Selling technologies

Commercial IT systems have been sold for about 70 years and through that period new technologies have been introduced. Another decade has commenced, but does it change the approach to identifying and adopting ‘new’ technologies?

To prosper, businesses that develop new IT hardware and software must bring the technology to the attention of potential buyers and then sell the product. A sales technique that gained notoriety in the early days of computers was to sell FUD (fear, uncertainty and doubt).  Fear factors included customers not having a good understanding of the particular technology and FOMO (fear of missing out); when your competitors purchase the technology and gain an advantage. Over this decade, the challenge for developers of new technologies and the sales approach will not change.

In supply chains, the approach to buying technology will differ by type of user:

• Logistics Service Providers (LSPs), including eCommerce businesses, have logistics as their core business. They are more likely to invest in ‘new’ technologies that are relevant to improving customer experiences and process efficiency
• At shippers, the supply chain functions of Procurement, Operations Planning and Logistics must compete for investment funds with other functions. Structuring a ROI and gaining internal support for proposals concerning ‘new’ technologies and supply chain focused applications can be difficult

‘New’ technologies

Software technologies

• Analytics:
  • Descriptive (explanation for success or failure in the past)
  • Predictive (enable decisions through analysing ‘big data’ within and between connected devices) and
  • Prescriptive (data supported options provided for evaluation and decision)
• Artificial Intelligence – decision-making enabled by using computers to learn in real-time from data provided by other digital technologies and human input
• Cloud computing and remote storage
• Distributed ledger applications (also called Blockchain)
• Optimisation tools for inventory and supply networks

Hardware technologies (industrial automation, promoted as Industry 4)

• Additive manufacturing (also called 3D printing)
• Augmented and virtual reality
• Autonomous vehicles; automated guided vehicles (AGV) and drones
• Industrial Internet of Things (IIoT) – enable operational data to be transmitted, normalised and structured for online analysis. In supply chains:
  • eCommerce warehouses: IIoT is required to enable operations at speed
  • Transport units: GPS used for vehicle tracking and sensors for refrigeration, heat and moisture content. Also driver behaviour alerts e.g. impacts that could damage the product, tyre wear and concentration in traffic:
    • Sensors
    • Instruments and programmable logic controllers (PLC)
    • Materials handling equipment (MHE)
    • Wearable and mobile technology
    • Automatic identification and data capture (AIDC), including: barcode scanners, optical character recognition (OCR), graphical user interface (GUI) and radio frequency identification devices (RFID)
• Robotics

Control of industrial automation applications is through a Supervisory Control and Data Acquisition (SCADA) system. This communicates with both SQL databases and the above operational hardware equipment, providing the bridge between technical and commercial systems.

Technologies in the hype cycle

Technologies can have a long gestation time from concept through ‘develop and test’ to launch. This is followed by the ‘hype cycle’, a life-cycle process identified (but not scientifically) by the research and advisory firm Gartner.

The technologies currently promoted as being ‘new’ have been in existence for a few years. Learn About Logistics considers them to be at different stages in the ‘hype cycle’ – you could have a different opinion:

Peak of inflated expectations:when the hype of the new technology, its multiple uses and ease of implement promotion has a reality check of reduced expectations:

Analytics – the challenge is identifying what is valuable data; accessing to the data; poor structuring of the data and an inability to understand the data. Attracting people with the required skillset as employees or contractors

Artificial Intelligence (AI): How to verify and validate an algorithm for bias? How will the performance of an algorithm be measured over time as it scales and absorbs and uses new data?

For supply chains, analytics and AI will be incorporated into software applications and sold as a service by larger 3PLs and specialist consultancies. Less likely will be an organisation developing applications for internal use.

Optimization tools for inventory and supply networks – the challenge of integration with a diverse set of systems and technologies

Augmented and virtual reality – although some interesting proof of concept applications, what uses are in supply chains that justifies the development investment?

Trough of disillusionment: when too many ‘proof of concept’ projects have few longer term achievements.

Distributed ledger applications (also called Blockchain) – some of the challenges identified at the ‘proof of concept’ ptojects:

• Interoperability: the capability of applications supplied by different software suppliers to accept, reformat and use data supplied from other applications
• Scalability: as more users interact, an application’s response times are reduced
• Extent  of legal language
• Jurisdiction for smart contracts
• Party which is ultimately responsible for the ledger

The most suited applications for Blockchain technology appear to be in supply chains, based on a ‘one to many’ relationship of each party in a supply chain:

• Smart contracts executed automatically, based on the fulfilment of certain conditions. Track and trace items (using IIOT) through supply chains:
  • address the UN Sustainable Development Goals (SDGs) as part of a corporate Environmental, Social and Governance (ESG) strategy
  • Origin of materials (lineage or provenance) i.e. conflict materials
  • Reverse logistics (warranty and eCommerce returns)
• Transport: electronic Bill of Lading which include standards

Additive manufacturing (also called 3D printing)

Volker Hammes, Managing Directorof BASF 3D Printing Solutions said at a recent conference: “There is a limited choice of industrial printers. Most lack speed, robustness and reproducibility. Solutions are not, as yet, scalable, certification standards are not yet there, nor is there proper IP protection, while there is a lack of industry-specific experienced design engineers and not enough data sets for AI in the design process”.

Slope of enlightenment: There is an understanding among developers and users of the potential and limitations of the technology.

• Autonomous vehicles; automated guided vehicles (AGV) and drones
• Industrial Internet of Things (IIoT)
• Robotics

For IIOT and Robotics, standardisation is slow, as each provider has its own standards, communication protocols and capabilities

Plateau of productivity: The technology is accepted and used and therefore no longer considered as ‘new’.

• Cloud computing and remote storage

As the physical and digital worlds continue to merge, artificial intelligence (incorporating analytics and machine learning) will be necessary technologies to bring digital to the closest proximity of reality. However, progress in the use of technology is usually slower than commentators forecast and that applies to supply chains.