IT overload: making sense of increasingly complex developments in supply chain IT

We are Supply Chain and Logistics Consultants who have been in business for over 30 years and have a very successful track record and a high rate of repeat business.

As an independent consultancy, we are bombarded with an ever-increasing number of IT companies marketing their latest supply chain IT products in what has become a crowded sector. Buzzwords such as ‘digitalization’, ‘transformation’, ‘AI’, ‘blockchain’ and ‘digital twinning’ abound. Functionally overlapping products and marketing content that plays fast and loose with newly introduced industry terms only add to the complicating mix.

So, we wanted to get behind the hype and create a simplified summary, in layman’s terms, of what the important new technologies are and the impact they may have on future supply chains. Here, we concentrate on six new and interrelated technologies that are likely to have the biggest impact on the development of future supply chains – either on their own or, more likely, when applied in combination.

Big Data (analytics)

The ‘Big Data’ analytics opportunity is being created by the advent of more data-generating devices in the supply chain. These devices can capture many different types of data, both internally and externally and either automatically or manually. This is characterised by the increasing ‘3Vs’ of data management (volume/velocity/variety) and takes us into the relatively new world of data science. This provides the opportunity to treat your data as a true business asset and derive additional value from its analysis. To achieve this, companies must – as well as having the technological solution – create a data management strategy to make best use of the opportunities. This could involve creating new KPIs and generating proper, actionable insights from an improved Business Intelligence facility or trend analyses from very large datasets, e.g. RFID-tracked goods movements in high-density storage or predicting the logistics impacts of promotional activity in retail logistics operations with high transaction volumes.

Blockchain

This is probably one of the most over-hyped developments in the supply chain and is also often confused with Bitcoin, which uses the same technology but is definitely not the same thing. Blockchain is basically a distributed (i.e. decentralised) digital ledger, which is cryptographically secured and immutable. Each transaction added to it is time stamped and has a cryptographic key signature (i.e. is counterfeit-proof). Blockchains can be public (open to all) or private (with permissioned access).

The application of blockchain technology within supply chains is still under debate but certainly areas related to the need for trusted and secure global trading processes – providing additional audit and document verification opportunities – fit well. Blockchain enables the coding of contracts that are automatically executed when specified conditions are met, avoiding the need for third parties such as lawyers and bankers. These ‘smart contracts’ have the potential to make blockchain revolutionary in the supply chain.

AR/VR (Augmented Reality/Virtual Reality)

AR and VR involve the expansion or replacement of physical reality through presentation of computer-generated information such as objects, text, videos, graphics or sound. The difference between AR and VR is that VR has no physical reality component at all. The four key functions of AR are (i) scene capture; (ii) scene identification – scanning the scene and deciding where to add content; (iii) scene processing – obtaining the virtual content; and (iv) scene visualisation – adding in the content.

Applications for AR/VR in a supply chain context can range from use in high-efficiency picking operations (using augmented reality, or ‘smart’, glasses) to use as a training aid or as part of a remote maintenance process for materials handling equipment.

AI/ML/DL (Artificial Intelligence/Machine Learning/Deep Learning)

AI is the overarching term, which includes both ML and DL as sub-sets. ML is specifically defined as being applied to machines, which can learn on their own without the need for explicit programming and hence are able to improve their responses as they learn. DL is a much more complex and specific application, based on creating neural networks and mimicking the way the human brain actually works.

At its core, AI is a method of training algorithms to learn and be able to make accurate predictions and hence decisions. AI is categorised as general (covering a range of activities) or narrow (applied to a narrowly defined task), with the latter covering the vast majority of AI applications today. AI ties in directly with Big Data, where exploratory analytics can be used to identify trends that the business had previously been unaware of.

The advantages are obvious, as – with reduced or no human input – error rates and costs are both reduced. Decision-making is systemised, resulting in improved and faster business decisions. Its application in the supply chain can be huge – from demand planning and forecasting, through to process automation within a warehouse, including control of autonomous vehicles.

Digital Twinning

A digital twin is a virtual representation of a real physical thing, which has the objective of simulating its real-world behaviour. It should be connected to real-world operational outputs and hence be continually updated for present conditions. It can be applied to a warehouse, an activity or – in a wider scope – to your entire supply chain and the physical network that supports it.

Having an accurate digital twin allows what-if scenario planning and modelling, thus allowing prediction of real-world results. This facilitates supply chain optimization and could also be an important factor in de-risking future supply chains.

One practical consideration in creating wide-scope digital twins is the need for ‘data augmentation’, i.e. capturing data from a wide variety of sources, which requires validation, correction and collation before use – something that AI would help to automate.

5G and IoT (Internet of Things)

5G can be considered more as a facilitator of the other technologies. Recent improvements in cellular technology (5G), utilising higher-frequency areas of the electromagnetic spectrum, have resulted in 10 to 20 times faster data-transmission speeds and reduced time-lag latency. This facilitates a far greater degree of data collection across the network in real time, as there is far more bandwidth capacity to accommodate internet-connected smart devices and sensors (IoT). So, although IoT is not necessarily dependent on 5G, its application in the real world has been massively enhanced by it. Hence, it is an enabler of the other technologies, creating the environment in which much more data can be captured, processed at speed and then analysed and interpreted, either conventionally or through AI.

In summary, it is clear that these technologies can support each other and could be used in an integrated way, combining their functions to accelerate IT development even further. This concept is known as ‘convergence’ and provides a glimpse of how self-managing, fully secure and continuously optimising future supply chains might work.

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