Tag Archives: supply chain

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:
  1. IBM. (2018, February). Global Chief Supply Chain Officer Study. Available from this URL
  2. Geriant, J. (2015, October). The Changing Face of Supply Chain Risk Management. SCM World.
  3. IBM & IDC. (2017, March). The Thinking Supply Chain. Available from this URL

Blockchain and Quality

Quality is all about satisfying stated and implied needs –now, or in the future. When we envision and design high-quality products and services for the future, that’s innovation. One of the most hyped innovations of 2017 was blockchain, which has the potential to transform business models and the way quality is managed. The purpose of this article is to explain this relationship in a simple way.

Blockchain is the innovative technology supporting the Bitcoin cryptocurrency. Bitcoin gained tremendous traction in 2017, starting at just over $1,000 in January and reaching nearly $20,000 by the end of the year.  It increased in value so much over this time that it’s been compared to the Dutch tulip market bubble of the 1630s.  After tulips were imported into Holland from Turkey, an alteration to the solid colors of the tulips caused the appearance of “flames” on the petals. This made people believe that the tulip bulbs held extreme value, and so many people traded their land and their savings to invest in what they felt was a “sure thing” – to lose everything not long after, when the market corrected itself.

Bitcoin (USD) prices, 1/1/17-12/13/17. Generated using https://www.coindesk.com/price/.

Bitcoin (USD) prices, 1/1/17-12/13/17. Generated using https://www.coindesk.com/price/.

The blockchain technology that supports Bitcoin is, at its core, a database. It’s a special kind of database, but no more magical, really – and easier to contextualize if you think about innovations in database technology over the past two decades.

Databases can be roughly classified into these categories:

  • Relational databases (Oracle, MySQL, PostgreSQL, Sybase): When you can organize your data in terms of tables, fields, and relationships between those entities, a relational database is often appropriate. For example, your customer data might be kept in the “people” table with fields like address, state, or gender. Each record in the people table might have a type – employee, partner, or customer. Although records can be changed, it’s easy to accidentally input bad data, and it’s also easy to accidentally generate duplicate records. Scaling a relational database can also be rather tricky.
  • Non-relational (NoSQL) databases (MongoDB, Cassandra, Redis): If most of your data comes in large blobs and you don’t want to split it up into fields and tables, these databases are useful. MongoDB is great for collections of documents, such as web pages, log data, or tweets. Cassandra works well for analytics applications. Sensor data and other data types that change frequently or need to be held in active memory (for example, in key-value stores) are handled well by databases like Redis. NoSQL databases are easier to scale than relational databases.
  • Other databases and data stores with special properties: Some databases are so unique they don’t feel or act like databases. Solr, for example, is traditionally used when you have to provide search functionality over a store of documents. Hadoop is a distributed file system, so it functions somewhat like a database even though it’s not one. Graph databases are designed for data stores where the relationships are the most important aspect, so they are gaining popularity for social networks. Large, institutional science projects often store their data in special binary files that have distinct formats, can be queried like databases, and in many ways act like databases – but they are not technically databases.

 

What Distinguishes Blockchain-based Databases from Ordinary Databases?

First, the blockchain is designed to handle transactions – it’s a digital ledger. So it’s not surprising that its first “successful” use cases are in the realm of cryptocurrency, where people engage in transactions with one another to exchange something of value.

Next, this database is immutable, meaning you can’t go back and change earlier records. Every time a new transaction occurs, a cryptographically sealed “snapshot” is taken of the entire database. When I first heard this, I was worried: so that means if we accidentally enter something incorrect into the database, it can never be changed, right? And its presence is memorialized forever? The answer to this question is: sort of. Thanks to “smart contracts”, we shouldn’t ever be in the situation where bad data gets entered into our blockchain-based system, because incoming data will be checked (by multiple agents) against the smart contract — and only allowed to join the blockchain database if it meets all the quality requirements specified by the contract. It’s like a fancy way to implement validation rules – with the added benefit of being totally traceable. Imagine how nice it would be to trace all the steps in the process that brought the fresh fruit into your kitchen – or any other product you use — just because all transactions in the production process were logged into a “supply blockchain.”

