The importance of analytics in global logistics and supply chains

Jonas Mehrhoff
Jonas Mehrhoff
April 25, 2021
5 min read

Note on the authors

Heiner Murmann is the founder and CEO of Orkestra SCS, a logistics, technology and services company. In addition, Heiner serves as Executive Chairman for Evolution Time Critical and President of The Five Inc., and as an Advisory Board Member for both Metro Supply Chain Group and Black & McDonald Limited. Notably, Heiner previously held various senior executive roles at DB Schenker, one of the top three global logistics companies, as a Member of the Board of Management responsible for Air and Ocean Freight, and as CEO of the Region Americas.

Arnold da Silva, Senior Ocean Freight Advisor for Orkestra SCS, is head of an ocean freight consulting company where he actively advises global shippers on ocean freight strategy and execution. With 40 years of experience in the ocean freight industry, Arnold served as Executive Vice President for Ocean Freight Region Americas for DB Schenker. Arnold's passion is to conceptualize and implement innovative ocean freight solutions that transform one’s supply chain and promote a shipper's success.

What is supply chain analytics

Supply chain and its management is very complex, as it involves a significant network of suppliers, vendors, buyers, carriers, agents, local and international business partners that require coordination. All partners in the supply chain are working towards the common goal of delivering a positive experience to the customer and ensuring their goods reach them in time. Naturally, a large quantity of data is generated. To effectively manage and use this data (also called the ‘new gold’), shippers will need to leverage the power of supply chain analytics.

Supply chain analytics analyses information that companies extract from data gathered from the various sources linked to their supply chain. The data includes information related to the processes of

  • Planning,
  • Procurement,
  • Production,
  • Distribution,
  • Customer experience,
  • Inventory,
  • Order management,
  • Logistics cost,
  • Warehouse operation.

supply chain analytics

The role of supply chain analytics

Supply chain analytics allows companies to convert data into actionable reports, dashboards, and visualisations to make key decisions and achieve better results. Having this data makes it possible for companies to be agile in managing their supply chain and take long-term strategic decisions that can give them a unique competitive advantage.

By using supply chain analytics effectively, companies can ensure that the overall efficiency of their supply chain improves, allowing them to achieve substantial cost savings, control inventory, and keep operational costs down. More and more companies are using supply chain analytics to control their daily supply chain operations and leverage the power of the various types of supply chain analytics available.

Some examples of where supply chain analytics is used include:

  • Inventory management that allows the business to track the movement of current orders, ready goods, transit stock and stock that needs replenishment.
  • Efficient management of inventory also allows the business to control its logistics costs.
  • Calculating the environmental impact of the operations based on the cargo movement.

Types of supply chain analytics

There are 4 main types of supply chain analytics for effective supply chain management.

Descriptive analytics

  • Combines metrics from internal and external sources to provide visibility into information such as inventory stock levels, lead times, fill rates.
  • Companies can compare data from previous periods to identify delay patterns, if any, in supplies and take corrective action after due investigation on the delays.

Predictive analytics

  • Uses the big data available to help predict supply chain behaviour, forecast future demand based on past performance, predict possible supply chain disruptions and risks, and proactively take steps to mitigate potential risks and situations that may disrupt the supply chain.
  • Allows companies time to prepare themselves and align their strategies to cater for any incidental peaks or troughs in demand and product movement.

Prescriptive analytics

  • Uses a combination of information from descriptive and predictive analytics and advanced analytical techniques to highlights actions that the company needs to take to achieve the desired results
  • Because of the combined action and advanced analytics used, prescriptive analytics implementation is more complex and requires robust technology to handle and convert data to actionable insights

Cognitive analytics

  • A new and advanced approach to decision-making by companies using advanced technology like Machine Learning, Artificial Intelligence to automate processes and solve complex supply chain problems.
  • Using these advanced technologies, customers can automate various activities involved in prediction, planning, inventory management and execution in supply chain.

Making use of supply chain analytics

Making use of supply chain analytics

While supply chain analytics is quite powerful and valuable by itself, most companies do not have all data on hand to leverage and make use of it. One key challenge that companies face is their dependence on ERP systems which often only contains internal data records. External data such as the status of purchase orders and shipments from material suppliers, contract manufacturers, and forwarders are not available. An alternative solution is to leverage supply chain analytics that is incorporated within a supply chain platform. A supply chain platform is usually the repository for shipment lead times, inventory levels, information relating to order fulfilment, and other critical data.

As per Research group IDC, customers must look for the “five Cs” in supply chain analytics

  • Supply chain analytics needs to be connected – like the integration with a supply chain platform that provides data sources and credible data input is vital for analytics;
  • Supply chain analytics needs to be collaborative in that it must have the capacity and capability to collaborate with the company’s suppliers using advanced technologies like the cloud;
  • Supply chain analytics needs to be cognitively enabled as cognitive analytics is one of the 4 key analytic models used in supply chain management which helps companies fully comprehend the effects of disruption;
  • Supply chain analytics needs to be comprehensive and provide reports and solutions which are functional and scalable, affording the company the ability to offer quick results;
  • Supply chain analytics needs to be cyber aware, especially with high levels of cybercrime and risk of cyberattacks in the new age. As the information provided by supply chain analytics is sensitive, companies need to be well equipped to handle these exigencies.

Why supply chain analytics is vital for a company

 Supply chain analytics provides significant benefits across the board for a company’s supply chain operation. Reasons why supply chain analytics is vital for a business are:

  • Better decision making in a company’s supply chain operations,
  • Understand, identify, and monitor potential risks,
  • Facilitate accurate demand forecasting,
  • Identify delay patterns and quantify same,
  • Optimise inventory balance and avoid excess/short stock,
  • Help improve customer experience.

Companies need end-to-end visibility on orders, shipments, supply chain operations and real-time analysis of how the business is progressing.

As per Infoholic Research, the Supply Chain Analytics Market is expected to reach a value of $9.43 billion by 2024, growing at a CAGR of around 18.62%.

Key take away

Supply chain management has come a long way since it was successfully implemented by Ford and other automotive giants. As the industry grew and adopted technology more and more, the demand from customers also grew.

Supply chain analytics helps companies meet the demands of their business through the effective usage of the various analytics models available. The advent of more advanced technologies like the cloud, Machine Learning, Deep Learning, Artificial Intelligence and Big Data has assisted in positioning supply chain analytics as one of the most important processes in the management of supply chain for any business.

The advantages of supply chain analytics are deep-rooted and long-lasting if a company chooses to use it effectively. Supply chain analytics provides much-needed comprehensive visibility of the supply chain operation and helps the company unearth opportunities for improvements in its business process coupled with foresight into the future.

For modern businesses, supply chain analytics is critical to achieving growth as it helps in improving operational efficiency through data-driven decisions at all levels of the company.