The recent advent of data analytics and related tools has helped company leaders and employees alike to become increasingly data-driven in their decision-making.
This not only includes the use of customer data and analytics to identify and act on shifting customer behaviors and interests. It also entails the use of these assets and tools to enable companies to optimize their business processes - both for internal efficiency gains as well as for improved experiences for customers, employees, and business partners.
HMG Strategy recently spoke with Mano Mannoochahr, Chief Data and Innovation Officer at GE, for his insights on how IT has evolved in recent years to a business optimization mindset and how this is playing out within GE.
HMG Strategy: What are the differences you're seeing in terms of how IT has been approached in the past from an efficiency standpoint and how it has advanced in recent years to a business optimization mindset?
Mano Manoochahr: Over the course of the last two decades, businesses have approached IT from an "efficiency" oriented mindset - "How can I do more with less or perform work more consistently?". Business value realization has largely been through automation of existing paper-based processes through technology - allowing businesses to achieve global scale and growth, without increasing costs dramatically.
Technologies that have enabled this type of automation have been well-known platforms like ERP (Enterprise Resource Planning), PLM (Product Lifecycle Management) and CRM (Customer Relationship Management) systems that allowed standardized approach to business transactions, performed in an efficient manner.
What has been missing is businesses' ability to optimize their processes through data-driven insights. When effectively designed and executed, the next generation insights-driven business tools are starting to allow the companies to directly impact business outcomes hence ultimately optimizing performance of the overall business. These new tools don't replace systems-of-record like ERP and CRM but rather build on the efficiencies, automation and data they have created.
Can you point to some examples of how this is reflected within GE?
MM: At GE, we are enabling this shift to business optimization through building advisory tools focused on enabling the front-line employees to become more productive and make better business/operational decisions.
A clear example of such shift can be seen in our manufacturing plants where we have deployed a data-enabled tool called Material Optimization Suite. The tool leverages data from our ERP systems and enables personas like Planners and Buyers at a plant to make smarter and better-informed decisions about raw material inventories at a plant. It allows these employees to clearly see which materials they may run short on and which materials/parts they have too much of at their given plant. The tool accounts for build-schedules, supplier performances and a number of other variables - allowing user to optimize inventory levels at their plants - not carrying excess inventory (hence avoiding tying up cash) or too little inventory and suffering on-time-delivery performance at their plants.
What are some of the benefits GE has seen from its use and application of data analytics in the business? Are there any examples you can share?
MM: At GE, we are starting to see real benefits from being able to glean insights from data, build mathematical models and incorporate those into advisory tools for front-line workers in the organization. Whether it is a buyer on a shop floor plant looking to make the best decision on inventory levels, a salesperson in the field evaluating their next ten best customer phone calls or a finance person modeling/forecasting cash-usage in the business and what their next best set of actions should be.
In the supply chain, we have seen inventory reduction in the range of 10%-to-20%, allowing us free up working capital while improving OTD delivery performance. In the commercial space, we have seen improvements in pricing/discounting decisions by pricing analysts. Lastly, we are driving product quality improvements by blending data from multiple sources such as real-live usage data from products in the field (e.g. engines and turbines) and data from engineering and manufacturing data-sources to gain cross-functional insights into our processes never before possible.
How do technologies such as artificial intelligence and machine learning fit into this?
MM: As I mentioned earlier, we are building advisory tools that are augmenting human decision-making by delivering broader/deeper/better insights to front-line workers. Technologies like AI and Machine Learning are helping us improve the quality of those insights/advice by allowing us to incorporate an increasing number of variables and larger volumes of data than previously possible.
For example, in our Energy Projects business where we may respond to thousands of RFQs per year containing hundreds of requirements spread out across hundreds of pages, analyzing those requirements and pulling together a bid is a laborious and error-prone process. An AI/ML-based engine is helping us search through our repository of historic responses to such RFQs with similar requirements and helping our engineers quickly pull together a draft response that can then be tweaked by engineers to create a final bid. The approach is greatly reducing time-to-respond to such RFQs and potentially increasing the volume of business we can go after at a profitable pace.
What are some of the opportunities for applying blockchain? Are there any applications for blockchain that are in production?
MM: There has been a lot of hype surrounding blockchain which has caused a gap in value-realized vs. expectations. The same is true at GE. After some initial excitement, we have had to pull back a little. This doesn't mean the value or opportunity is not there, it just means we had to slow down investments to match the pace of technology evolution, acceptance of change internally and readiness of the broader ecosystem to work with us.
We do have projects going on in the external marketplace around energy trading, emissions record tracking and even in healthcare around chain of custody tracking for cell therapy that are enabled by blockchain. There are also projects internal to GE that are addressing inefficiencies in the area of intercompany billing and attempting to reduce associated banking fees, supply chain/order fulfillment inefficiencies and allowing us to collect cash quicker.
In my opinion, for blockchain technology to deliver on its full potential, it will require us to adopt a broad-lens view of the larger ecosystem that GE participates in. Blockchain can be a game changer, but we will need to rethink the entire business model/business process for a given area of the value-chain we are targeting. Projects like that take time and higher levels of coordination to manage risks that we are not typically prepared to handle. That, combined with a general lack of readiness of technology platforms and non-GE stakeholders, has slowed adoption of blockchain technology across the company.
Are there any additional thoughts you'd like to share?
MM: As I mentioned earlier, we are expanding the view of information technology at GE from that of efficiency-oriented to optimization of business outcomes. Data, analytics and digital technologies in general are playing a key role in enabling that shift.
We firmly believe that through the deployment of insights-driven digital tools to front-line workers at GE, we can create a step-function improvement in productivity within our business operations. To us, future-of-work consists of humans being advised by machines/advisory tools to make better and more insightful decisions that lead to better business outcomes. If we can do this effectively, we can aspire towards the next phase that Gartner calls "Algorithmic Business" where machines are doing the work while human workers are overseeing the machine and helping it improve over time.
Mano Mannoochahr is a Chair for the HMG Strategy 2018 Atlanta CIO Executive Leadership Summit taking place on August 16, 2018. To learn more about other top-tier conference chairs and speakers, click here.