A blockchain database is also decentralized and distributed — you don’t just “buy a blockchain database” and install it at your company. Databases can be centralized, decentralized, or distributed. Most business databases in the past were centralized: there was one instance installed, and a database administrator (or team of them) ensured the performance and security of the database while everyone in the organization created and used applications that interacted with the data. Today, these databases are more commonly distributed: there’s not just one instance, but several – there is no central storage, but there may be storage on many computers, or over a network of connected computers (or “in the cloud”). 

Decentralized systems have many advantages – for example, nodes can join or leave the network at will. For example, you can create a web site or take it off the internet whenever you want, if you own and control it. In decentralized systems, there is no single point of control. If a business wants to implement blockchain but also wants to control all the nodes, that should be a big red flag. By its nature, blockchain is decentralized just like the internet itself.

Finally, blockchain is transparent. Any of the participants who own nodes can see all the transactions — so there should be fewer opportunities for fraud. This doesn’t mean that there isn’t opportunity for danger, though.

 

Why is Blockchain Potentially Useful for Quality Assurance?

In addition to enhancing provenance and traceability, one of the biggest envisioned applications of blockchain databases is to support machine to machine transactions. As intelligent agents grow in complexity and are trusted to handle more tasks, and as the Internet of Things (IoT) expands, there needs to be a high-quality record of how those objects and agents interact with other objects and agents – and with humans. Blockchain could also be used to support new business models like decentralized energy markets, where you can consume energy from the local power plant, but also potentially generate your own and contribute the excess energy to your local community for a fee. It could potentially transform middleware as well, which is software that allows different software systems to communicate with one another. (A long time ago, someone told me that it’s like “email for applications” – they can send messages to one another so they know how to react, for example, when a company receives an order and several systems need to be alerted that the order has arrived.)

In principle, transactions logged to a blockchain make it impossible to defraud participants in the process, and impossible to manipulate records after they are recorded. They are self-auditing and fully traceable. Blockchain won’t make quality assurance, tracking, or auditing EASY, but you should expect it to make the business landscape different – new business models will be possible, and it will be possible to entrust intelligent agents with more tasks.  

Blockchain can help us ensure that stated and implied needs are met, and do it in such a way that the integrity of our data is assured simply by its presence. But we’re not there yet. Developers still need to implement simple, demonstrable use cases to make it easier for managers and executives to map these technologies onto specific business needs. In addition, blockchain is slow compared to relational database systems, so this needs to be addressed as well before widespread adoption.

 

Read more in our December 2017 SQP article.

The Origins of Just-In-Time

A couple weeks ago, the students in my ISAT 654 (Advanced Technology Management) class at JMU asked about where and when Just-In-Time (JIT) manufacturing actually started in the United States. Although I still can’t identify the FIRST company to adopt this approach, I was also curious about how the adoption of JIT in the US grew from the Toyota Production System (TPS).

Just-in-Time (JIT) is only one element of lean manufacturing, which is a broader philosophy that seeks to eliminate all kinds of waste in a process.  Although JIT is often considered an enterprise-wide philosophy of continuous improvement, I’d like to focus on the mechanistic aspects of JIT – that is, the development and operations of a production system that employs continuous flow and preventive maintenance. In an effectively implemented JIT production system, there is little or no inventory – which includes Work-In-Process (WIP) – and production is tightly coupled to demand.

The origin of JIT can be traced back to Henry Ford’s production line, in which he was keenly aware of the burdens of inventory. However, Ford’s production system generated large volumes of identical products created in large batches – there was no room for variety, and the system was not coupled to demand levels.

In post-war Japan, Taiichi Ohno (“Father of JIT”) adapted the system at Toyota to handle smaller batch sizes and more variety in the parts that could be used to construct assemblies. In 1952, work on their JIT system was initiated, with full deployment of the kanban pull system by 1962. This was the genesis of the Toyota Production System, an elegant (and sometimes elusive) socio-technical system for production and operations. This approach bridged the gaps between production and continuous improvement and became the basis for lean manufacturing as it is known today.

