Today, huge amounts of data are generated in supply chain networks. As recently as 2017, a typical supply chain accessed 50 times more data than just five years earlier, yet less than a quarter of this data was being analysed. While approximately 20% of all supply chain data is structured and can be easily analysed, the remaining 80% of supply chain data is unstructured or dark data. So the challenge for organisations is finding the best ways to analyse this.
Artificial Intelligence (AI) goes beyond information retention and process automation. It can think, reason and learn like a human. AI can also process vast amounts of data and information, both structured and unstructured data, and provide summaries and analyses of that information very quickly. It also allows organisations to analyse supply chain data and intelligence in real time. Along with other blockchain technologies, companies can now forecast and predict events.
How does supply chain analytics work?
Supply chain analytics can help an organisation make smarter, quicker and more efficient decisions. Given the very large amounts of data generated, supply chain analytics helps to uncover patterns, generate insights and make sense of this data. This in turn allows for the ability to make data-driven decisions, based on a summary of relevant, trusted data, often in the form of graphs and charts.
What are the types of supply chain analytics?
- Descriptive analytics: providing visibility for both internal and external systems of data
- Predictive analytics: helps organisation project and mitigate disruptions and risks
- Prescriptive analytics: enables businesses to collaborate with logistic partners to reduce time and effort in minimising disruptions
- Cognitive analytics: assists companies to solve complex problems or issues in the supply chain process
Why is supply chain analytics important?
- Reduces costs and improves margins: it enables you to access comprehensive data to gain a continuous integrated planning approach and real-time visibility
- Better understanding of risks: it helps you to identify known risks and help to predict future risks by spotting patterns and trends throughout the supply chain
- Increases accuracy in planning: chain analytics can help you better predict future demand. It helps an organisation decide what products can be minimised when they become less profitable or understand what customer needs will be after the initial order
- Achieves the lean supply chain: you can utilise supply chain analytics to monitor warehouse, partner responses and customer needs for better-informed decisions
- Prepares for the future: advanced analytics can process both structured and unstructured data, giving your organisation an edge by making sure alerts arrive on time, so they can make optimal decisions
- Minimises risks: advanced analytics can build correlation and patterns among different sources to provide alerts that minimise risks at little costs and less sustainability impact.
Achieving supply chain visibility
The COVID-19 pandemic threw global manufacturing, stock and delivery processes into disarray and as a result businesses are looking to boost the visibility and oversight of their operations to safeguard their supply chains against future challenges.
Data visibility and transparency is the key to all successful supply chains. The goal is fluid business operations, which helps in giving the level of customer service needed to enhance and maintain a business’s reputation. Because global modern supply chains are so complex, involving a huge network of manufacturing and logistics personnel, even simple mistakes can result in lengthy delays. Businesses must recognise that the combination of visibility and data is critical to future planning, relationship management and crisis response.
Real time data analysis
As technologies such as AI become more commonplace in supply chain analytics, data can now be analysed in real time. AI can rapidly and comprehensively read, understand and correlate data from disparate sources, silos and systems. It can then provide real time analysis based on interpretation of the data. Companies that use this will have far deeper supply chain intelligence; they can become more efficient and avoid disruptions while supporting new business models.
In the context of, for example, a retail chain, everything from the manufacturing process to storage and shipment of goods needs to be planned perfectly to meet demand with minimised wastage. If the manufacturing segment of the supply chain is struggling, real time data can help reallocate resources. This limits the delays that impact consumer relations and therefore your bottom line.
Real time visibility can also help strengthen customer relations by driving timely deliveries with customer relationship management (CRM) applications playing a key role. When a customer orders a product, real time data from a CRM can show the consumer when their product will be arriving and update them on any changes to their order. The same data can help the retailer to spot trends quickly and strengthen customer service.
Under half of businesses capture and use data in real time, but this is changing
If your business is, for example, e-commerce, it’s a highly competitive market where being first is critical. If data on stock requirements and seasonal consumer habits isn’t readily available, it becomes much harder to meet orders and delivery deadlines and means frustrated customers.
This damages supply chain efficiency, as company efforts are directed towards handling complaints rather than sourcing and securing opportunities for growth. The longer this happens, the more resources are wasted and reputation is damaged. Ultimately, this means demand isn’t being met and this is reflected in the company’s revenue.
Using data to build smarter plans
Plans created using data-driven analytics can project potential logjams and dramatically improve a business’s first-to-market chances. Large companies can use advanced electronic data interchange (EDI) and application programming interface (API) systems designed to keep suppliers and customers up to date during the buying process. Smaller businesses can benefit from using third-party companies with pre-established infrastructure for their ordering process, to be able to compete with larger corporations.
Inefficiencies in the supply chain result in:
- lost money from wasted materials
- understocked or overstocked products
- storage fees
- reputational damage
Catalyst BI can run audits on business operations to help spot supply chain issues that are affecting efficiency, and help to create a strategy to resolve them. The Enterprise Data Management Health Check provided by Catalyst BI, measures and manages data and processes for better discovery, transparency and value. Your data management is measured on the following key pillars:
- Data Sourcing and Quality
- Data Integration
- Data Warehouse
- Data Governance
This impartial and neutral Health Check then matches you to your best technologies and practices, demonstrating how you can ensure that you can optimise your data capabilities. If this sounds like something that your company would benefit from, click here to request your no obligation Health Check.
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