Optimising supply chains with data analytics

Any business that sends or receives goods is at the mercy of its supply chain, so effective supply chain management is a must. And in the modern business environment, where huge amounts of data are being generated at every stage of the supply chain, the integration of data analytics has become a key strategy for enhancing supply chain performance.

What is supply chain management?

Supply chain management (SCM) is an end-to-end process that includes designing, planning, executing, controlling, and monitoring the movement of goods and services from the point of origin to the final consumer.

This complex network involves a series of interrelated activities, from procurement and production, to transportation and distribution.

The aim is to streamline these supply chain processes – reducing costs and enhancing overall efficiency – and ultimately to provide customers with the right products at the right time.

Common challenges in supply management

Effective supply chain management can face various challenges, including:

• forecasting inaccuracies
• inventory management issues
• disruptions in the supply chain network.

With so many moving pieces within a supply chain, just a few issues can cause big problems. 

For example, BDO UK says current challenges in the supply chain (such as cost increase, delayed raw materials, and shortages) are the main concern for more than a third of mid-sized businesses. In fact, 77% report persistent disruptions in their supply chains, and almost half say they plan to onshore their supply chain operations in the next 12 months as a result of these supply chain challenges.

What is data analytics?

Data analytics involves a series of analytics techniques and analytics tools to analyse, interpret, and derive valuable insights from large sets of data – including both structured and unstructured data – and is also known as big data analytics. 

The objective of this area of data science is to enable informed decisions, optimise processes, and give businesses a competitive advantage over other organisations in their sectors.

“Data analytics converts raw data into actionable insights,” explains Amazon Web Services (AWS). “It includes a range of tools, technologies, and processes used to find trends and solve problems by using data.”

Common areas in data analytics

Data analytics encompasses a spectrum of activities – and a number of these are relevant to supply chain management. 

• Descriptive analytics – focuses on summarising historical data as well as current data to provide a snapshot of past trends and changes.
• Predictive analytics – uses statistical algorithms and machine learning to forecast future trends.
• Prescriptive analytics – recommends actions to optimise outcomes based on predictive models.
• Cognitive analytics – use artificial intelligence and advanced analytics to monitor behaviour and spot trends.

How can data analytics support supply chain management?

Data analytics is a powerful tool for supply chain management and business intelligence more broadly. In fact, when it’s applied to the supply chain, data analytics is actually known as supply chain analytics.

It supports supply chain management through:

• Demand forecasting. Data analytics can improve demand planning and forecasting by analysing historical data and identifying patterns, and then making reasonable predictions about future demand. Machine learning algorithms can also adapt to changing market conditions, providing more accurate predictions and reducing the risk of stockouts or excess inventory.
• Inventory management. Advancements in supply chain data analytics help in optimising inventory levels by analysing historical data, lead times, and market trends. Doing so means businesses can maintain sufficient stock to meet demand without overstocking. It also ensures accurate sales data to support automation processes, and reduces warehousing costs as well.
• Supply chain visibility. Supply chain data analysis provides an end-to-end understanding of the chain, allowing businesses to track products from manufacturing to delivery, as well as supplier performance. This transparency helps identify inefficiencies, bottlenecks, and areas for improvement throughout the supply chain network. It also ensures real-time visibility. By implementing and monitoring key performance indicators in real time, businesses can respond swiftly to fluctuations in demand, or disruptions and other unforeseen events, and adjust pricing or other metrics as needed.
• Risk management. Data analytics assists in identifying potential risks in the supply chain, such as disruptions in the transportation network or geopolitical issues. By anticipating and mitigating these risks, businesses can better maintain continuity in their operations.

Benefits of optimising supply chains through data analytics

More informed decision-making

By leveraging data analytics, businesses can use data to make informed supply chain decisions based on real-time information and predictive insights. This enhances the accuracy of their decision-making processes and ensures actions around the supply chain align with overall business objectives.

Improved cost optimisation

Applying data analytics to supply chain management leads to cost savings because it minimises excess inventory, reduces lead times, and enhances overall operational efficiency. On top of that, businesses can allocate resources more effectively – known as enterprise resource planning, or ERP – resulting in a leaner and more cost-effective supply chain.

Increased customer satisfaction

Accurate demand forecasting and real-time data within the supply chain typically contribute to improved customer satisfaction. This is because businesses can meet customer needs more efficiently, and reduce the likelihood of out-of-stock items or delays in product delivery.

Greater competitive advantage

Data-driven decisions around the supply chain often provide a competitive edge in the market because businesses can respond quickly to market trends and adapt to changing customer needs.

Enhanced sustainability

Data analytics can contribute to greater environmental sustainability by optimising transportation routes. This reduces and minimises the environmental impact of supply chain operations. This not only aligns with typical corporate social responsibility goals but can also resonate positively with environmentally conscious consumers.

Maximise success with data analytics and visualisation

Develop the skills to evaluate business information and examine how it can be used to its best effect with the 100% online MBA Data Analytics at Abertay University. This flexible MBA degree will help you gain an in-depth knowledge of a data-driven leader, and develop your ability to communicate key findings to stakeholders across a business in a clear and confident manner.

You will learn how to use complex and large data sets and interpret analytical findings for a range of business decision-making situations, while key modules in data for decision-making and business analytics will build your understanding of data sources, data types, and challenges in data management.