Reducing Power and Dependency in Supply Chains Cloud

Roger OakdenLogistics Management, Logistics Planning, Procurement, Supply Chains & Supply NetworksLeave a Comment

Business plan thinking

Risks when relying on the Cloud

Data and information have value, so organisations need to ideally own, manage and control their supply chain data and information. But this can be a challenge in a Cloud based IT world.

The continual message about ‘partners’ and ‘partnerships’ in business can hide the fact that suppliers are in business to make a profit. A more typical profit aim of Cloud based IT application providers is to acquire and monetise data and information – called the ‘harvest and hoard’ business model.

In contract negotiations for Cloud services, Procurement must identify the balance of power and dependency between the parties. While there are benefits in using the Cloud, there are also risks to relying on the Cloud, at three levels – data, information and knowledge.

The supply chains raw data is collected within your business in two areas:

  1. assets (typically connected sensors attached to equipment communicating in real-time, increasingly via the Industrial Internet of Things (IIoT)
  2. the movement tracking and storage of items (materials, WIP and finished goods); also, the IIoT based condition monitoring of items

Using applications, the data becomes information, which is an input for planning supply chains. To improve planning requires knowledge gained from the analysis of information and data. The question is, which organisation should manage these tasks – the brand owner or an IT services provider?

Manage data and information

When assessing your supply network planning and analysis (SNAP) applications, identify them as either planning or analysis.

SNAP Applications in Supply Chains

In an ideal situation, data and information inputs for planning supply chains will remain inside your business. Knowledge to be gained from detailed analysis of the data and information could be provided by external services. This is because elements of Artificial Intelligence (AI), such as Machine Learning and Neural Learning, will be more developed inside specialist service organisations.

The desired situation is likely to be a viable scenario in the near future, enabled by three technologies that are not new. The growth in data volumes and communications capability have combined to make implementation possible. The technologies are Edge computing, 5G communications and Data Visualisation.

Edge computing

Two terms that will be used regularly are:

  • Bandwidth: the amount of data that can be transmitted in a given amount of time. Bandwidth controls the number of possible connections and enables high-bandwidth applications, such as video
  • Latency: the time taken for a signal to be delivered from a device to a computing capability, analysed and a response. Low latency is required where fast data analysis and response are critical

The projected increase in the number of IIoT devices will produce an extreme amount of data. But, the volume of data cannot be processed with acceptable transfer rates and response times at data centres, which may be located thousands of kilometres away from the place where the data is generated.

Commercial and Control Systems linked

Where the requirement is for real-time processing of ‘instant’ data, generated by sensors, devices or users, computation capability needs to move away from data centres to the edge of the network and close to the devices. This allows local processing and storage of data, with caching of information content for forwarding to data centres. Edge computing is therefore complementary to the Cloud concept.

According to IBM, the need to connect devices, employees, suppliers and customers in a decentralised environment will require three types of Edge computing – Cloud Edge; IIoT Edge and Mobile Edge that utilises 5G communications.

5G communications

The standards for 5G have defined three classes of service over different frequency ranges: low frequency; medium frequency and high frequency. The higher the frequency, the faster the data transmission, but high frequencies are more difficult to implement. The initial implementations are therefore focussed on low frequency applications which can be implemented on the existing telecommunications infrastructure. Medium and high frequency standalone 5G networks will be deployed by telcos over the next few years.

When this occurs, 5G services will be enabled that run on Edge servers, close to devices (called cloud-native microservices). This will allow for at least two types of 5G to be provided:

  • When low latency and high reliability are not critical, but low power consumption is required. An example is where large numbers of IIoT devices talk to each other or to Edge computers, such as at a tank farm equipped with sensors e.g. flow meters and load cells
  • When low latency and high reliability are essential. IIoT devices that require data for 30 seconds or less, with a response time of under 10 milliseconds. A automated distribution centre is an example.

As 5G is introduced, Wi-Fi 6 will also become available. This will be about three times faster than Wi-Fi 5 (also known as 802.11ac) and also able to communicate with more devices. However, because high frequency 5G has a lower latency than Wi-Fi and supports more devices within a small area, it will likely be preferred for industrial (including logistics) environments. However, both technologies will be deployed to address different outcomes.

At the other end of the speed scale is the 0G (Zero generation) network. This is a low-bandwidth wireless network designed for basic, low power and low cost IIoT devices to connect via the Internet to send and receive non time-critical small messages over a long distance. For example, it can be used for condition monitoring related to temperature/humidity/time for short shelf-life items in containers and trucks.

Graphic Visualisation

Bringing supply chain professionals, Edge computing and 5G together is the database technology for graphic visualisation. This is the presentation of data and information using visual elements of maps, charts, graphs etc. to help in the analysis of data and understanding of patterns, trends and outliers.

Supply Chains Data Visualisation

These applications should also enable Logisticians to graphically analyse and view the nodes, links and interdependencies between all parties, locations and assets in their supply network.

These three technology approaches can assist in the improvement of supply chain planning and operations, while reducing the risks concerned with ownership and control of information. However, they will only provide value when supply chain teams are knowledgeable of and using supply chain principles. Technology alone will not overcome your challenges.

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About the Author

Roger Oakden

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With my background as a practitioner, consultant and educator, I am uniquely qualified to provide practical learning in supply chains and logistics. I have co-authored a book on these subjects, published by McGraw-Hill. As the program Manager at RMIT University in Melbourne, Australia, I developed and presented the largest supply chain post-graduate program in the Asia Pacific region, with centres in Melbourne, Singapore and Hong Kong. Read More...

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