Industry
Manufacturing
TCS Neural Manufacturing: Driving Agility & a Competitive Edge
Highlights
- Manufacturing enterprises are looking to augment supply chain capabilities through digital solutions, supported by artificial intelligence (AI) and machine learning (ML).
- Building a cognitive, collaborative, and connected neural supply chain is crucial for future growth.
- Such a supply chain model will lead to the creation of a neural manufacturing enterprise, one that is purpose-driven and adaptable.
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Abstract
The ever-shrinking innovation time frames, evolving competition, and market disruptions impede the ability of manufacturing organizations to offer differentiated products and services. At the same time, an increasing number of customers now prefer on-demand, eco-friendly solutions aligned to the circular economy. These trends mandate transitioning to connected smart products and assets, which require an efficient supply chain.
Traditionally, supply chains in manufacturing organizations are characterized by siloed, reactive, intuitive, and manual responses. Augmenting supply chain capabilities through digital solutions, supported by AI and ML is the next logical step. These technologies will create automated, cognitive, and self-acting responses throughout the manufacturing value chain.
This paper explores how supply chains with self-acting capabilities, comprising traits such as collaboration, cognition, connectivity, agility, and responsiveness will lead to the creation of a neural manufacturing enterprise, one that is purpose-driven and adaptable.
The neural supply chain
Building a cognitive, collaborative, and connected neural supply chain is crucial for continuous intelligence provisioning, data sharing in real time, and the enabling of on-demand capabilities and personalization through multi-form and multi-source data analysis.
Characteristics and business drivers of a neural supply chain
The combination of digital, mobility, cloud, and real-time-based technologies (see figure 1) can equip neural supply chains to spot aberrations, discover connections between multiple events across business processes, and recommend actions or insights to stakeholders. Such technology-embedded supply chains allow for a ‘management by exception’ approach. They are not constrained by geographies or organizational data boundaries, allowing for the adoption of a data-driven, automated approach while ensuring data security.
The characteristics of the supply chain described above form the basis of Neural ManufacturingTM, a thought leadership framework, that enables an organization and its ecosystems to become connected, cognitive, and collaborative. It drives agility and exponential growth, providing an intelligent edge.
At the operational level, a neural supply chain will redefine current notifications, alerting, and tracking behaviors, thus improving collaboration among stakeholders and key performance indicator (KPI) metrics. Neural capabilities such as connectivity and intelligence can be seamlessly woven within a manufacturing organization’s supply chain fabric. These capabilities will manifest in the planning, logistics, and transportation functions, all of which will enhance health and safety, ensure cost sensitivity, and improve collaboration at inter- and intra-organization levels, thereby impacting customer satisfaction and influencing the bottom line.
Maturity stages of a neural supply chain
The journey from a traditional and reactive supply chain to a neural and self-acting one is distinguished by stages, with specific characteristics. A neural supply chain enables an organization to identify its maturity, create a roadmap, and set targets to achieve its desired maturity level.
The three-level maturity model is punctuated by becoming neural ready, neural adoption, and neural self-acting, which organizations can adopt to determine their current maturity level (see figure 2).
The three maturity stages of neural adoption in supply chain
Currently, the supply chains of manufacturing organizations are at the neural ready or neural adoption stages. Supply chains at these stages are characterized by digitalization, operational efficiency, multi-level data collaboration, interface standardization, enhanced data processing, process mining, and cognitive and analytical capabilities. They also include features such as chatbots, self-help report generation, quotation, and product configuration. While these characteristics ensure that the manufacturing organization will operate at its best efficiency level, they will not provide gains from the neural abilities such as cognitive, collaborative, and connected.
The neural self-acting stage is the pinnacle of capabilities. It supports real-time data collection, alerting, monitoring and actioning, multi-level personalization, seamless and secured data sharing, cognitive abilities. Further, this stage fully leverages AI, ML, mobile, and cloud-based technologies.
The journey to building neural self-acting supply chains
An organization undergoes three phases (see figure 3) to create a neural self-acting supply chain:
The adoption zone: To apply neural thinking, an enterprise needs to augment its infrastructure, technology, and value chain components, and identify business cases. According to Gartner, over 50% of enterprises have not initiated supply chain digital transformation.
The learning zone: A manufacturing organization iteratively builds its supply chain based on the experience from the adoption zone. This zone is powered by AI, ML, digital technologies, and human interventions through algorithmic inputs to realize incremental business value.
The neural value realization zone: Here, supply chains are highly predictable and accurate. This drastically reduces the need for human intervention as the supply chain is supported by highly advanced AI and follows the principles of management by exception
The journey of building a neural self-acting supply chain
For an enterprise to become a neural self-acting entity, its current supply chain system must be optimized for operational excellence and have additional bandwidth to support basic neural capabilities, including chatbots, self-help abilities, and on-demand services. Further, a neural self-acting supply chain is characterized by distinct milestones and stages and neural abilities (see figure 4).
The neural readiness of supply chains
Manufacturers can adopt two approaches to becoming self-acting entities. Factors such as functional and technical expertise, current infrastructure, and clarity of the business objectives will determine the approach to be adopted by the organization.
Approach 1: Capability augmentation
- The focus at the beginning is largely internal and limited to the enterprise in silos.
- The organization explores and adopts various neural aspects along with business acumen.
- Procurement, warehouse, inventory, transportation, and logistics are key functions.
- Suitable for a technically oriented, inward focused, and tier-1 enterprise.
Approach 2: Ecosystem focus
- The focus from the start is to include multiple value chain components within the ecosystem.
- Based on the individual business case, neural aspects are gradually introduced at relevant stages.
- Typical ecosystem elements include distributors, customers, and compliance agencies.
- Tier-2 and 3 and ecosystem-oriented organizations can adopt this approach.
Neural supply chain: Business use cases
A neural supply chain can offer numerous benefits to manufacturers, as illustrated below:
Purchasing: Processing inputs from prevalent market conditions, combined with supply chain aspects such as forecasting, demand, and planning, can help purchasing departments in enterprises. Factors such as the past performance of vendors, material sources, quality, price, and more, can ease the procurement process.
Logistics: Planning and tracking inbound and outbound shipments using real-time intelligence along with the ability to plan a workaround, benefits the logistics arm of an enterprise.
Inventory and warehouse management: The demand and availability conundrum can be solved at the individual enterprise and geographical levels with better data on scheduled manufacturing, future and historical demand patterns, and confirmed demands and end user behavior. These factors optimize the picking, packing, and dispatching of operations and inventory.
Transportation: Neural capabilities can determine the most optimal distribution route in a firm’s logistics and supply chain using real-time tracking, historical, structured, and unstructured data inputs, chatbots, and 5G networks for communication. The system relies on the intelligence gathered through various ML algorithms.
The neural future has just started
Currently, technological enablers such as AI, ML, self-learning algorithms, data science, and internet of things (IoT) are in various stages of development and implementation maturity. Manufacturing supply chains are still fully connected to one another. Thus, creating a neural self-acting supply chain is a work in progress, which is taking place at a rapid pace.
However, in the not-so-distant future, the distinct elements of a supply chain will align and manifest themselves as an army of AI bots, each specializing in a specific functional aspect with collaborative capabilities.
Bots from procurement will collaborate with those from forecasting, planning, inventory, manufacturing, transportation, logistics, and more. They will gain knowledge about an organization’s supply chain by drawing upon existing data models, structured and unstructured inputs, historical data, and will reinforce that learning by using self-learning algorithms. This will create a true self-acting neural supply chain that is connected, intelligent, collaborative, and personalized.
