Using IoT to identify profitable products and customers

For manufacturers profits are under pressure from both ends of the supply chain. On one side, material, labour and energy costs are increasing. On the other, customers want smaller batch sizes, faster deliveries and lower prices. How can manufacturers use IoT to identify their profitable products, and more importantly, their profitable customers?

For many manufacturers, product costing was an imprecise business. Material and direct labour costs can be accurately assigned, but not the overall manufacturing cost allocation. For example, absorption, or full costing spreads the full manufacturing overhead equally across the production volume of all products. The risk of this approach is the loss of important cost factors. For example if a line is running slower, breaks down, or creates more scrap and wastage.

A more accurate approach is to use activity-based costing (ABC). ABC assigns the cost of each activity to all products and services according to the actual consumption by each. The problem for users is that it is costlier to install and more time consuming to use. However, Iot will change that

IoT and IIoT have the potential to capture vast quantities of real-time data and turn it into useful information. Using manufacturing data from smart machines, devices and sensors enables users to identify costs in real-time. Furthermore real-time accounting identifies profitability both by product, by order and by customer.

Profitable products

By superimposing profit control and process control, a strategy of profitable efficiency emerges. A combination of sensor-based data from the process, and financial data calculate the cost and profit points across industrial processes. This also becomes the driver for allowing operators to gain access and influence to profitability data. Real-time accounting measures will become the primary performance indicators of industrial operations. Stake-holders can enable continual operational profitability improvements for the life of the plant. Moreover, it enables the measurement of operational profitability for any initiative that impacts the performance of the operations.

This generates business value in two ways. Firstly by establishing a performance baseline for a production line or cell. From this, managers can to measure the value and benefits of improvement to automation. Secondly, from the baseline stakeholders can act in a way that drives more operational profitability. Adopters are finding practical applications for linking modernisation efforts to improved profitability.

Acquiring more data from a plant will result in a more productive process. By using a small investment from additional sensors, more data becomes available. Data analytics will show if changes to the production automation process control will increase efficiencies. Known as secondary sensing, it enables changes to the process without a large capital investment. Measurable efficiency improvements then translated into financial benefits.