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
One of the heuristics we use at Intelex to guide decision making is former US President Truman’s advice that “imperfect action is better than perfect inaction.” What it means is — don’t wait too long to take action, because you don’t want to miss opportunities. Good advice, right?
When I share this with colleagues, I often hear a response like: “that’s dangerous!” To which my answer is “well sure, sometimes, but it can be really valuable depending on how you apply it!” The trick is: knowing how and when.
Here’s how it can be dangerous. For example, statistical process control (SPC) exists to keep us from tampering with processes — from taking imperfect action based on random variation, which will not only get us nowhere, but can exacerbate the problem we were trying to solve. The secret is to apply Truman’s heuristic based on an understanding of exactly how imperfect is OK with your organization, based on your risk appetite. And this is where loss functions can help.
Along the way, we’ll demonstrate how to do a few important things related to plotting with the ggplot package in R, gradually adding in new elements to the plot so you can see how it’s layered, including:
Plot a function based on its equation
Add text annotations to specific locations on a ggplot
Draw horizontal and vertical lines on a ggplot
Draw arrows on a ggplot
Add extra dots to a ggplot
Eliminate axis text and axis tick marks
What is a Loss Function?
A loss function quantifies how unhappy you’ll be based on the accuracy or effectiveness of a prediction or decision. In the simplest case, you control one variable (x) which leads to some cost or loss (y). For the case we’ll examine in this post, the variables are:
How much time and effort you put in to scoping and characterizing the problem (x); we assume that time+effort invested leads to real understanding
How much it will cost you (y); can be expressed in terms of direct costs (e.g. capex + opex) as well as opportunity costs or intangible costs (e.g. damage to reputation)
Here is an example of what this might look like, if you have a situation where overestimating (putting in too much x) OR underestimating (putting in too little x) are both equally bad. In this case, x=10 is the best (least costly) decision or prediction:
# describe the equation we want to plot
parabola <- function(x) ((x-10)^2)+10
# initialize ggplot with a dummy dataset
p <- ggplot(data = data.frame(x=0), mapping = aes(x=x))
p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) +
xlab("x = the variable you can control") +
ylab("y = cost of loss ($$)")
In regression (and other techniques where you’re trying to build a model to predict a quantitative dependent variable), mean square error is a squared loss function that helps you quantify error. It captures two facts: the farther away you are from the correct answer the worse the error is — and both overestimating and underestimating is bad (which is why you square the values). Across this and related techniques, the loss function captures these characteristics:
Not all loss functions have that general shape. For classification, for example, the 0-1 loss function tells the story that if you get a classification wrong (x < 0) you incur all the penalty or loss (y=1), whereas if you get it right (x > 0) there is no penalty or loss (y=0):
# set up data frame of red points
d.step <- data.frame(x=c(-3,0,0,3), y=c(1,1,0,0))
# note that the loss function really extends to x=-Inf and x=+Inf
ggplot(d.step) + geom_step(mapping=aes(x=x, y=y), direction="hv") +
geom_point(mapping=aes(x=x, y=y), color="red") +
xlab("y* f(x)") + ylab("Loss (Cost)") +
ggtitle("0-1 Loss Function for Classification")
Use the Loss Function to Make Strategic Decisions
So let’s get back to Truman’s advice. Ideally, we want to choose the x (the amount of time and effort to invest into project planning) that results in the lowest possible cost or loss. That’s the green point at the nadir of the parabola:
What this means is — if we don’t plan, or we plan just a little bit, we incur high costs. We might make the wrong decision! Or miss critical opportunities! But if we plan too much — we’re going to spend too much time, money, and/or effort compared to the benefit of the solution we provide.
The trick is to FIND THAT CRITICAL LEVEL OF TIME and EFFORT invested to gain information and understanding about your problem… and then if you’re going to err, make sure you err towards the left — if you’re going to make a mistake, make the mistake that costs less and takes less time to make:
The moral of the story is… imperfect action can be expensive, but perfect action is ALWAYS expensive. Spend less to make mistakes and learn from them, if you can! This is one of the value drivers for agile methodologies… agile practices can help improve communication and coordination so that the loss function is minimized.
## FULL CODE FOR THE COMPLETELY ANNOTATED CHART ##
# If you change the equation for the parabola, annotations may shift and be in the wrong place.
parabola <- function(x) ((x-10)^2)+10
my.title <- expression(paste("Imperfect Action Can Be Expensive. But Perfect Action is ", italic("Always"), " Expensive."))
