Digital Supply Chain Twin: A central element of tomorrow’s digital factory

How industry managers can apply artificial neural networks (ANN) to reform their supply chain management and reduce costs

Production downtimes and non-compliance to delivery commitments are the worst-case scenario within globally networked production and collaborative supply chain relations. There is an overall huge need to balance internal target parameters and KPIs – such as on-time shipment while minimizing inventory levels and optimizing plant utilization. The digital factory enables opportunities to keep an overview of increasingly complex supply chain processes and to identify and exploit efficiency potentials.

PaaS application Edge.One: The Digital Factory as an interlinked and synchronised ecosystem

Digital Supply Chain Twin: The central planning hub for a global real-time monitoring of value chains

The Digital Supply Chain Twin forms the central strategic planning element combining Sales & Operations Planning (S&OP), Demand Planning, Inventory Optimisation or Advanced Planning & Scheduling (APS), among others. Thus, the hub models the interaction of customers, suppliers and the shop floor to display production alternatives and alternative procurement methods (e.g. make or buy) in real time and on a simulation basis. The planning scenarios applied to this process can be compared against each other through a wide variety of key figures such as customer segmentation, product contribution margin or expected contractual penalties.

The digital factory learns self-sufficiently: Forecasting optimization through artificial neural networks (ANN)

A central data analysis and the use of the information obtained enables companies to profit from the past, to monitor the present and plan for the future. Within forecasting, artificial neural networks (ANN) are trained with specifically prepared data pools. With the help of these, time series-spanning data sets and offline information may be applied – both in the form of the company's own historical data, but also through additional industry, supplier and customer data, as well as other external data sources. The quality of the planning data may improve considerably through the application of ANN.

Advanced Planning & Scheduling (APS): The link between the Strategic Digital Supply Chain Twin and the Digital Twin at execution level

Is the digital twin not just a central hub, but even the all-encompassing DNA of the digital factory? Definitely yes! The digital factory pursues the goal of making processes smarter – and thus more tightly networked, more efficient, more agile, faster and more transparent. When linking strategic supply chain and operational shop floor management, we will also have to add "synchronised". But more on that later!

As the industry continuously generates large amounts of master and transaction data – such as countless data points on raw material or customer files, sensor data from the process/production plants or in the form of laboratory information – the information is stored in the central ERP or in digital twin applications such as in MDX (Machine Data Exchange), SCADA (Supervisory Control and Data Acquisition) or QMS (Quality Management-System).

ORSOFT MWB Advanced Planning & Scheduling (APS): Interface of Digital Twin and Digital Supply Chain Twin
ORSOFT MWB Advanced Planning & Scheduling (APS): Interface of Digital Twin and Digital Supply Chain Twin

 

A central Advanced Planning & Scheduling (APS)-platform modelled on a Digital Supply Chain Twin allows the data to be synchronised with each other. This enables real-time analyses under strict end-to-end criteria. Hence, a connected production scheduling process can optimize target parameters such as delivery reliability, maximisation of plant throughput and/or minimisation of inventories. Through its operational planning approach, the APS, as an interface to the Digital Twin, can quickly point out capacity conflicts on the shop floor and implement automated changes, and compare this information with the forecasting provided from Demand Planning.

According to the DNA of the digital factory, formerly autarkic isolated solutions and silos in the shop floor (actual state) and supply chain management (planned state) are reversed and merged into a common digital ecosystem.

PaaS application Edge.One: The Digital Factory as an interlinked and synchronised ecosystem

The Digital Factory thus needs a central digital hub that links and synchronises the digital twins. Even though the term Digital Factory is quite broad, it can still be easily transferred to industrial reality. Platform as a Service (PaaS) solutions like Edge.One may provide the digital anchor for future smart extensions, for example in the form of IIOT, augmented reality or blockchain applications.