Objectives
Objectives must be SMART: Specific, Measurable, Attainable, Relevant and Time Based
Figure 3: SMART Objectives
An example of a SMART logistics objective is to reduce the per mile cost of fuel and maintenance by 5% within 6 months. This objective is specific and measurable as a Vehicle Management system is capable of recording this data. 5% is a modest figure which should be achievable. It is relevant because achieving this will ultimately improve asset utilisation and reduce operational costs. By setting a 6 month target the objective is also time-based.
Models
Logistics modelling attempts to simulate all logistics processes including operational constraints in order to plan logistics operations efficiently. This allows an organisation to identify potential bottlenecks in the processes to deploy contingencies and optimise resource utilisation.
Variability
An effective model must identify potential variability in processes e.g variations in transit times for specific journeys. Assuming best case scenarios or using average values can lead to errors and poor logistics decisions.
Data
Data must be accurate, timely and comprehensive as this is what drives logistics optimisation. Using technology such as barcoding or RFID which reduces manual intervention can significantly improve the quality of data and ensure it is recorded in real-time.
Integration
Integration of logistics systems within the organisation systems is vital to ensure data transfer is fully automated for reporting purposes. In addition, providing selective system access via portals or Electronic Data Interchange to suppliers and customer fully integrates the supply-chain leading to improved response times, reduced waste and greater collaboration.
Delivery
Delivery against key performance benchmarks must be measured and reported to management at regular intervals.
People
Employing competent and motivated motivated people to support technology and systems is vital to efficient logistics operations.
Process
Processes must support optimisation and be focusses on continuous improvement. This involves systematic monitoring of data, models and performance.