IIoT- Increase in bottom line performance

The Internet of Things is at the tip of the tongue of every facet of manufacturing today. The Industrial Internet of Things is facilitating access to a potential goldmine of previously untapped data by enabling predictive Industry 4.0 through continuous integration, real-time data collection, machine-to-machine communication and big-data analysis. Manufacturers and key industry players are now realising the benefits such as rise in bottom line performance of the insights and predictive maintenance solutions obtained from historical and real-time analysis of this vast data bank.

bottom line performance

According to findings made by Accenture’s most recent study on IIoT trends:
72% of the companies were set to increase their IIoT spending by 30% or more by 2020.

60% of the companies are already involved in IIoT initiatives.

Most importantly, the biggest driver of adopting IIoT initiatives is cost reduction at the bottom-line. and hence improving the bottom line performance. It is interesting to note that product development, improvement and business model research took second place, which shows that investment in IIoT also has potential in top-line growth.

 

Cost of Unplanned Factory Downtime in Manufacturing Industries

Downtime is the biggest source of production time and cost loss in manufacturing industries. Unplanned downtime is a period of time that the factory machinery is not in use due to unexpected equipment failure and machinery breakdown. When such downtime occurs, the overhead costs are on the rise, but the value of production is zero. This heavily impacts the bottom-line performance of a manufacturing company.

According to a report by Analyst firm Aberdeen Research, one hour of factory downtime can cost a company as much as 260,000$, with 82% of companies experiencing downtime between 2015 and 2018!

Areas of cost incurrence to the bottom-line due to factory breakdown:

Detection cost: Post-breakdown investigating and analysis to find the cause of machine failure.

Response cost: Incidental costs related to client business disruption,  and recovery.

Equipment cost: Cost of purchasing new equipment and repairing damaged equipment.

Productivity cost: Loss of potential profit due to a halt in productivity.

Capacity cost: Inability to accommodate an increase in demand due to suboptimal plant capacity.

End-user cost: Time, productivity and financial loss incurred by product end-users.

Third-party cost: Cost incurred by third-party contractors and projects.

Direct labour cost: Increase in labour cost per unit due to  reduction in efficiency.and production.

Inventory cost: Increase in cost of holding inventory due to increased holding time.

According to the findings of Cisco studies in 2017, 74% of companies that begin an IoT initiative fail. More often than not, projects go over budget, deployment times run long, interoperability issues occur across legacy platforms or planning and resources aren’t allocated appropriately, leading to their cancellation. This failure rate has lead to a greater hesitation for manufacturers to embark on their digital transformation journeys.

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