Top tips on how to use data analytics in the supply chain

10 January 2014 | Georges Bory

Georges Bory is managing director and co-founder at Quartet FSIn the not so distant past, a supply chain was simply the road from a farmer’s field to the market. Fast forward to 2014 and the picture has changed completely.

Supply chains are now extremely complex global networks, strained by increasing consumer demands. The simple A to B path has been replaced with interdependent routes involving many suppliers, intermediaries and localities. Adding to these challenges are strict commercial limitations, such as aggressive service level agreements and pressurised delivery times. Together, these demands play their part in creating networks that can prove difficult to govern.

Managers and network co-ordinators now frequently face overwhelming amounts of fluctuating operational data produced by multiple transactional systems. But new solutions such as in-memory analytics – software which can synthesise data from a number of information sources - can provide businesses with the ability to harness this data; monitoring and interacting with their entire supply chain in real-time.

We have compiled a few key tips to help supply chain managers effectively use in-memory analytics to solve numerous use cases.

• Achieve end-to-end supply chain visibility. Real-time aggregation of data from all areas of your supply chain ensures that if/when the unexpected happens, a manager is prepared. An unexpected event will trigger an alert for outlying patterns and because all data is immediately accessible, the root cause of the issue can be identified quickly. This enables managers to assess the impact a disruption will have downstream in the process and act to minimise its impact. If a disruption is inevitable, the warning given by the alerts will enable the manager to proactively warn clients so they may have a chance to adjust their own operations accordingly.

• Track everything. An advanced analytics system can track activity from across the supply chain and calculate key indicators that are refreshed in real-time. This provides valuable insight that allows a business to increase efficiency and to monitor whether things happen according to plan. Metrics that can be analysed include the return on investment of each item delivered; performance and location data from single employees or packages up to a company-wide level; and granular breakdowns of the most profitable routes, or preferred types of delivery. This can be mapped to times and targets and used to introduce efficiencies and eliminate waste.

• Use your data proactively. Using the data available in real-time to optimise delivery and pick up schedules. For example, the “final mile” is one of the most inefficient parts of the supply chain. If used properly, data can highlight the latest delivery times for pick-up and drop-off so that cut-off times are met and inefficiencies are reduced. Data collected on the weight and dimensions of the cargo can also be used in a proactive way. A clear understanding of the split of packages according to their weight can help determine optimal pricing categories for packages. Combining the weight information with the frequency each type of package is handled and the level of service (premium or economy, for example) can enable what-if simulations to prioritise next day deliveries over those that have more flexible schedules. Size and volume data can also be used proactively in areas such as capacity planning. To avoid the cost penalties associated with over- or under-committing to space on transport, historical data can be used to estimate the capacity needed to minimise loss on transport charges.

Georges Bory is managing director and co-founder at Quartet FS

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