We explore five key considerations to deliver scalable fit-for-purpose IoT solutions for the supply chain industry.
COO – Thinxtra, The IoT Telco
As technological innovation advances, it is common for prices to drop and products to improve and the Internet of Things (IoT) has followed this common trend.
For the supply chain and logistics industry, IoT solutions promise endless innovative applications that deliver operational efficiencies, create business opportunities and support competitive advantage.
Yet, despite widespread IoT interest, debate and many trials, it is rare to find industrial IoT projects that have reliably scaled.
In our experience of working with many different organisations in this industry, the biggest challenge for scaling occurs because most IoT trials and Proof of Concepts (PoCs) aim to only prove the technology’s capabilities. Most don’t seek to prove, validate or understand the business benefits that could be unlocked by the data insights IoT solutions can deliver.
The key for successful fit-for-purpose industrial IoT solutions lies in the ability to translate the data into actions which ultimately realise the value, regardless of the technology choice.
5 Key Considerations to Deliver Scalable Fit-for-purpose IoT Solutions for the Supply Chain Industry
The key to unlocking true business value at scale lies in designing IoT solutions that are fit-for-purpose for your business. Over the last five years, we have learned from countless projects:
1. Technology Agnostic Approach
2. Clarity of The Business Problem or Opportunity
3. Ultimately it is all about Data: Requirements and Value
4. Fit-for-purpose Solution Requirement List
5. Total Cost of Ownership (TCO)
- purchase, or design and development of the right IoT devices to produce and communicate the required data.
- quality, reliability and life of the battery to run the IoT device.
- installation, deployment and maintenance costs.
- customisation for efficient operation.
- reliable communications of the data.
- the digital platform enabling workflows that use the data to create operational efficiencies.
- change management to nurture and sustain adoption.