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.
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.
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:
IBM. (2018, February). Global Chief Supply Chain Officer Study. Available from this URL
Geriant, J. (2015, October). The Changing Face of Supply Chain Risk Management. SCM World.
IBM & IDC. (2017, March). The Thinking Supply Chain. Available from this URL
As Industry 4.0 and Digital Transformation efforts bear their first fruits, capabilities, business models, and the organizations that embody them are transforming. A century ago, we thought of organizations as machines to be rigidly designed and controlled. In the latter part of the 20th century, organizations were thought of as knowledge to be cultivated, shared, and expanded. But “as intelligent systems gain traction, we are once again at a crossroads – where organizations must create complete and meaningful experiences” for their customers, stakeholders, and employees.
“Participatory Art” doesn’t just mean creating things that are pretty to look at in your office lobby or tradeshow booth. It means finding ways to connect with your audience in ways that help them find meaning, purpose, and self-awareness – the ultimate ingredient for authentic engagement.
Designing experiences to make this happen is challenging, but totally within reach. Learn more in today’s new article!
Each year, the second Thursday of November day is set aside to reflect on the way quality management can contribute to our work and our lives. Led by the Chartered Quality Institute (CQI) in the United Kingdom, World Quality Day provides a forum to reflect on how we implement more effective processes and systems that positively impact KPIs and business results — and celebrate outcomes and new insights.
This year’s theme is “Quality: A Question of Trust”.
We usually think of quality as an operations function. The quality system (whether we have quality management software implemented or not) helps us keep track of the health and effectiveness of our manufacturing, production, or service processes. Often, we do this to obtain ISO 9001:2015 certification, or achieve outcomes that are essential to how the public perceives us, like reducing scrap, rework, and customer complaints.
But the quality system encompasses all the ways we organize our business — ensuring that people, processes, software, and machines are aligned to meet strategic and operational goals. For example, QMS validation (which is a critical for quality management in the pharmaceutical industry), helps ensure that production equipment is continuously qualified to meet performance standards, and trust is not broken. Intelex partner Glemser Technologies explains in more detail in The Definitive Guide to Validating Your QMS in the Cloud. This extends to managing supplier relationships — building trust to cultivate rich partnerships in the business ecosystem out of agreements to work together.
This also extends to building and cultivating trust-based relationships with our colleagues, partners, and customers…
What’s Quality 4.0, why is it important, and how can you use it to gain competitive advantage? Did you know you can benefit from Quality 4.0 even if you’re not a manufacturing organization? That’s right. I’ll tell you more next week.
Want to find out what Quality 4.0 really is — and start realizing the benefits for your organization? If so, check out the October 2018 issue of ASQ’s Quality Progress, where my new article (“Let’s Get Digital“) does just that.
Quality 4.0 asks how we can leverage connected, intelligent, automated (C-I-A) technologies to increase efficiency, effectiveness, and satisfaction: “As connected, intelligent and automated systems are more widely adopted, we can once again expect a renaissance in quality tools and methods.” In addition, we’re working to bring this to the forefront of quality management and quality engineering practice at Intelex.
Quality 4.0 Evolution
The progression can be summarized through four themes. We’re in the “quality as discovery” stage today:
Quality as inspection: In the early days, quality assurance relied on inspecting bad quality out of items produced. Walter A. Shewhart’s methods for statistical process control (SPC) helped operators determine whether variation was due to random or special causes.
Quality as design: Next, more holistic methods emerged for designing quality in to processes. The goal is to prevent quality problems before they occur. These movements were inspired by W. Edwards Deming’s push to cease dependence on inspection, and Juran’s Quality by Design.
Quality as empowerment: By the 1990’s, organizations adopting TQM and Six Sigma advocated a holistic approach to quality. Quality is everyone’s responsibility and empowered individuals contribute to continuous improvement.
Quality as discovery: Because of emerging technologies, we’re at a new frontier. In an adaptive, intelligent environment, quality depends on how:
quickly we can discover and aggregate new data sources,
effectively we can discover root causes and
how well we can discover new insights about ourselves, our products and our organizations.”
In previous articles, we introduced Quality 4.0, the pursuit of performance excellence as an integral part of an organization’s digital transformation. It’s one aspect of Industry 4.0 transformation towards intelligent automation: smart, hyperconnected(*) agents deployed in environments where humans and machines cooperate and leverage data to achieve shared goals.
Automation is a spectrum: an operator can specify a process that a computer or intelligent agent executes, the computer can make decisions for an operator to approve or adjust, or the computer can make and execute all decisions. Similarly, machine intelligence is a spectrum: an algorithm can provide advice, take action with approvals or adjustments, or take action on its own. We have to decide what value is generated when we introduce various degrees of intelligence and automation in our organizations.
How can Quality 4.0 help your organization? How can you improve the performance of your people, projects, products, and entire organizations by implementing technologies like artificial intelligence, machine learning, robotic process automation, and blockchain?
A value proposition is a statement that explains what benefits a product or activity will deliver. Quality 4.0 initiatives have these kinds of value propositions:
Augment (or improve upon) human intelligence
Increase the speed and quality of decision-making
Improve transparency, traceability, and auditability
Anticipate changes, reveal biases, and adapt to new circumstances and knowledge
Evolve relationships and organizational boundaries to reveal opportunities for continuous improvement and new business models
Learn how to learn; cultivate self-awareness and other-awareness as a skill
Quality 4.0 initiatives add intelligence to monitoring and managing operations – for example, predictive maintenance can help you anticipate equipment failures and proactively reduce downtime. They can help you assess supply chain risk on an ongoing basis, or help you decide whether to take corrective action. They can also improve help you improve cybersecurity: documenting and benchmarking processes can provide a basis for detecting anomalies, and understanding expected performance can help you detect potential attacks.
(*) Hyperconnected = (nearly) always on, (nearly) always accessible.