Big data analytics and enhancing the performance of supply chains

Big data analytics in supply chains

More than ever before organisations are faced with streams of data flooding in from various channels at an accelerating rate. Data overwhelm can hamper an organisation's ability to keep up with data inflows and derive valuable insights. The problem can be exacerbated by interactions between internal and external parties up and down the supply chain which, in turn affect business operations.

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It is becoming increasingly apparent that supply chains that learn to harness the power of the data sources benefit significantly; leveraging the advantages of advanced analytics, supply chains can become more responsive, demand-driven and customer centric.

Decision makers in supply chains are seeking ways to effectively manage big data sources. There are numerous examples of supply chain operations applying big data solutions which demonstrate the abundance of process improvement opportunities available through the effective use of data.

Big data solutions that support integrated business planning are currently helping organisations orchestrate more responsive supply chains as they better understand market trends and customer preferences. The triangulation of a range of market, sales, social media, demographic and direct data inputs from multiple static and dynamic data points provides the capability to predict and proactively plan supply chain activities.

The Internet of Things (IoT) — and machine learning are currently being used in predictive asset maintenance to avoid unplanned downtimes. IoT can provide real-time telemetry data to reveal the details of production processes. Machine learning algorithms that are trained to analyse the data can accurately predict imminent machine fails (1).

Big data solutions are helping avoid delivery delays by analysing GPS data in addition to traffic and weather data to dynamically plan and optimise delivery routes.

Applications of big data at a global level are enabling supply chains to adopt a proactive rather than a reactive response to supply chain risks (e.g. supply failures due to man-made or natural hazards, and operational and contextual disruptions).

These examples provide just a glimpse into the numerous advantages derived from the analysis of big data sources to increase supply chain agility and cost optimisation. While it is a relatively new approach, it is being embraced by supply chains globally.

In this four part series we aim to present a more in-depth exploration of the world of big data and the significant opportunities it provides for supply chains to increase agility and efficiency.

In Part 1 of the paper we explore the concept of big data and how it is differentiated from small data. We then move on to identify big data sources and the applications of big data solutions in supply chain operations, and the skills required for supply chains to gain analytical competence and avoid paralysis by analysis.

 

1 Jacob LaRiviere, Preston McAfee, Justin Rao, Vijay K. Narayanan and Walter Sun, Where Predictive Analytics Is Having the Biggest Impact, Harvard Business Review 2016.

Customer & Operations

Customer & Operations

Our group assists organisations to make better operational decisions based on a more complete understanding of their opportunities and risks.

Analytics, Information & Modelling

Analytics, Information & Modelling

Our team helps organisations take the mystery out of big data and demonstrates how to leverage data resources to produce better business outcomes.

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