The use of automation in supply chains is not a new phenomenon. Right from the start of the industrial age, we have witnessed companies deploying automation to drive innovation and efficiency.
Traditionally, supply chain automation has always focused on making physical tasks such as fast deliveries and the physical movement of goods easier. One of the best examples is how e-commerce giant Amazon deployed over 200,000 mobile robots to move its products between warehouses and delivery channels, and finally to the end customer.
The emergence of cognitive technologies such as Artificial Intelligence (AI) and machine learning (ML) is now adding a new dimension to supply chain automation. In addition to automating ‘regular’ physical tasks, it is now enabling ‘thinking’ and ‘reasoning’ in processes that were previously out of the scope of automation.
What is cognitive technology and how can it enable digital transformation across supply chains? Let us delve into that.
What is cognitive technology?
Also referred to as ‘thinking’ technology, cognitive technology is a set of various automation tools and techniques including robotic process automation (RPA), AI, ML, and natural language processing (NLP).
Cognitive technologies are transforming business process automation in multiple ways including detecting data patterns, classifying or identifying images, or reviewing contentin a way previously not possible. Rated asintelligent technologies, they are taking automation to the next level of tasks that require high-level ‘human thinking.’
Role of cognitive technologies in supply chain
A 2019 survey conducted by KPMG LLP found that over 77% of supply chain professionals are planning to invest in cognitive technologies, while 80% realize the enormous importance of AI and ML in addressing business problems. Cognitive technologies are also a key component of Industry 4.0 where the focus is on driving a data-backed decision-making process.
Next, let us look at 5 ways by which cognitive technology is changing supply chains. 5 Uses of Cognitive Technology for Supply Chains
Providing higher visibility
A Geodis survey revealed that only 6% of companies have complete visibility into supply chains at any given time.
By analyzing real-time data, ML algorithms are enabling supply chains to respond faster to disruptions by providing instant visibility and transparency into day-to-day operations. Bob Stoffel at UPS talks about supply chain visibility as simply not 'visibility into your supply chains," but also "visibility among partners, which enables collaborative decision-making."
Through ML algorithms, alternative actions are being offered for any disruption or unplanned events. For instance, weather-related data can be combined with operational data to predict logistics or transport disruptions and recommend suitable actions. Similarly, organizations can improve supply chain visibility by implementing Configure-Price-Quote also known as CPQ, which leverages data from diverse supply chain systems including sales, production, and inventory.
Emulating human senses
Cognitive technologies, in combination with other digital technologies, are also emulating human capabilities such as sight, hearing, and mental processing. For instance, digital supply networks or DSNs along with computer vision technology can be deployed on factory floors for any asset malfunctioning and monitoring, thus freeinghuman workers to take up more challenging functions.
Similarly, self-learning ML algorithms can be used to rank the best supplier, renegotiate contracts based on available data, and perform preemptive maintenance to prevent any breakdowns.
Planning for optimum capacity When it comes to optimizing freight delivery, supply chain companies can no longer depend on territorial maps or conventional geographical data. Over 40% of procurement executives expect cognitive computing to add value to global logistics and distribution.
With constant fluctuation in customer demands for products, logistic companies are unable to perform capacity planning or the optimum use of delivery vehicles to meet the current capacity. The volatility in delivery volumes means most companies experience disruptions such as last-mile order cancellations or vehicle idle time on selected days. Cognitive automation in logistics, in tandem with efficient cloud infrastructure, can improve efficiency in capacity planning and recommend real-time improvements.
Reducing shipping costs
Supply chain operations are now focusing on direct customer shipping to reduce their costs and improve their operational efficiency. Direct shipments are found to save between $5-8 on each shipment as compared to other forms of shipping. However, to implement direct shipping, supply chain logistics need to have a complete view of their operations and end-to-end processes.
This is where cognitive technologies can help in optimizing shipping logistics and discovering better transportation modes for on-time and cost-effective deliveries.
Improving operational efficiency By integrating with advanced automation tools, ‘thinking and self-learning’ supply chain models can be trained to augment the human decision-making process, thus elevating operational efficiency. Across industries, supply chain professionals are integrating technologies including the Internet-of-Things (IoT), cloud computing, and predictive analytics to their supply chain processes such as managing inventories and energy consumption. This allows the inflow of operational data from connected devices and cloud platforms, which when analyzed by ML models is becoming the baseline for new product launches or go-to-market strategies.
Cognitive technologies such as AI and ML are not just automating manual physical tasks across supply chain operations but are facilitating a smarter and ‘thinking’ supply chain that can respond efficiently to daily disruptions.
Cognitive automation is increasingly driving the digital transformation of varied industries including manufacturing and supply chains. As an IT consulting firm, Stralynn is here to provide you with the best services in digital transformation.