In my first post, I explained how the 3R Approach offers an ideal framework for organizations to orient their digitization and innovation initiatives through Rethinking products and services, Reimagining customer experience and Re-engineering the value chain. We discussed in detail the first two steps in previous posts. Today’s post is on the third and final step of the 3R approach – reengineering the value chain.
Looking Inwards to Move Forward
Businesses that have stood the test of time are built on a foundation of complex systems that come together to create value. Most modern value chains are discrete and organized into silo-styled stages around divergent operations such as market research, product development, marketing, manufacturing, distribution, and ultimately customer service and engagement. While the traditional methodology of business has given rise to these siloes, digitization is well on its way to dismantle such barriers.
New systems that are built on the values of digitization demand end-to-end integration across the value chain. At the end of the day, the transformative power of ‘rethink’ and ‘reimagine’ is meaningless unless they align complex business functions through standardization and automation techniques.
Enterprises now have access to cutting-edge tools and technologies that can help them execute this approach effectively and have complete clarity of their own operations. Key technologies such as the Internet of Things (IoT), machine learning, artificial intelligence (AI), analytics, automation and robotics, have utility across business operations in proven ways. These tools can transform various enterprise technologies, such as autonomous logistics, integrated planning and execution, logistics visibility, procurement, and warehousing management, into more optimized solutions.
The integration of technologies across the value chain enables organizations to greatly enhance their decision-making power and potentially even predict change. By capturing data at every node and action junction in the value chain, leaders become more prepared to manage disruptions and utilize digital modeling to prepare for potential situations and implement scenario-based action plans in real time as conditions change.
The benefits of reengineering the value chain percolate down the managerial chain to all business operations and vastly reduce time-consuming and repetitive tasks. Automation systems which use intelligent operations help enterprises drive down cycle times and increase accuracy. This process allows enterprises to detect, predict, and prevent all the pain points they may not have known existed. Machine intelligence can effectively replace intuition, saving millions in guesswork and generating millions or even billions of dollars in efficiencies over the long term.
In effect, the true digitization of a consumer business, or any business for that matter, rests on reengineering our value chains through process standardization, automation, visibility, analytics, and collaboration capabilities.
Discovering the Unknown
For logistics planning, the problem has always been to ensure the availability of the right quantity of supplies at the right place and at the right time – a subset of the overall business challenge we’ve discussed earlier. With machine learning-based modeling enabling an enterprise, the inbound logistical management can factor in a number of variables such as order placement, shipping, warehousing and utilization to predict and plan for future requirements.
A real-world example is when Walmart leveraged their Retail Link machine learning system to analyze information flowing throughout their supply chain. With it they were able to discover gaps and make seamless corrections in real time. Honda, on the other hand, deployed machine learning to discover patterns in their warranty return notes and mechanic reports to backtrack quality issues beyond the assembly line. Similarly, Caterpillar was able to save its fleet customers millions of dollars by using their machine-learning based Asset Intelligence platform, powered by IoT data, to identify an optimized power generation process for ships carrying refrigerated containers.
McKinsey Global Institute’s 2017 report states that machine learning has received the largest share of internal investment. This makes perfect sense given the potential payoff that it has proven to have for the bottom line. Major players like Google and Baidu strive to lead this movement from the front and are rapidly pushing the technology forward.
As an organization takes these technological tools and applies them toward its own operations, the results can be just as effective. Consider Amazon, which, in their attempt to create a fusion between the real and digital world, launched the Amazon Go store, which was first operated internally for employee beta testing. Amazon is constantly experimenting and testing diverse technologies like voice recognition, computer vision, machine learning, and AI to integrate the convenience of the digital with the real.
Imagine a customer walking into a store to buy a shirt. What if the store could have the desired shirt, currently unavailable, delivered to the customers within hours?
Furthermore, the customer can try out shirts to know how they fit, but augmented reality tools can give the customer a clear visual of how they would look in that shirt across an assortment of colors, helping them make their decision. And, by using real-time inventory tracing, the shop knows where the desired color shirt is and how quickly it can be delivered to the customer.
In the next wave of digital transformation, this shirt would be custom-made within hours for the customer if it wasn’t already in stock. In fact, this isn’t that far from reality. Amazon has already won the patent to create an automated on-demand factory that will, one day, do exactly that, and possibly change the way retail works in the digital space.
Reinvention or Obsolescence?
As companies move forward in the age of technological disruption, they have little choice but to reinvent themselves. The volatility of business models is growing and what works will become more unexpected and surprising with each passing day. The 3R approach to digitization helps provide a concrete way to address the issues on the journey to reinvention.