For many decades we have understood disruption from technology and feared the changes disruption requires of us. For only a couple of years, however, have we understood that disruptions today, happen faster and faster, and from places, we never even knew existed. The question is: what are we disrupting for?

Supply Chain volatility has increased

It is obvious to everyone. The Supply Chain is changing. Changes that have the potential to change the entire business. The technological advances that we have seen over the past several decades have finally driven us to a tipping point. We are now on the precipice of a fourth industrial revolution that will truly transform business and all stakeholder relationships. The push toward the fourth industrial revolution has accelerated over the past two years. Not only are technological advances occurring at a fast and furious rate, but the level of volatility in the Supply Chain has also increased.

Persistent challenges from increasing customer demands, disruptive competitors, and economic fluctuations make the optimization of Supply Chain designs harder. Various megatrends mean that Supply Chain complexity and risk are growing. Decision-making speed and quality need to increase to enable faster recovery from disruptions. At the same time, there is a need to handle real-time data and complex business requirements across multiple networks – and balance risks and trade-offs.

Need for new capabilities across all timeframes

In the face of these raised levels of volatility, we have no choice option but to lead transformative changes to reach a Supply Chain Future-Fit stage. Simply going through the motions of change is no longer an option. Companies that make changes simply to make them – without having the desired endpoint in mind or following a systematic process will never achieve success.

The only way forward is to change most if not all the settings in the company at both the human and technical levels. Achieving a highly resilient company that can grow and prosper in todays’ uncertain environment will require new advanced capabilities across both the strategic (two to five years), tactical (one to 24 months), and operational (one to 30 days out) timeframes.

Reimagining the Supply Chain is key

The challenge is to achieve the ideals of fully integrated, efficient, and effective Supply Chains capable of creating and sustaining competitive advantages. Downward cost pressures must be balanced as well as the need to manage effective ways to manage the demands of market-driven service requirements. At the same time, there is a need for a resilient and transparent Supply Chain.

It could potentially mean reimagining and reconfiguring the network in terms of capacity, sourcing mix and location, manufacturing capacity, and location. It could potentially also mean adding to their classical S&OP processes a tactical scenario testing capability to be used during times of serious disruption. In other words, when conventional forecasting processes become unworkable and unreliable, it will allow the company to become more agile and resilient. Supported by end-to-end visibility and the ability to make decisions fast. Fast decision-making enhances the resilience of the entire company.

Technologies such as AI are transforming how decisions are made. More people no matter what their roles are can now have real-time access to the information and perspectives that they need to do their job. As a result, individuals and teams will be better able to manage themselves, work together, and the types of decisions without the involvement of management. Companies will not need to organize themselves in the traditional way. I.e., there is no need to ensure that decisions are aligned around its goals because that happens automatically.

The Supply Chain to be seen as a living system

Digitization is re-shaping the Future-Fit Supply Chain. It changes the culture to one which is more adaptive, resilient, innovative, and customer-centric. In other words, an organization that functions as a living system – not just a mechanical one. What’s standing in the way?

According to an article written by John Gattorna and Pat Mclagan in Supply Chain Quarterly, under the title: Supply Chain, the platform for driving true business transformation, three powerful mindsets continue to sustain the norms and behaviors that drive old cultures. Command and control relationships, silo identities, and the application of engineering approach. These powerful mindsets work against creating an environment that fosters initiative-taking, creativity, cooperation, and rapid problem-solving. These mindsets belong to the early 1900’s when the goals were efficiency and control, and people were viewed as machines whose behavior had to be controlled.  

Supply Chain executives must change their mindsets

Supply Chains are in a powerful position to drive this deep transformation in both the hard (structural and technical) as well as the soft (human and cultural) dimensions of the business. This is because they are the focal point for the artificial intelligence/digital/internet of things disruption. They contain and control the main activities that add value for the customers, and account for about 80% of the business costs and associated risks.

The challenge is often that Supply Chains are marginalized, lacking representation in the c-suite. Supply Chain leaders must become active change agents for both their end-to-end value streams and the overall business that supports them. Supply Chain leaders will need to develop new decision-making capabilities, transform their Supply Chain organizational structures, and get rid of old, non-productive mindsets. Most importantly though they must make sure to understand what they are disrupting for.

With inspiration from: Supply Chains, the platform for driving true business transformation brought in Supply Chain Quarterly Q4, 2021. Page 44 and onwards.

Welcome to a Recap of The Optilon Supply Chain conference 2021, that was held on September 15th 2021.

The overall theme for this conference was: Thriving in uncertainty. Preparing for the future. 

Why is this topic interesting?
Supply Chains are typically designed for efficiency, cost, and proximity to markets, but not necessarily for transparency and resilience. Now they are operating in a world where disruptions are regular occurrences. Both business-to-consumer (B2C) and business-to-business (B2B) companies expect to see meaningful shifts in future demand. This will affect commercial models. Thriving in uncertainty and preparing for the future means building resiliency by improving the Supply Chain and transparency, minimizing exposure to shocks, and building the capacity to respond.

Below you will find an outline of the speakers of this conference:

Block 1:

Speaker Matt Britton on the topic of: Understanding the conscious consumers of tomorrow.
Matt is a true leader when it comes to connecting the dots between the brands of today and the consumers of tomorrow. Matt has inspired and educated the world’s leading brands, on the state of the new consumer and its effect on business models and consumer trends. Listen to this energetic talk and learn how your brand and business will be affected by the conscious consumer.

