The immense volume of data generated throughout the value chain is shifting the supply chain models from linear and sequential to an interconnected and agile model. The traditional supply chains were a combination of electronic and paper-based processes and documentation, operating in functional and geographic silos. These silos locked up the data for individual trading partners leading to suboptimal performance.
The integration of digital technologies in the supply chain has created opportunities for visibility and collaboration in the value chain. With the integration of digital technologies, such as social, mobile, cloud, IoT, artificial intelligence, machine learning, and so on, the supply chain has become more collaborative, standardized, and integrated. It allows communication across functions resulting in superior reliability, agility, and effectiveness. This helps the stakeholders in making decisions with a holistic view.
Cloud computing enables storing of volumes of the supply chain data and the machine learning technologies help in finding insights from that data. In the enterprise supply chain, machine learning helps to accelerate the generation of business insights by integrating data from external partners, automating internal data classifications, and identifying patterns.
Infoholic Research states – Cloud Supply Chain Management Software market is expected to reach $7.03 billion by 2023, growing at a CAGR of 14.3% during the forecast period 2017–2023.
IoT in supply chain helps in implementing new operational and business models as the data from customers can be used as a direct input. Similarly, blockchain can automate trust through a distributed digital ledger database and automate transactions when previously set conditions are met.
The processes of supply chain wherein digitization can be implemented using such technologies include:
1. Supply Chain Integration: IT integration of the functions across the internal organization and the supply chain partners.
2. Supply Chain Automation: Radio or GSM tagging or tracing of goods, packaging, and containers. It also covers robotics and autonomous vehicles.
3. Supply Chain Reconfiguration: 3D Printing, additive manufacturing, and e-platforms.
4. Supply Chain Analytics: Big data analytics for the improvement of supply chain management.
The key areas where digitization is being implemented include demand forecasting and planning, networking, logistics and route optimization, freight document handling, warehousing, and transport management.
Some examples of the implementation of digitization in the supply chain include:
Hilti Corporation, a multinational company that develops, manufactures, and markets products for the construction, building maintenance, and mining industries, developed a comprehensive omnichannel system whose asset tracking uses smart supply chain to get real-time information on the availability of tools.
Amazon, one of the online retail giant, uses analytics in its fulfillment centers to build optimized space utilization, reduce order-to-delivery cycle times, and minimize time to find inventory.
BMW, one of the leading automotive companies, uses smart robotics, planning, and 3D simulation software to sell custom configured automobiles at competitive prices.
Tesco, a British multinational grocery and general merchandise retailer, reduced the chance of product stock outs by feeding weather data into its predictive analytics tool to forecast the demand of weather-dependent products.
However, implementing digital technologies exposes the supply chain to threats related to cybersecurity. The demand for continuous online communication creates opportunities for hackers to exploit vulnerable security practices. The companies implementing digital technologies in the supply chain need to have an updated technology defense to be in place.
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