After the oil crisis in 1973, other Japanese companies started to take note of the success of Toyotaand the approach became more widely adopted. The JIT technique spread to the United States in the late 1970’s and 1980’s, but due to inconsistencies in implementation and a less mature grasp on the human and cultural elements of the Toyota Production System, western companies experienced limited success. The Machine that Changed the World by James Womack made the JIT+TPS concept more accessible to US companies in 1990, which led to the widespread adoption of lean manufacturing techniques and philosophies thereafter.

JIT is very sensitive to the external environment in which it is implemented. For a review of Polito & Watson’s excellent 2006 article that describes the key barriers to smooth JIT, read Shocks to the System: Financial Meltdown and a Fragile Supply Chain.

(P.S. Why the picture of butter? Because JIT, when implemented appropriately, is perfectly smooth and slippery and thus passes The Butter Test.)

I’ve Converted to OrderTopianism

Yesterday was really a fantastic day for me. In addition to starting it off right with a total solar eclipse at 2:11am ET, January 15th will go down in history as the first time I placed an order using OrderTopia. It will definitely not be the last time!

OrderTopia is a social, cloud-based ordering system that integrates directly into the point-of-sale (POS) systems at local merchants (such as restaurants). You place an order online, or with your mobile device, and the OrderTopia system automatically processes your payment and contacts the right people at the right places in the kitchen to construct your meal order.

The process improvement benefits are evident on both the customer and the merchant sides. As a customer, I don’t have to wait in line any more or keep giving out my credit card information – OrderTopia already has it as part of my account. I just walk into the restaurant at the time I said I’d pick up my order, and it’s there, ready for me to go. On the merchant side, all data quality issues between the time you place your order and the time it’s fulfilled (for example, the cashier misinterpreting what you say, or typing it wrong into the POS system) are erased. By eliminating those steps from the process of fulfilling your order, the path through the system is also shortened.

I can also sense that OrderTopia will improve my quality of life in the future. I won’t be spending valuable minutes waiting in line for lunch — nor will I spend a lot of time trying to figure out what I should order — I’ll just be clicking on a “favorite lunch” option on my Droid, specifying the time I want to pick it up, and then showing up to get my lunch. It will be like having a personal assistant, only it will be OrderTopia. I’ll be able to see what lunches my friends have ordered around town, find out who likes what, and track what I’ve eaten too. In the future, I’ll never worry about how long the line is at one of my favorite lunch places… or whether I’m going to miss out on Eppie’s Wednesday tamales because I showed up too late… OrderTopia will take care of it!

Quality and the Great Contraction

From the July 6, 2009 issue of Business Week:

“A new world order is dawning – one in which the West is no longer dominant, capitalism (at least the American version) is out of favor, and protectionism is on the rise… the era of laissez-faire economics is over, and statism, once discredited, is making a comeback – even in the U.S…. global trade is set to fall this year, for the first time in more than two decades.”

We have been conditioned to think that the notion of space – geographic space – does not matter in the new economy. We have the Internet, and ideas can zing from one place to another with ease (and nearly instantaneously, for that matter). Add to this videoconferencing with Skype, and keeping up with your contacts on Twitter and Facebook in near-real time, and it’s no wonder that people have also become accustomed to assuming that materials can move from one place to another with similar relative ease.

Space does matter. We know this when we are designing facilities and plant layouts, for example, because one of our common considerations is to minimize traffic between areas and departments. More often than not, we do this to minimize the time spent moving people or equipment around a plant, so that time is not wasted. But the same concept could apply to our supply chains. Why aren’t we minimizing the time that components or goods spend traveling through the supply chain, when it could lead to reductions in energy costs? Furthermore, why aren’t we shortening our supply chains to strengthen local and regional businesses, and train the next generation of skilled workers (who can actually do something useful for the regional economy)?

The logic has been something like this: energy is cheap, therefore transportation is cheap, and transportation is easily available and accessible through third-party providers like FedEx and UPS. But I can’t shake the feeling that “supply chain status quo” is not good for quality in the long-term – because it encourages us to source the products and components that are most affordable, rather than the ones that might help us cultivate a quality consciousness in our local areas.

Inspection, Abstraction and Shipping Containers

On my drive home tonight, a giant “Maersk Sealand” branded truck passed me on the highway. It got me thinking about how introducing a standard size and container shape revolutionized the shipping industry and enabled a growing global economy. At least that’s the perspective presented by Mark Levinson in The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger.

