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Supply chain analytics: More control with data analysis and monitoring

by Editor

Companies are currently confronted with a multitude of risk events that directly or indirectly impact procurement, production and supply security. Most of these incidents are beyond direct control – yet there are ways to identify risks, adjust processes and optimize systems. The basis for this is supply chain analytics – the targeted evaluation of the data stream that accompanies goods along the supply chain.

Supply chain analysis: Data as the basis for transparency

Tracking systems, control software, ERP silos, the cloud, enterprise asset management (EAM) and increasingly also new technologies such as the Internet of Things: From the order to the production to the delivery of a product, these sources provide a wide range of structured and unstructured data material – thanks to artificial intelligence now in real time and across the entire supply chain.

But only targeted analysis links the consolidated data to transparent information that allows conclusions to be drawn and deductions for the future to be made. Based on the results of such analysis, key figures and events in the supply chain can be predicted and supply chain processes can be planned and controlled with greater foresight.

Application and benefits of supply chain analytics

For example, the results enable data-based inventory planning by way of more accurate forecasting of future order quantities. Further, risks such as supply bottlenecks become visible in good time and can be specifically controlled by immediately switching to alternative suppliers. Another important function is the identification of cyber security gaps. As the 16th Hermes Barometer shows, the majority of logistics managers in German companies rate the risks of immature IT and data security as very high. With new requirements such as CO2 tracking of the entire supply chain, precise analysis and continuous monitoring create a solid basis for measurable optimization.

Supply chain analytics yield the most reliable results when all participants in the physical supply chain are closely linked via smart tools and the IT infrastructure. The more comprehensive and high-quality the data material it is based on, the more precise the analysis will be. Through constructive collaboration, risks can jointly be reduced so that every company in the supply chain can benefit. This applies to supply bottlenecks as well as to the new requirements of the German Supply Chain Act, which in the future will demand increased transparency not only from large companies, but indirectly also from smaller suppliers.

What are the different types of supply chain analytics?

The theoretical basis of supply chain analytics is provided by the Gartner Analytics Model. It describes four types of analytics that build on each other and use the evaluation of past results to optimize future processes:

  • Descriptive analytics: Descriptive analytics is based on the evaluation of “historical” data. It provides a basic understanding of previous operations and processes.
  • Predictive analytics: Predictive analytics forecasts and anticipates future developments based on historical and current data. In this way, risks can be identified in due time.
  • Diagnostic analytics: Diagnostic analytics aims at a deep understanding of interactions within a system. It identifies patterns and their consequences – for example, the causes of recurring bottlenecks in inventory management.
  • Prescriptive analytics: “Inferential” analytics simulates processes and their results, for example with a “digital twin”. The results of the digital model can be used to derive optimization potential for the physical supply chain and to automate processes. This is made possible by state-of-the-art technologies such as machine or deep learning.

Supply chain monitoring using data analytics: Methods and technologies

Professional technologies for evaluating complex processes in the supply chain combine different types of analytics: They bring together data from processes that have already been completed, diagnose events and derive predictions about potential courses of events in the future.

Various supply chain analytics technologies are currently available on the market: Most ERP providers already offer analytics functions for supply chain management. Manufacturers of specialized SCM applications also often standardly integrate functions for targeted supply chain monitoring. For example, the analytics software is embedded in a supply chain suite or business intelligence tool that accesses supply chain data. In addition, adaptive software models exist on the market, which can be customized to the individual needs of the company and integrated into the business processes.

Which technologies and methods are most suitable depends heavily on the IT structures already in place, the goals of analytics and monitoring, and the degree of cooperation in the supply network. The precise linking of data sources is crucial during implementation.

In particular, small and medium-sized enterprises (SMEs) often benefit from cooperation with an experienced logistics service provider that has already integrated monitoring and risk management solutions into its offering and can ideally link risk and transport data. Depending on the agreed scope of services and the transport contract, the logistics service provider may, for example, grant access to an SCM platform that SMEs can use to monitor their own supply chain. An additional connection with reporting tools and risk management software ensures comprehensive transparency and a wide range of control options. For SMEs, cloud solutions in particular are convenient, secure and cost-efficient models.

Supply chain analytics technologies include, for example, the following functions:

  • Data visualization: The visualization of information and data, for example using diagrams
  • Stream processing: Deriving insights from consolidated data streams that flow together from different applications and sources
  • Process mining: Reconstruction and analytics of processes based on data
  • Graph databases: Representation and storage of highly interconnected information using graphs
  • Natural Language Processing (NLP)/ Text Mining: Identifying, organizing and evaluating unstructured data from documents, news sources and (social) media using AI-driven text analysis

Supply chain analytics as an effective method in risk management

Precise analytics are the basis of efficient and effective risk management. Supply chain risk management software relies on the evaluation of existing data, prediction, diagnosis and derivations for the future. Machine learning and text mining processes, for example, are utilized: Artificial intelligence filters and evaluates information from thousands of information sources in real time and issues alerts as soon as disruptions are identified at a supplier or within the company’s own business field. This enables timely responses in case of risk events – from severe weather to political unrest to compliance violations. Supply chain analytics, integrated into appropriate digital tools, can thus make a valuable contribution to risk management and protect companies and their supply chains from damage.

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