The importance of analytics in logistics and supply chain management

Any supply chain leader would agree that managing and continuously improving a global supply chain is complex. You’re tasked with coordinate operations between a significant network of suppliers, vendors, buyers, carriers, agents, and local and international business partners, with the common goal of delivering a world-class experience to the customer. However, this can be very difficult with so many moving parts, the amount of flowing data and information and the many disruptions that constantly put your supply chain to the test. Naturally, in this complex system a massive quantity of data is generated. To effectively manage and use this data, industry leaders are quickly realizing the importance of analytics in their organizations. A study Gartner found that roughly 79% of supply chain leaders are working to create plans to train their teams on adopting advanced analytics. So, why is this so important for your supply chain’s success? And how can you get ahead of this issue and utilize your supply chain data to its fullest? This article will explore just that.

What is supply chain analytics

Supply chain analytics analyses information that companies extract from data gathered from the various sources linked to their supply chain. You can’t improve the areas lacking in your supply chain if you can’t measure them, and that’s where analytics come into play. The goal of analytics is to extract value from your data and answer key questions you may have about how your systems currently run. The data includes information related to the processes of

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

The four types of supply chain analytics

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

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 a 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 and 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

While supply chain analytics is quite powerful and valuable on its own, most companies do not have the data on hand to leverage and make use of it. One key challenge that companies face is their dependence on the ERP system, 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. Another challenge we see is virtually no system in place, but the use of spreadsheets instead to manage global supply chains. So, data is on local computers, is not accessible to everyone, and cannot be analyzed or optimized to its full potential. To leverage analytics in your global logistics and supply chain, companies must look for the “five Cs” in supply chain analytics, as per Research group IDC:

• Supply chain analytics needs to be connected and leverage different data sources both internally and externally
• Supply chain analytics needs to be collaborative in that it must have the capacity and capability to collaborate with the entire company’s supply chain network partners
• 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 understand 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's success

Supply chain analytics provides significant benefits across the board for a company’s supply chain operation. When used correctly, it allows companies to convert data into actionable reports, dashboards, and visualisations to achieve better results through:

• Better decision making in a company’s supply chain operations
• Understanding, identifying, and monitoring potential risks
• Facilitating accurate demand forecasting
• Identifying delay patterns and quantifying same
• Optimising inventory balance and avoiding excess/short stock
• Helping improve customer experience

All of this results in increased agility and resilience of your global supply chain, better control over orders, shipments, inventory and permanently lowered operational costs.

Conclusion

Supply chain analytics work to help companies meet the demands of their business through the effective usage of the various analytics models outlined above. Supply chain analytics provides comprehensive visibility into global supply chain operations, helping companies unearth opportunities for improvements through increased foresight. Supply chain analytics is key in achieving real success in your organization, ultimately increasing operational efficiency through data-driven decisions at virtually every level.