The Connected, Intelligent, Automated Industry 4.0 Supply Chain
ASQ’s March Influential Voices Roundtable asks this question: “Investopedia defines end-to-end supply chain (or ‘digital supply chain’) as a process that refers to the practice of including and analyzing each and every point in a company’s supply chain – from sourcing and ordering raw materials to the point where the good reaches the end consumer. Implementing this practice can increase process speed, reduce waste, and decrease costs.
In your experience, what are some best practices for planning and implementing this style of supply chain to ensure success?“
Supply chains are the lifeblood of any business, impacting everything from the quality, delivery, and costs of a business’s products and services to customer service and satisfaction to ultimately profitability and return on assets.
Stank, T., Scott, S. & Hazen, B. (2018, April). A SAVVY GUIDE TO THE DIGITAL SUPPLY CHAIN: HOW TO EVALUATE AND LEVERAGE TECHNOLOGY TO BUILD A SUPPLY CHAIN FOR THE DIGITAL AGE. Whitepaper, Haslam School of Business, University of Tennessee.
Industry 4.0 enabling technologies like affordable sensors, more ubiquitous internet connectivity and 5G networks, and reliable software packages for developing intelligent systems have started fueling a profound digital transformation of supply chains. Although the transformation will be a gradual evolution, spanning years (and perhaps decades), the changes will reduce or eliminate key pain points:
- Connected: Lack of visibility keeps 84% of Chief Supply Chain Officers up at night. More sources of data and enhanced connectedness to information will alleviate this issue.
- Intelligent: 87% of Chief Supply Chain Officers say that managing supply chain disruptions proactively is a huge challenge. Intelligent algorithms and prescriptive analytics can make this more actionable.
- Automated: 80% of all data that could enable supply chain visibility and traceability is “dark” or siloed. Automated discovery, aggregation, and processing will ensure that knowledge can be formed from data and information.
Since the transformation is just getting started, best practices are few and far between — but recommendations do exist. Stank et al. (2018) created a digital supply chain maturity rubric, with highest levels that reflect what they consider recommended practices. I like these suggestions because they span technical systems and management systems:
- Gather structured and unstructured data from customers, suppliers, and the market using sensors and crowdsourcing (presumably including social media)
- Use AI & ML to “enable descriptive, predictive, and prescriptive insights simultaneously” and support continuous learning
- Digitize all systems that touch the supply chain: strategy, planning, sourcing, manufacturing, distribution, collaboration, and customer service
- Add value by improving efficiency, visibility, security, trust, authenticity, accessibility, customization, customer satisfaction, and financial performance
- Use just-in-time training to build new capabilities for developing the smart supply chain
One drawback of these suggestions is that they provide general (rather than targeted) guidance.
A second recommendation is to plan initiatives that align with your level of digital supply chain maturity. Soosay & Kannusamy (2018) studied 360 firms in the Australian food industry and found four different stages. They are:
- Stage 1 – Computerization and connectivity. Sharing data across they supply chain ecosystem requires that it be stored in locations that are accessible by partners. Cloud-based systems are one option. Make sure authentication and verification are carefully implemented.
- Stage 2 – Visibility and transparency. Adding new sensors and making that data accessible provides new visibility into the supply chain. Key enabling technologies include GPS, time-temperature integrators and data loggers.
- Stage 3 – Predictive capability. Access to real-time data from supply chain partners will increase the reliability and resilience of the entire network. Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), and radio frequency (RFID) tagging are enablers at this stage.
- Stage 4 – Adaptability and self-learning. At this stage, partners plan and execute the supply chain collaboratively. Through Vendor Managed Inventory (VMI), responsibility for replenishment can even be directly assumed by the supplier.
Traceability is also gaining prominence as a key issue, and permissioned blockchains provide one way to make this happen with sensor data and transaction data. Recently, the IBM Food Trust has demonstrated the practical value provided by the Hyperledger blockchain infrastructure for this purpose. Their prototypes have helped to identify supply chain bottlenecks that might not otherwise have been detected.
What should you do in your organization? Any way to enhance information sharing between members of the supply chain ecosystem — or more effectively synthesize and interpret it — should help your organization shift towards the end-to-end vision. Look for opportunities in both categories.
References for Connected, Intelligent, Automated stats:
