Digital Twinning of Supply Chains
Published on : Wednesday 02-09-2020
Jenis Sheth elaborates upon how to address the challenges in digital transformation for the supply chain and logistics industry.
This article focuses on transformative ideation of transport and logistics value chains. Current practices of Digitisation, e-shopping and Industry 4.0 have disrupted the market, which is embarking for a revision of managed processes, policies and outcomes that may have once served the business well but are now being challenged at the fundamental level. Creating out-of-the-box ideas requires a sandbox in SDLC approach for safe experimentation within the digital twins of transformative ideas. Tools in the sandbox have been carefully picked and open to enhancements as it need to be built with bridges. These tools should organise in such a way to deliver interim milestone results and data collection itself is progressive and matched to what is identically required in the respective digital twin.
Digital supply chain hitches
Traditional supply chains with linear and long chains may not be sufficient in this digital driven era. Now-a-days businesses need to be dynamic in ratifying the ever-changing trends of consumer demand and shift to a more connected supply network, via digitally interconnected devices and complex platforms to keep pace with digital transformations. Currently digital supply chain needs to have the capabilities for comprehensive data availability, superior collaboration and seamless communication across value chains.
Here I portray a few disruptions in terms of elements and expectations, which drive to the need of digital supply chain:
Predictive Deliveries: Adjusting shipment to avoid delivery delay, predicting service disruption across the supply chain and detecting what customers want most Customer Centricity: Shorter delivery lead-time, narrower and more specific delivery time-windows, on-demand logistics, highly fragmented dynamic demand, real-time tracking and delivery customisation.
Automation: Auto-capturing data, real-time information update and autonomous decision making.
Responsiveness: Fast response to changing demand, flexibility to change supply and demand across the supply chain and dynamic planning, routing, scheduling and pricing.
Connected-Network and Visibility: Information sharing, visibility to important information for all stakeholders, the unified views of important information, and stakeholder collaboration.
If these disruptive elements not handled properly then it can cause problems and issues in the supply chains, ultimately leading to high operational costs, poor company margins, unacceptable service levels, and low productivity.
Data driven supply chain innovation
Internet of Things, Machine Learning and Big Data are at the heart of supply chain digital transformation. It produces enormous data and information that can be in the form of structured data such as delivery transactions and warehouse operational data or unstructured data from external resources and social media such as delivery feedbacks.
Supply chain understanding and requirement: This step involves understanding what supply chain aspects are to be improved or identification of the supply chain problems to be addressed before re-shaping the supply chain network. Bottlenecks need to be clearly identified at this stage.
Data collection and acquisition: Gather the data which are identified at earlier step and focus on data availability and accessibility. Relevant data is collected from different sources. It can be structured or unstructured data, in the format of text, picture, audio or video.
Data processing: The collected data may be duplicate or with errors, which needs to be cleaned before subsequent analysis.
Data modelling and algorithm designing: Mathematical formulas,
mathematical/optimisation/simulation data models to the supply chain network generates insights by identifying relationships among variables, finding patterns from the data, predicting what is likely to happen and optimising solutions by using what-if scenarios to evaluate transformative strategies for structuring the supply chain network.
Data communication, visualisation and business insights: Once the data is modelled and analysed using one or more modelling methods and algorithm designs, data along with insights and results from the model can be reported in many formats for communication with the relevant decision makers.
Supply chain innovation: Based on the data visualisation results of the supply chain, the business owners would be able to take action to transform their network design. It may result in new incremental or radical innovations in the supply chain network.
Supply chain orchestration platform
To address all pain areas of industry by utilising the extensive supply chain orchestration platform. It would structure in a way where it equips all parties with proper advocacy in managing changes of goods planning and flow.
The platform aims to tackle the main challenges of today’s supply chain that can be summarised as follows:
Supply Chain Transparency: Supply chain orchestration platform aims to leverage various cutting-edge technologies (i.e. Internet of Things, Big Data Analytics and Machine Learning Algorithms) to provide seamless integration for all processes and activities in the supply chain with secure data sharing which ensures that all stakeholders have the same view of the database to process real-time information automatically.
management interface.
Supply Chain Collaboration: Supply chain orchestration platform would enable information sharing across the supply chain to encourage both vertical and horizontal collaboration between the parties of network value chain.
Supply Chain Flexibility: Supply chain orchestration platform would enable real-time planning of inventory and delivery milk runs to dynamically optimise and configure the supply chain to accommodate changing parameterised values such as change of vendor, order quantity, buffer SKUs and lead time. Dynamic optimisation and multi-scenario simulation are the main tools to help networks self-configure to achieve the flexibility.
Supply Chain Intelligence: Supply chain orchestration platform consisting of intelligent engines will seek to understand the customers’ demands and reduce the discrepancy between production quantity and customer’s forecast. Using a machine-learning algorithm, it would reveal demand insights and provide suitable forecasting mechanisms in order to maximise revenues, reduce costs/losses/risks within the value chain, increase responsiveness with minimum investment and manpower usage and minimise the mismatch gap of demand-supply.
The features in the supply chain orchestration platform are divided into three main features,
namely: control tower interface, intelligent engine and data configuration and controller.
Visibility & exception interface
Supply chain visibility and exception management interface is used to interact with the user and visualise the information and results to the users. The functionalities in this feature can be divided into four groups, namely:
1. AS-IS visualisation and modelling interface
2. To-Be (Standard) modelling interface
3. To-Be (Practical) modelling interface, and
4. Dynamic planning and monitoring interface.
Intelligent engines
Supply chain orchestration platform would be equipped with intelligent engines to generate scenarios and solutions that will be presented by the visibility and exception interface. Specific engines for supply chain as well as core intelligent engines are integrated in this platform The integrated engines would create digital twinning of the ‘physical’ supply chain network for evaluating possible improvement scenarios and solutions. The results from these intelligent engines would be sent to and presented in the visibility and exception interface.
Data Configurator and Controller
This feature would capture the data and information from different data source (such as transaction database, social media or sensor data) and store it in the one integrated database. Due to the variability of the data, some data may need to be cleaned before it is used by the intelligent engines.
Key takeaways
In this article, I address the challenges in digital transformation for supply chain and logistics industry through a supply chain orchestration platform that develop and scale to better efficiency and effectiveness of logistics assets and workforce in digital transformation era.
Jenis Sheth is a Digital Supply Chain Consultant, Market Researcher, Trend Watcher, Critical Thinker, Sales Enabler, T&L Futurist, Influencer in Supply Chain and Supply Chain Analyst. A dynamic Transport & Logistics enthusiast and a SCM professional offering 15+ years of experience of conceptualisation, identifying, evaluating & delivering impressive bottom-line results in supply chain tech. Sheth has produced a continuous stream of organisational development and innovative solutions to the business of IT Product & Solutions; and conceptualised and implemented effective business strategies, and demonstrated strong management skills during several complex projects. He is an effective communicator and influencer who also embraces ideas and perspectives and eagerly collaborated with diverse ideas. As an enterprise strategist & prolific writer, Jenis focuses on the changing face of enterprise technologies. His research is designedfor the early adopter seeking first mover advantage.
