In the first part of this article, I discussed why so many analysts insist supply chain transformation (i.e., building a digital cognitive supply chain) is essential for success over the next decade. Nicole Pontius, marketing communications manager for Camcode, insists digital transformation is essential in order to capitalize on gains in efficiency offered by automation, provide greater visibility, break down unproductive data silos, and reduce operational costs.[1] In this article, I want to discuss some of the transformation strategies recommended by various experts. Perhaps the biggest challenge with any strategy is deciding where to begin. Matt Yearling (@MattYearling), CEO of PINC Solutions, laments technology capabilities are so plentiful “it’s not immediately obvious where one should start.”[2] He recommends a staged implementation approach that makes starting the transformation a bit easier.
Process Automation
The first stage involves implementing solutions that manage “repeatable processes consistently across your organization.” Yearling explains:
“This provides the basis for managing and refining processes. Unfortunately, most organizations place too much emphasis on data that is sourced from human input, which is prone to error, so degrading the quality and potential insight the data could provide. This data often resides in siloes of information, focusing on a specific function, such as warehouse automation. Connecting these islands of information makes every such investment more valuable. Connecting data from related systems is not as hard as it used to be thanks to modern web services based integration technologies. Now you can do in days what used to take weeks of programming. We encounter so many organizations suffering from delays and errors caused by reliance on people to double enter information into multiple, disconnected systems. Eliminate islands of information to get a holistic view of your supply chain.”
The general term for what Yearling is recommending is Robotic Process Automation (RPA). As I discussed in the Part 1 of this article, we are moving towards a cognitive value chain and I believe Cognitive Process Automation™ (CPA) will part of that transition. To learn more about Cognitive Process Automation, read my article entitled “Cognitive Process Automation™ can be Good for Business and the Soul.” CPA goes beyond RPA because it learns and improves as it operates. Lora Cecere (@lcecere), founder of Supply Chain Insights, calls this the “Learning Supply Chain.”[3] She writes, “In the evolution of future architectures, ontologies will define supply chain rules. The use of cognitive learning, coupled with ontologies, will drive new outcomes.”
Improved Visibility
Cognitive systems use machine learning to improve over time. The more data they analyze the better they become. Yearling suggests the second stage towards a digital supply chain involves sensors (i.e., technologies that gather data). He explains, “Let sensors take care of the timely and accurate input of operational information, assisting the flow and management of information in the software, and avoiding the need for workers to input data manually. Thanks to the rise in awareness of sensors in [Internet of Things (IoT)] technology, organizations have a multitude of ways to Auto-ID and locate assets. The beauty of using sensor technology in place of manually entered data results in high quality data that you can depend on.” Cecere foresees a future in which corporate networks expand dramatically into “a network of networks which enable interoperability between networks” involving all cognitive supply chain stakeholders. Pontius reports a recent study explored “the relationship between organizations and their partners throughout the supply chain.” According to Pontius, the study examined “current and future states of digital disruption across the supply chain. The survey found that half of the executives responding view digital transformation as very important (75 percent agree that digital transformation is at least ‘important’), but more than 30 percent are dissatisfied with their progress to date. The primary factor contributing to dissatisfaction with digital transformation efforts is that while key technology drivers have been identified, most remain largely unused. Most expect to see tremendous growth within the next five years.”
Cognitive Value Chain
Adrienne Selko (@ASelkIWok) observes, “A host of potentially disruptive technologies are creating digital ‘always-on’ supply chains that will provide better efficiency, visibility, and customer service across a variety of industries.”[4] Those disruptive technologies include:
- Predictive analytics
- Robotics and automation
- Sensors and automatic identification
- Wearable and mobile technology
- Driverless vehicles and drones
- Inventory and network optimization tools
- Cloud computing and storage
- 3-D printing.
Yearling suggests once you have all the pieces in place (i.e., technologies like those mentioned by Selko) an enterprise can “take a holistic enterprise perspective.” He explains, “Have this data feed an enterprise grade tool that enables organizations to navigate a path of continued improvement and to provide transparent engagement with partners. Managing enterprise wide metrics, best practices, process execution, flow of information and inventory, provides the business guidance on the path of continuous organizational optimization.” I believe his vision entails what I call a Cognitive Value Chain. A Cognitive Value Chain is one that takes a holistic view of the business landscape and the ecosystem in which today’s global businesses must survive. Puneet Saxena, Vice President of Industry Strategies at JDA Software, asserts, “Those that are not prepared to harness these technological advancements are likely to be left behind — and eventually go out of business.”[5]
Summary
Saxena observes, “Digital supply chains are becoming a reality across almost every industry vertical.” The thread running through all of the implementation stages of the digital supply chain is reliance on a good cognitive computing system. A cognitive computing system can automate processes, execute routine decisions, integrate and analyze structured and unstructured data, and provide actionable insights to decision makers. Cecere concludes, “I believe that the future architectures will be many trading partners connected to many trading partners, and that they are connected through a process, a canonical and rules-based ontology, to support the many-to-many architecture. (Today’s structures are one-to-one or one-to-many.) I also believe that the network of networks will be powered by prescriptive and cognitive analytics with new forms of visualization and benchmarking.” Yearling adds, “These are exciting times in supply chain industry, as complex challenges can now be tackled using technology more simply than ever before.”
Footnotes
[1] Nicole Pontius, “Transformation via Technology: The Key Drivers of Digital Supply Chain Disruption,” Business.com, 19 August 2016.
[2] Matt Yearling, “The Four Stages of Digital Disruption in the Supply Chain,” Logistics Viewpoints, 29 March 2016.
[3] Lora Cecere, “Supply Chain 2030: Forge a New Path,” Supply Chain Shaman, 13 September 2016.
[4] Adrienne Selko, “How to Deal with ‘Always-On’ Supply Chains,” Material Handling & Logistics, 20 April 2016.
[5] Puneet Saxena, “Technology Trends For The Digital Supply Chain,” Manufacturing Business Technology, 26 August 2016.