A synopsis of the story and a sample chapter are available; Wikipedia’s entry on containerization also presents a narrative describing the development and its impacts.

Here’s how impactlab.com describes it:

Indeed, it is hard to imagine how world trade could have grown so fast—quintupling in the last two decades—without the “intermodal shipping container,” to use the technical term. The invention of a standard-size steel box that can be easily moved from a truck to a ship to a railroad car, without ever passing through human hands, cut down on the work and vastly increased the speed of shipping. It represented an entirely new system, not just a new product. The dark side is that these steel containers are by definition black boxes, invisible to casual inspection, and the more of them authorities open for inspection, the more they undermine the smooth functioning of the system.

Although some people like to debate whether shipping containers were an incremental improvement or a breakthrough innovation, I’d like to note that a single process improvement step generated a multitude of benefits because the inspection step was eliminated. Inspection happened naturally the old way, without planning it explicitly; workers had to unpack all the boxes and crates from one truck and load them onto another truck, or a ship. It would be difficult to overlook a nuclear warhead or a few tons of pot.

To make the system work, the concept of what was being transported was abstracted away from the problem, making the shipping container a black box. If all parties are trustworthy and not using the system for a purpose other than what was intended, this is no problem. But once people start using the system for unintended purposes, everything changes.

This reflects what happens in software development as well: you code an application, abstracting away the complex aspects of the problem and attaching unit tests to those nuggets. You don’t have to inspect the code within the nuggets because either you’ve already fully tested them, or you don’t care – and either way, you don’t expect what’s in the nugget to change. Similarly, the shipping industry did not plan that the containers would be used to ship illegal cargo – that wasn’t one of the expectations of what could be within the black box. The lesson (to me)? Degree of abstraction within a system, and the level of inspection of a system, are related. When your expectations of what constitutes your components changes, you need to revisit whether you need inspection (and how much).

Supply Chains and Supply Networks

chainsSupply chains aren’t actually linear chains, but socio-technical systems that can be expressed as networks. In an October 2004 article in DM World, a data management magazine, Dennis Ladd expressed it well:

Today’s competitive, fast-moving business environment has irrevocably changed the supply chain and the management of its functions as we know it. The traditional “chain” of sourcing/production/distribution linked in a linear and simple fashion is no longer a reality given the complicated and global rate at which business is now conducted. Many industry pundits have even created new nomenclature: it is no longer a “supply chain” but rather a “supply network.”

For simplicity, we’ll refer to a supply chain and a supply network interchangeably. A supply chain is a process of transformation:

  • A process starts with Inputs (which could be raw materials, components, or data),
  • which are Transformed by adding value (through processing, assembly, or applying specialized knowledge) into
  • Outputs (work-in-process material, physical products, knowledge products, services)

People and software are involving in all phases of the supply chain: getting inputs, adding value, and producing outputs. Together, they manage three types of flows through the system: 1) the flow of materials, 2) the flow of information, and 3) the flow of funds. Not surprisingly, supply chains can be challenging to manage! Here are three factors that can influence supply chains:

Environment. A supply chain defines the connection of a company’s operations with the outside world. When that world changes, the supply chain can be impacted – whether the changes are on a large scale (e.g. the current U.S. financial crisis) or a small scale (your most critical supplier just implemented a new ERP system and half their office staff quit).

Interactions. Coordinating the elements of a supply chain means that people must interact with people, software must interact with software, and people must interact with software. Sometimes the people and the systems are based in different companies, with different pressures, priorities, interests and levels of experience.

Complexity. Supply chains are only as strong and healthy as their component processes. And all too often, those processes are overly complex. This complexity works itself into the underlying supply chain management (SCM) software systems, sometimes making those systems unwieldy. The remedy is to strive for simple, understandable processes that are easy to explain – to yourself and to others.

[For network junkies: if you express your linear supply chain as a network, it will end up looking like a bow tie, with your company in the middle of the bow. If you add into this network the interactions between people and software, and how they interact with each other and with each stage of the supply chain itself, the result will be a complex socio-technical network that will have to be analyzed statistically.]

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