main features of supply chain analytics. With improved visibility, you get real-time access to accurate data and a comprehensive overview of your supply chain – from top to bottom. This helps you spot bottlenecks or inefficiencies so you can proactively implement solutions before those issues escalate.
Reporting and analytics
Reporting and analytics – when integrated with an enterprise resource planning (ERP) system, supply chain analytics can shed light on specific steps within your procurement and distribution, for example, predicted lead times, order fulfilment rates and warehouse safety stock levels.
Predictive analytics and demand forecasting
Predictive analytics and demand forecasting use historical data to predict what your business needs to do operationally to meet future customer demand. Supply chain analytics produce more accurate forecasts, which means your demand planning and inventory management will improve, too. Ultimately, better forecasting means higher service levels and happier customers.
Data visualisation
Data visualisation refers to slicing and dicing data into graphs, charts and other visualisation tools. It's a way to simplify large volumes of data into digestible reporting that can be used across the business to identify potential risks or opportunities.
Collaboration
Collaboration between stakeholders in the supply chain is essential. With supply chain analytics, every person, team and partner in your supply chain can access and work with the same up-to-date information, which helps minimise errors and miscommunication.
Benefits of supply chain analytics
The benefits of supply chain analytics all stem from improved visibility. With access to real-time data, you can streamline your processes, reduce operating costs, take more informed risks, and provide a positive customer experience.
Improved supply chain efficiency
Improved supply chain efficiency means your processes run faster and create less waste. With supply chain analytics, you can analyse the various stages of your supply chain, identify where inefficiencies are causing delays or bottlenecks – and quickly find ways to solve the issues.
Reduced supply chain costs
Reduced supply chain costs are another benefit. Many of these expenses directly impact profitability, so keeping them as low as possible ultimately means healthier profit margins. Supply chain analytics will show you if you're ordering too much inventory and paying excess carrying costs or if you're at risk of running out of a product based on a supplier's typical lead time – which could lead to lost sales.
Better risk management
Better risk management also comes from supply chain analytics. With accurate, real-time information, you can address inventory levels, production delays or other disruptions before they escalate.
Improved customer service
Improved customer service is the ultimate goal for any business. A hiccup at any point along the supply chain can negatively impact customer experience. With supply chain analytics, you can ensure you're delivering the right products on time to every customer without risking stock-outs or shipping delays.
Better inventory management
Better inventory management means optimising your inventory levels so you always have enough stock to meet demand – without overstocking. Supply chain analytics provides critical insights for demand planning and forecasting and, by extension, how well you manage your inventory. It can also help improve cash flow management by ensuring you don't have too much cash tied up in stock.
Supply chain analytics FAQs
What are the five Cs in supply chain analytics?
The five Cs in supply chain analytics refers to a 2020 report by International Data Corporation (IDC), which identified the five critical factors of effective supply chain analytics:
1. Connected
The accessibility of data – structured data from the Internet of Things (IoT), unstructured data from social media and traditional data sets from traditional ERP and B2B integrations
2. Collaborative
You're using cloud technologies to communicate more effectively with all stakeholders in your supply chain.
3. Cyber-aware
Your systems' security and safeguards from cyber-attacks should be an enterprise-wide concern.
4. Cognitively enabled
You're using artificial intelligence to assess data and make data-driven decisions automatically.
5. Comprehensive
Your supply chain is capable of scaling its analytic capabilities as data increases so you can continue to make informed decisions.
What is an example of supply chain analysis?
Demand planning is an example of supply chain analysis. It uses historical data and other factors to predict what your customers will buy in the future. The more accurate your demand planning, the more likely you'll avoid overspending on procurement and holding on to excess stock. Inventory management is another example that involves tracking the sell-through of items and what stock needs replenishing.
What tools can be used for supply chain analytics?
An enterprise resource planning system (ERP) is a tool that can be used for supply chain analytics, especially if it offers advanced inventory management functionality. Likewise, the MYOB business management platform has reporting, forecasting and analytics tools, inventory management software, and more – all on one platform.
Optimise your supply chain with MYOB
Whatever your business goals, optimising your supply chain will help you get where you want to go. That's why supply chain analytics are so important – without visibility of what's happening in your supply chain, it's hard to make informed decisions about resource allocation, inventory and forecasting.
If you’re ready to scale up your business and want to use supply chain analytics to optimise its performance, contact our ERP team today.
Disclaimer: Information provided in this article is of a general nature and does not consider your personal situation. It does not constitute legal, financial, or other professional advice and should not be relied upon as a statement of law, policy or advice. You should consider whether this information is appropriate to your needs and, if necessary, seek independent advice. This information is only accurate at the time of publication. Although every effort has been made to verify the accuracy of the information contained on this webpage, MYOB disclaims, to the extent permitted by law, all liability for the information contained on this webpage or any loss or damage suffered by any person directly or indirectly through relying on this information.