Fast data in logistics and supply chains

The logistics marketplace is extremely price sensitive. Savvy enterprise customers and frequent shippers focus on very small price differences, all the time expecting top-notch service and handling of their items. Logistics providers are under huge pressure to maintain and reduce costs. How the different providers achieve their operational efficiencies varies by their approach and strategy, but providers that find unique and innovative ways to optimise their costs can quickly come out ahead in this intensely competitive industry.

According to the World Bank’s 2012 Logistics Performance Index, Singapore came up on top as the leading logistics hub amongst 155 countries globally. This is no small feat and is only possible due to the nation’s strategic geographical location and a strong infrastructure. Competition amongst the providers is strong. In today’s market of thin margins, competitors and new market entrants are always looking for opportunities and new angles, and are ready to pounce whenever an opportunity arises.

Logistics enterprises must understand that in order to stay ahead of the pack, a forward-looking vision paired with the right strategy and technology will be key to grow or even sustain current market position. Today’s customers are accustomed to fast-paced business environments where they can quickly find and engage their suppliers, or to switch from an existing supplier to a new supplier. Savvy suppliers and shippers are increasingly leveraging information and the digital marketplace to gain new types of competitive advantage.

Logistics and perishable inventory

Perishable inventory is any item or offering that loses its value after some time. What commonly comes to mind is a food item that spoils or expires. For logistics, the perishable inventory is the capacity to move items. For example, consider the available space within a container; empty space in the container is inventory that once the container is shipped, the potential value is lost. Likewise, the carrying capacity of ships or vehicles is perishable inventory. If the vessel departs with unused space, or even if the vessel is sitting idle, then this is perishable inventory. (For this discussion, ‘vessel’ is any truck, ship, plane or other vehicle used to transport items.)

The airline industry has long recognised the importance of perishable inventory with respect to empty seats. They have developed sophisticated pricing models, advertising campaigns and dynamic offers to minimise the loss of their perishable inventory. Increasingly, shipping companies are adopting similar models for enticing logistics customers to ship at times and in ways that enable the shipper to maximise the use of their inventory, which in turn lowers their costs and can also be passed on in lowered costs for the shippers.

How useful is big data?

Big data is not only about volume, but also about new types (variety) of information and incorporating recent changes for information in a much faster manner (velocity). The real success stories with big data are all about getting ‘big value’ from new types of information in new and different ways. Powerful yet easy to use, Visual Analytics tools are used by progressive enterprises to leverage their data to identify key strategies and opportunities.

For example, a transportation company may use big data and visual analytics to identify a weekly trend for unused capacity on Thursdays, and a monthly trend for unused capacity on the 3rd week of the month. They could leverage that information to contact a special large manufacturing customer and offer them a discount if they ship materials to their warehouse on that particular day.

Likewise, a manufacturer might analyse the variable shipping costs for different time periods. Using that information, they could schedule their supply chain and manufacturing cycles for both operational efficiency and cost optimisation. An understanding of the varying costs can also be used for negotiating beneficial pricing if certain schedules are used for the shipping.

Predictive analytics

The ability to spot trends or patterns in data and predict future values has long been the arena for highly trained statisticians with complicated analytical models. Easy-to-use tools are now available which put the power of predictive modelling into the hands of everyday business users. Typically, the tools guide the users through the process of developing the models on a training set of data, then validating the model on a test set of data. 

A shipper might use one of these tools to analyse their inventory utilisation trends to develop a predictive model. For example, they might identify that they usually receive 50% of the orders for items that ship on Thursday morning by noon on Wednesday. They can use the actual order volume at noon on Wednesday to predict the volume that will need to be shipped. Depending on a higher or lower than normal predicted volume, the shipper might plan additional resources or generate “Last Minute Special” offers for reduced prices in order to maximise their utilisation.

Visual analytics tools are used by progressive enterprises to leverage their data to identify key strategies and opportunities.

Fast data

Data that must be responded to in a short time frame is called fast data. Other dimensions of fast data are information that has a rapidly reducing value over time. For example, knowledge of your perishable inventory unitisation from last month might be interesting, but it’s much more valuable to know where you stand right now for upcoming shipments. Or consider a disruption event such as a truck breakdown. It is important that you are informed about the disruption very quickly, then assess the impacts and respond in a short time frame.

Companies that leverage fast data are reaping huge benefits. Major global shippers commonly have their routes, item handling and resources predefined. A select few are receiving significant benefits by incorporating fast data and real-time information to dynamically react and respond with dynamic handling and allocation of resources.

For example, a shipper might have next-day and second-day services, which have different handling of packages. If they can determine and predict that there will be unused capacity on the next-day service, they can dynamically divert the second-day items to fill the capacity on the next day transportation route. This can allow them to maximise the perishable inventory utilisation on the current day, and to schedule less or smaller resources for the following day.

Logistics moving forward

The logistics and supply chain enterprises that will thrive are the ones that innovate to use every available advantage and opportunity. Gone are the days where business can be run and decisions made based on legacy policies, ridged approaches or even intuition. Organisations that leverage increasing varieties of fast data along with analytics and predictive models will find themselves a few steps ahead of their competition.