Daryoush Ziai, CEO of Schindler China, explains how OEMs can gain efficiency and cost reduction through IoT implementation
Daryoush Ziai is a seasoned business leader with over 30 years of experience in leading businesses and teams across Asia, Europe and North America. Born and raised in Tehran, he received much of his education in the US, earning his bachelor’s and master’s degrees in civil engineering and an MBA. He started his career at Otis Elevator Company, and has worked at Carrier Corporation and Schindler Group, where he is currently CEO of Schindler Group China. He is fluent in English and Farsi, and speaks Mandarin; transitioning almost effortlessly between cultures. Daryoush is based in Shanghai with his wife, who is Chinese, and their two boys and a girl. All three languages spoken at home and the family spend holidays visiting family in the US or Iran, or, going somewhere remote, to relax and learn, and where Daryoush can disconnect from work in a constantly connected world.
Callum Sarsfield: During our previous Q&A, we spoke about the financial challenges and revenue generation opportunities of IoT implementation for OEMs in more traditional industries. Where else can OEMs create value?
Daryoush Ziai:
This is a broad and interesting topic. It is clear there is an enormous opportunity for value creation across industrial and consumer products as our environment becomes more connected. In this Q&A, let us consider how OEMs can drive value by gaining efficiency and cost reduction from IoT implementation.
An effective IoT implementation has to drive productivity, cost reductions and even improvements in cash flow in four broad ways.
1. IoT powered by greater volume, depth, and diversity of data
The implementation of IoT will deliver a much greater volume, depth, and diversity of data to an OEM. This vast amount of data, the subject matter expertise of the OEMs in their products, and new big data analytics provide OEMs with the ability to predict the timing of failures and to improve the accuracy of failure prediction over time, as they collect more data. The ability to accurately predict failures, along with real-time or near real-time access to product performance and usage information, enables the OEM/service provider to replace components which are about to fail on a just-in-time basis. This capability eliminates both the risk of potentially costly repairs and the cost of replacing components too frequently or too soon. It also enables the OEM/service provider to schedule such work, instead of having to do it as an emergency repair, thus driving people and organizational productivity improvements. Finally, spare part inventories can be further optimized to increase asset turnover, cash flow and the costs of warehousing and logistics.
2. Predictive maintenance driving cost reduction
In traditional service models, service technician or engineers visit and inspect equipment at regular time intervals. In the best case, these time intervals are defined based on usage levels and environmental conditions. The processes followed by the technician during each visit are also pre-defined. Big data analytics allows OEMs to develop predictive maintenance models which enables them to tailor equipment visit schedules. This reduces the frequency of visits and the amount of time spent during any planned inspections due to the rich data flow driving predictive maintenance and predictive failure models. This enables OEMs/service providers to significantly optimize employee resources and productivity.
3. Remote monitoring improving productivity and cash flow
Users with multiple locations, a large campus or even cities, with assets located across a wide geographic area, may be able to gain productivity, reduce costs, increase safety and levels of service to their customers, or even increase revenue by real-time access to the status of assets and equipment. Examples are public transport authorities, large hotel chains, large healthcare providers, municipal governments, and the likes. Customers can gain efficiency and reduce both operating costs and capital investments through better and more efficient use of assets, made possible by the availability of significantly richer and more timely information.
4. Product cost optimization driven through IoT
Finally, companies can use the ability to predict product failures through IoT, to further optimize product design. In most industrial products, there is a necessity to design certain components to a higher safety factor or specification, due to higher stress factors while being operated. In many cases, OEMs over-engineer some of the components which come under high stress or are deemed to be critical, whilst other parts are designed to a lower standard or tolerance, and therefore have higher failure rates. This phenomenon increases both product cost, due to over-designing certain components, and operational cost, driven by excessive failures of other components. A market-driven approach to defining product reliability and quality requirements, coupled with order of magnitude improvement in product and component failure simulation capability driven by IoT, can enable OEMs to further optimize product costs.
In our next article with Daryoush, we explore how IoT can improve reliability and service levels.
Interview series for Pullmann in Focus led by Callum Sarsfield, Partner at Pullmann Global | July 22, 2020
