Digitalisation in logistics planning and execution 
Information and communications technology ICT play a major role in the planning and management in supply chain. There is a myriad of software applications that, roughly, provide supply chain managers with support for three functions:
  • Business intelligence for the positing of logistics within the firm’s business
  • Supply chain planning for strategic and tactical questions where investments are invoiced with a return period of a year or longer and supply chain execution, which supports the actual storage and movement of products.
Commonly use information and communications technology systems in logistics:

 
Business intelligence
ECM- Enterprise Commerce Management
PPM- Performance and Profitability Management
Supply chain planning
SND- Strategic Network Design
DPS- Demand Planning System
APS- Advanced Planning/ Scheduling system
MRP- Material Requirements Planning
Supply chain execution
GTMV- Global Trade Management and Visibility
MES- Manufacturing Execution System
WM/CS- Warehouse Management/ Control System
FMS - Fleet Management System
TMS- Transportation Management System
 
Enterprise Resource Planning System (ERP) have a wide scope at company level and are connected to most of those. Above the company level, or between company divisions or plants, share web-based platforms may be used to allow inter-company or inter-company communication. A concrete example of a typical situation is in the box below where the information flow and the physical flow related to a port call for a container transport is showed. Already in the current situation, information flows are much more complex than the physical flows. Very little of these flows are exchanged in digital form or in an automated fashion, most of it is on paper. One can imagine the magnitude and complexity of the change that digitalisation implies, with these flows and actors involved.
ICT is probably the strongest force transforming the logistics industry today. Ubiquitous ICT systems are being made possible by several concurrent developments that have been evolving since the middle of the 20th century.
  • The exponential increase in computing power of processors (as predicted by Moore in 1965, a doubling in processor capacity each 18 months).  
  • A reduction in size of computers from the mainframes in the 1950’s to the devices (wearables, smartphones, smart things, sensors) of today. 
  • The widespread implementation of computing power, connectivity and communication/actuation capabilities into autonomous objects, creating the IoT.
  • The massive increase in our information processing capabilities and analytics due to artificial intelligence (neural networks, machine learning, deep learning etc.). 
  • Collective mobilisation and shared use of computing resources across the planet to create practically limitless storage and calculation capacity (cloud and fog2 computing)
  • Digitalisation of administrative and paper-based communication systems, leading to instantaneously exchangeable data and increased adaptability of systems.
  • A growing availability of data about logistics operations, and an opportunity to use contextual data (big data) to interpret this data and optimise processes situationally.
  • Awareness of the potential of exchanging data between actors in the chain, to an extent that new data markets are created, shared systems are developed (blockchain) and investments are pooled.
The main impact of digitalisation of logistic services is twofold. Firstly, the conventional services in the physical logistics world are enhanced and improved, due to improved information availability. Secondly, the dimension of information based services is extended, providing additional added value to the physical product and its delivery, which could not be provided before, such as, for example, prediction of product quality. Figure below shows this dual innovation. 
Digitalized world business model
The opportunities and challenges for traditional business models are enormous (see Fleisch et al., 2014; Strandhagen et al., 2017) and go well beyond the transfer from analogous to digital flows:  
  • New product propositions have to address global, digital services for local, physical assets that seem unconnected and inaccessible. 
  • New languages have to be developed to allow systems and people to communicate.
  • Firms need to make adaptations from hierarchical, to decentralised, to distributed systems. 
  • Trust needs to be built between supply chain partners to exchange and protect sensitive data for mutual benefit.
The independent and autonomous behaviour of objects has to be factored in. we arrive at the following main impacts of transformational ICT innovations: 
  • A move towards collective and shared software and data, first on a smaller scale (bilateral systems), later moving towards larger groups; these support end-to-end supply chain integration, including transport and storage systems as well as security. 
  •  An increase in data analytics capabilities for all areas of supply chain execution from descriptive, to diagnostic, predictive to prescriptive information.
  • Growing autonomy of movable assets, including the product itself, in a series of steps starting with automation and situational awareness, to autonomous movement. Remote monitoring and control of assets and products facilitates reliable operations.  
  • Knock-on effects of low communication costs and fast communication on the number of partners in a network and on supply chain deadweight losses due to cash-to-cash cycle time reduction.
 
Key Information and Communication Technology innovations and their implementation in logistics 
 
ICT Innovation 
Nature of innovation
Intended effects
Final impact
Technology  Level 
Analytics as a Service, including:
 - machine learning 
- deep learning
Deriving meaning from very large amounts of heterogeneous data for operational control. Includes descriptive, diagnostic, predictive and prescriptive analytics.
Obviates complex modelling and gives a rapid turnaround of measurements to situation analysis and advice for sense-and-respond systems.
Tactical and operational control of transport and logistics systems (FMS, WMS, Transportation forecasting). Possibly also strategic business intelligence.
8
Cloud/Fog Computing 
Computing power and databases made available as a service
Access to high quality and capacity services for all.
Necessary to host platforms for data exchange and software for analytics, such as crowdshipping, shared data or business process as-a-service platforms. 
9
Internet of Things (IoT) 
Objects can sense, actuate and communicate over the Internet.
Global autonomous sensing and actuation networks
All areas where intelligent objects are useful (Robotisation, Mobile Asset Optimisation, Warehouse Control Systems, Temperature Control etc.).
5
Blockchain
Securely shared, collectively governed database of all transactions (“distributed ledger”).
Zero time lag between action and information; no intermediary; installs trust for trade and cooperation in large groups.
Smart contracts for service delivery, product traceability, e-compliance, supply chain finance, supply chain visibility.
5
Big Open Data
Access to pooled data for purposes of visibility and analysis.
Pre-condition for analytics.
E-governance, Supply chain visibility, statistics, research, analytics. 
8
Augmented Reality/Virtual Reality
Context visualization through screens or wearables for higher situational awareness.
Increase of performance for operational tasks in regular and disturbance conditions.
Complex operational environments in transport, warehousing, production.
8
Traffic Control Towers and Intelligent Transport Systems
Merger of Fleet Management System (FMS) and Transport Management Software (TMS) navigation apps with traffic management ICT applications.
Improved responsiveness of logistics to traffic conditions.
Shorter and more reliable travel times for all modes.
9
 
Source:
Innovation and Technology in Multimodal Supply Chains-2018