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Big data’s many uses in supply chain

True supply chain visibility may finally be within reach for those organisations able to exploit Big Data, according to the authors of a recent white paper on big data by Transport Intelligence and oTMS. Ken Lyon is the Managing Director of Virtual Partners and Mirek Dabrowski the president and co-founder of oTMS.
At any time, shippers will expect to be able to interrogate their partners systems in order to see detailed views of shipments in transit. They will expect to be alerted automatically about any changes or delays, but more likely, they will expect the service provider to have either resolved the issue, or at least present them with options for dealing with the problem, all backed up by the data and rationale supporting the decision. Unless your systems environment is capable of evolving into something like this, you will be at a significant disadvantage to any competitors that are able to do so.
The term ‘Big Data’ is regularly used to describe a very large data repository containing everything an organisation needs to know, or wants to know.
The explosion in the population of connected devices is also generating huge amounts of data. As this data is aggregated by organisations in large databases, it is termed as ‘Big Data’. How this data is qualified, classified and analysed is crucial, because when it’s done well, it can lead to competitive advantage in the marketplace, said the paper.
But data quality is determined by its accuracy and precision of any context. Cleaning up inaccurate data sets can be costly and possibly of limited value. But it’s more disruptive to include inaccurate data in any Big Data project as it could lead to misleading information.
The implementation of any new systems, then, must be established using a master data set that has been validated for accuracy and context, ensuring that any other systems are able to reference to a common source once they are deployed.
Large scale Cloud platforms are now being used by many companies as a way of interconnecting trading partners and enhancing their ability to communicate and collaborate. This strategy also makes it easier to accumulate very rich data sets that can be exploited for the benefit of the community of users as a whole, said the paper.

Using big data
Applied to supply chains, big data could reveal opportunities for efficiencies and performance gains. If a supply chain is able to share data from every aspect of its operation at any time, it is more likely to result in definite conclusions. Data that is accurate and available in abundance usually results in better decisions and less ambiguity, the report said

Clarity from supply chain complexity?
A system capable of capturing data at every point in the supply chain, qualifying its meaning and placing it in the appropriate context, is an incredibly powerful management tool, or more accurately, decision support tool for the business in such situations.
Supply chain visibility has been seen as an essential component for managing supply chains for years, but so few companies have been able to achieve it. This is usually due to the relevant information being captured by a variety of different systems, most of which are controlled by different organisations. The age of the various systems also has a bearing on matters, as older systems are difficult to interact with (notoriously so in some cases), data definitions are inconsistent and mechanisms to exchange data electronically inefficient.
As the proliferation of sensors, devices and systems generate huge amounts of data, it is essential that the operational systems used to monitor and manage supply chain and logistics activity do not become overwhelmed. Unfortunately, time is not on their side, as the benefits of analyzing streams of operational data ‘as it happens’ (in Real Time, as it’s called in the IT business) are compelling.

“A Single Version of the Truth”
Competing interpretations of what data may mean is not only unhelpful, it can be very costly. This is why there should be only one reference point informing all others – the so called ‘Single Version of the Truth’.
The value of this is clearly illustrated in situations where companies are collaborating across a transport network or supply chain. The ability to discuss problems (or opportunities) is so much easier if all parties are examining the same data, enhanced with appropriate layers of context that describe what it means to them. This avoids confusion, misinterpretation and errors such as double counting or duplication.
Capturing data is one thing, being able to analyse and reveal the appropriate information it holds is something else. Data visualisation tools are very powerful and this is vital in the context of supply chain data, as many participants are often viewing the same information in different ways. This can result in confusion and misunderstanding. The key is to use simple universal images and terminology to create a common understanding of the data. This builds trust between supply chain partners, which in turn, leads to greater collaboration and cooperation.
Modern supply chain operations rely on logistics service providers to deliver a constant flow of information back to shippers and between the related parties. This enables more informed decisions and faster responses to unexpected events. As products are now usually built to order or manufactured in small batches, this has resulted in smaller inventory pools. This makes sense as it has reduced costs for manufacturers who have reduced the amount of capital they have invested in inventory. This only works if they can keep the supply chain moving, as any delays or interruptions may mean the products or parts are not available when their customers want them. If the logistics operators are able to monitor every stage of their operations, they can manage their assets more effectively, plan more accurately and alert partners in advance of any problems. This obviously means that they need information systems capable of supporting these requirements.
Flexible and agile information systems will become available to the smaller players.
The Internet provides universal communications access, powerful computing platforms are available, on demand, as services in the ‘Cloud’ and smartphones act as personal information assistants, delivering data and information directly to supply chain managers and operators. These platforms have been designed to support very large volumes of transactions and store data generated from numerous locations.
Unfortunately many older logistics systems were never designed to exist in such an environment. They are often limited in the amount of data they can handle, the number of transactions they can process and the ability to accept a colossal volume of messages arriving simultaneously. Although some of these systems may have unique and customised functionality for specific operations, unless they can be accessed and this capability shared via cloud services, they will struggle. If they cannot function within these new environments, their value will be limited and will rapidly become redundant.
As logistics operations evolve, the underlying data stores will be massive repositories of information that will increase in value the more they are refined and augmented. Data related to supply chain operations will be combined with data captured from other sources and systems, to provide a rich picture of the operating environment and the parties therein.
A detailed understanding of the customer and their shipping activities should provide insight into how services can be tailored specifically for them. The analytic systems will suggest options for new services or alternate scenarios for dealing with shipment delays or disruptions. Depending on the scope of the logistics operators services, it may be possible to design cross industry services for managing inventory, providing transport capacity priced by real time rate engines, (similar to the methods used by the new taxi services that adjust price against capacity by the second) and the ability to reconfigure orders in transit in response to variable market demand. All of these and many more can only exist if the data driving them is timely, accurate and presented in context.


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