Big data is certainly enabling better decisions and actions, and facilitating a move away from gut feel decision making. But as more reliance is placed on algorithms, data and analytics, the question of trust is emerging as an important consideration. Another important consideration is working with external parties to bring together far more meaningful insights and actions. This again emphasizes the need to really trust in the analytics to be confident in decision making.

Big Data and logistics are made for each other, and today the logistics industry is positioning itself to put this wealth of information to better use. The logistics sector is ideally placed to benefit from the technological and methodological advancements of Big Data. A strong hint that data mastery has always been key to the discipline is that, in its ancient Greek roots, logistics means “practical arithmetic”. Today logistics providers manage a massive flow of goods and at the same time create vast data sets. For millions of shipments every day, origin and destination, size, weight, content, and location are all tracked across global delivery networks. But does this data tracking fully exploit value? Probably not. Most likely there is huge untapped potential for improving operational efficiency and customer experience, and creating useful new business models. Consider, for example, the benefits of integrating supply chain data streams from multiple logistics providers; this could eliminate current market fragmentation, enabling powerful new collaboration and services. DInnovum realizes that Big Data is a game changing trend for the logistics industry.


“The leading retailers will be able to effectively analyze and interpret big data to stand out from their peers by winning customer experience [by 2020].”… Erich Gampenrieder, Global Head of Operations Advisory, KPMG Germany

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 organizations 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 analyze the data can accurately predict imminent machine fails.

– Big data solutions are helping avoid delivery delays by analyzing GPS data in addition to traffic and weather data to dynamically plan and optimize 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 advanced analytics capabilities are now being used to take a cross functional view of an organization where commercial, finance and supply chain operations are able to combine the vast data pools and make customer centric decisions whilst balancing cost, revenue and profit drivers. As the complexity of supply chains only increases, the ability to analyze and derive meaningful and timely insights will become central to organizations. Organizations that fail to do so will quickly become irrelevant. For supply chain leaders of today, identifying the required capabilities of tomorrow is of vital importance to equip the supply chain organization in readiness for this emerging mega trend.

Looking ahead, there are admittedly numerous obstacles to overcome (data quality, privacy, and technical feasibility, to name just a few) before Big Data has pervasive influence in the logistics industry. But in the long run, these obstacles are of secondary importance because, first and foremost, Big Data is driven by entrepreneurial spirit. Several organizations have led the way for us – Google, Amazon, Facebook, and eBay, for example, have already succeeded in turning extensive information into business.


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