Maximising Supply Chain Performance with Analytics

6 Mar 2019

5 mins read

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In the current hyper-competitive business environment, organisations seek to maximise their performance by optimising their value chain elements. Supply chain is the most significant element of value chain and has gained increasing attention from scholars, with particular focus on maximising firm performance.

In fact, the concept of competition has shifted from being inter-firm-based to being supply chain-based or even network-based. Supply chains are a rich place to look for competitive advantage, partly because of their complexity, and the significant role they play in a company's cost structure. With the power of new analytics, companies can now fine-tune their supply chains in ways that were simply impossible in the past.

At its essence, supply chain analytics is about transforming all the collected historical data and incoming flow of existing supply chain data into insights for making informed decisions. Supply chain analytics employ big-data analytic tools that use sophisticated algorithms and machine learning to aggregate and dissect data. The following benefits can be reaped using the SC analytics:

(i) Descriptive analytics which describes data or provides summaries of past raw data is helpful for data mining and aggregation to historically address the question, “What happened in the past?” In supply chain management, descriptive analytics is used to improve supply chain visibility and make data-driven decisions to manage supply chain complexities regarding variability, velocity, volume, and variety.

(ii) Diagnostic analytics takes the data a step further by pointing to the root cause of issues based on patterns derived from historical data, enabling directional guidance for more effective reactions to resolve day-to-day issues of managing a supply chain.

(iii) Predictive analytics is alerting or analysing what could happen in the future. Supply chain predictive analytics can be used in various aspects of supply chain including demand management, capacity management, and inventory design.

(iv) Prescriptive analytics recommends the best course of action for a particular problem. Using simulations and optimisation algorithms prescriptive analytics provides answers to the question, “What should we do next?” When supply chains executives use prescriptive analytics, they can quantify future decisions and optimise inventory levels, scheduling, and production. Prescriptive analytics closes the loop by tying all the analytics components into actions and automated decisions to improve the supply chain surplus.

By Dr Masih Fadaki, Deputy Program Director, Master of Supply Chain and Logistics Management Programme, RMIT University
Posted online, 06 Mar 2019