arrow.x <- c(10, 10, 10, 10)
arrow.y <- c(35, 50, 65, 80)
arrow.x.end <- c(6, 6, 6, 6)
arrow.y.end <- arrow.y
d <- data.frame(arrow.x, arrow.y, arrow.x.end, arrow.y.end)
p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) +
xlab("Time Spent and Information Gained (e.g. person-weeks)") + ylab("$$ COST $$") +
annotate(geom="text", x=10, y=5, label="Some Effort, Lowest Cost!!", color="darkgreen") +
geom_point(aes(x=10, y=10), colour="darkgreen") +
annotate(geom="text", x=0, y=100, label="$$$$$", color="green") +
annotate(geom="text", x=0, y=75, label="$$$$", color="green") +
annotate(geom="text", x=0, y=50, label="$$$", color="green") +
annotate(geom="text", x=0, y=25, label="$$", color="green") +
annotate(geom="text", x=0, y=0, label="$ 0", color="green") +
annotate(geom="text", x=2, y=0, label="minutes\nof effort", size=3) +
annotate(geom="text", x=20, y=0, label="months\nof effort", size=3) +
annotate(geom="text",x=3, y=85, label="Little (or no) Planning\nHIGH COST", color="red") +
annotate(geom="text", x=18, y=85, label="Paralysis by Planning\nHIGH COST", color="red") +
geom_vline(xintercept=0, linetype="dotted") +
geom_hline(yintercept=0, linetype="dotted") +
geom_segment(data=d, mapping=aes(x=arrow.x, y=arrow.y, xend=arrow.x.end, yend=arrow.y.end),
arrow=arrow(), color="blue", size=2) +
annotate(geom="text", x=8, y=95, size=2.3, color="blue",
label="we prefer to be\non this side of the\nloss function") +
Now sometimes you need to make this investment! (Think nuclear power plants, or constructing aircraft carriers or submarines.) Don’t get caught up in getting your planning investment perfectly optimized — but do be aware of the trade-offs, and go into the decision deliberately, based on the risk level (and regulatory nature) of your industry, and your company’s risk appetite.
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!
Like a champion rowing team, your organization needs to make sure everyone is working together, engaged in synchronized work and active collaboration, and not working at cross-purposes.
But like risk management, working on alignment can seem like a luxury. No one really has time to slow down and make sure everyone’s moving in the same direction. And besides, alignment just happens naturally if each functional area knows what they’re supposed to be working on… right?
Neither of these statements are, of course, true. Synchronizing people and processes – and making sure they’re aware of the needs and desires of real customers instead of cardboard personas – takes dedicated effort and a commitment from senior leaders. There are other critical impacts too: lack of alignment negatively impacts not only project outcomes – but also professional relationships and the bottom line.
An Example of Diagnosing Misalignment
Although alignment is a many-to-many problem, and requires you to look at relationships between people in all your functional areas, a January 2018 survey from Altify examined one part of the organizational puzzle: alignment between sales and marketing. This is a big one, because sales teams use marketing materials to understand and sell the product or service your company offers. Their survey of 422 enterprise-level executives and sales leaders showed that:
74% of marketers think they understood customer needs, but only 44% of sales people in their organizations agreed
71% of marketers think sales and marketing are aligned, but only 59% of sales people in their organizations agreed
These differences may seem small, but they reveal a lack of alignment between sales and marketing. One group thinks they “get it” – while people in the other group are just shaking their heads.
Symptoms of Misalignment
…include things like:
of Fear. Your organization has a strategic plan (knows WHAT it wants to do),
but there is little to no coordination regarding HOW people across the
organization will accomplish strategic objectives. You know what KPIs you’re
supposed to deliver on, but you don’t know how exactly you’re supposed to work
with anything in your power or control to “move the needle.”
Tower Syndrome. You’re in a meeting and get the visceral sense that things
aren’t clear, or that different people have different expectations for a
project or initiative. But you’re too nervous or uncertain to ask for clarification
– or maybe you do ask, but you get an equally unclear answer.
Naturally, you assume that everyone in the room is smarter than you (particularly
the managers) so you shut up and hope that it makes sense later. The reality is
that you may be picking up on a legitimate problem that’s going to be problematic
for the organization later on.
A department committed you to a task, but you weren’t part of that decision. Once
you find out about it, the task just may not get done. Alternatively, you’ll
have to adjust your workload and reset expectations with the stakeholders who
will now be disappointed that you can’t meet their needs according to the
original schedule. Or maybe work evenings and weekends to get the job done on
time. Either way, it’s not pleasant for anyone.
How often are you called on to respond to something that’s absolutely needed by close of
business today? How often are you expected to drop everything and take care
of it? How often do you have to work nights and weekends to make sure you don’t
In this scenario, key stakeholders are called into projects at the 11th
hour, when they are unable to guide or influence the direction of an
initiative. The initiative becomes a “dead man walking” that’s doomed to an
untimely end, but since the organization has sunk time and effort into it, people
will push ahead anyway.
Cut Off at
the Pass. Have you ever been working on a project and find out – somewhere in
the middle of doing it – that some other
person or team has been working on the same
thing? Or maybe they’ve been working on a different project, but it’s ultimately
at cross purposes with yours. Whatever way this situation works out, your
organization ends up with a pile of waste and potential rework.
That’s the subject for more blog posts that will be coming this spring – as well as what causes misalignment in the first place (hint: it’s individual behaviors on an organizational scale). The good news is – misalignment can be fixed, and the degree of alignment can be measured and continuously improved. Sign up to follow this blog so you don’t miss the rest of the story.
What other symptoms of misalignment have you experienced?