Speaker Thomas Bjørnsten on Improving business intelligence with human data.
One way of working with resiliency is to work with end-to-end transparency and demand shifts. Thomas Bjørnsten, Phd. works with human data at Innovation Lab. In his speeech he provides insights into facts and fantasies when it comes to the human factor in a data-driven business. He teaches about emotion computing and how feelings can become big (data) business. He also shares how the interactions will be between humans and machines and discuss the role of trust in adoption.

Speaker: Marketing Associate at Optilon John Wikström on the topic: The unredeemed Supply Chain potential in the Nordics. 
Nordic companies have a potential to redeem a significant potential when it comes to unnecessary inventory, tied up working capital and unnecessary square meters used for storage and distribution. Speaker: John introduces us to the report, which this year covered all the Nordic countries, named The unnecessary report 2021. John shares the possible actions that can be taken to redeem the potential.

Block 2:

Panel discussion on how you can realize the full potential of Supply Chain sustainability
In the panel we had Thought Leader and strategic advisor Alis Sindbjerg Hinrichsen from Optilon, Karl Orrling from Alfa Laval and Eva Grønbjerg Christensen from Sustainify.

Speaker: Manuel Maihofer on the topic of: Improve your end-to-end planning with a digital twin
Companies that utilize the digital capabilities of Supply Chain planning will be much more resilient and better equipped to handle challenges, as well as competing more effectively. What does that mean in practical terms? Manuel Maihofer, Business Analyst and Project Manager from SKF focuses on how a digital twin could be an enabler. Manuel Maihofer is convinced, that transparency, business intelligence and digitalization of processes are key facilitators to improve supply chains. Manuel manages agile IT development projects, establishes workflows and turns data into insights, from purchasing to customer service and from production planner to top management. He plays an important role in creating SKF’s digital twin, which fuels initiatives like Integrated Planning, Demand Management and S&OP.

Block 3:

Speaker: Andreas Wieland on the topic of: Transformative Supply Chain Management
Andreas Wieland is an Associate Professor of Supply Chain Management at Copenhagen Business School. He is the Program Director of CBS’s Graduate Diploma (HD) in Supply Chain Management. His current research reinterprets global Supply Chains as social–ecological systems. Global supply chains can be quite complex. Many managers have understood this. But maybe supply chains are even more fundamentally different from what we often imagine? In his talk, Andreas Wieland challenges the conventional assumptions we have about supply chains and supply chain management. He provides transformative solutions to futureproof supply chains in an era of crises.

Speaker: Andrew Spence on the topic of: Transforming the world of work with technology
Andrew talked about how organizations will be able to find the talent they need – when they need it – from a liquid workforce. Hence, they will require fewer full-time employees, and we will see the demise of the traditional job. The focus will be leading work, not employees. What does this mean in the short and long term?

Network design and planning teams are often faced with the difficult task of meeting strategic cost reduction, sustainability and customer satisfaction targets whilst at the same time maintaining an operationally sound and effective product distribution network.

In this session we will look in to how statistical and Machine Learning-based solutions can strike the balance between cost, CO2 emissions and distance reduction. This to continuously produce optimum routing alternatives.   

At Optilon we believe, that the next generation of Supply Chain competitive advantages will come from Artificial Intelligence (AI). We believe that it is necessary to have a platform that automates and empowers organizations to embrace the AI journey on their own. Optilons AI solutions are designed to solve existing Supply Chain challenges without the need for deep experience or knowledge of AI. Therefore, we have collated this Supply Chain AI Kit to get companies started on their AI journey.

Getting started with the journey
Many companies are still struggling to get the journey started. To get you off for a good start you might want to have a look at the whitepaper we created on AI. You should read the whitepaper if you would like a general understanding of what AI is, how it could benefit your business and how it can be implemented in your Supply Chain.
You can download the whitepaper right here: Turn your Supply Chain into a competitive advantage with AI (whitepaper).

From a business perspective we have in this blogpost highlighted why Supply Chain AI improves competitiveness (blog)

If you want a quick introduction to how to get the journey started then read this blogpost – 5 steps to get started with AI in the Supply Chain (blog)

We have also recorded a session – how to get started with AI in the Supply Chain (webinar) 

Explore your Supply Chain data with AI
If you want to explore more of your data, and you are still uncertain about what kind of business value you would be able to create, then you can find some inspiration in the following webinars.

How can you drive out more value of your data with AI

Utilizing AI to drive insights

Deep Dives – more detailed cases
We have a group of Nordic companies who have already embarked on the journey and with whom we have gathered some experience. We have started sharing that experience and cases in a series of “Deep Dive” webinars.

In this session we explored how companies can get a higher customer satisfaction by using AI to monitor supplier reliability:

Business leaders often have to balance strategic goals for cost reduction, sustainability and customer satisfaction, while at the same time maintain an operationally healthy and efficient product distribution network. The lack of tools that help create a digital overview of the operational network further complicates this. It often results in silo thinking as well as static and ad-hoc route configurations and decisions.

Imagine that you are a manufacturing company that needs to distribute your products to end users or distributors, both nationally and internationally. So how should the item travel through the network? How should the policies regarding the use of 3rd party transport suppliers be put together? How is this carried out in the most sustainable and cost effective way?

Why is Artificial Intelligence / Statistical Modelling a Good Idea?
We see that using Artificial Intelligence/statistical modelling can create real business results. In the cases we have worked with so far, we have shown a potential of 4% in annual transport costs by using a share of the recommended routes. 23% of the new routes simulated had not historically been used before. This resulted in an estimated 1% reduction in annual CO2 emissions (when CO2 was weighted by 30% of the score). At the same time, this enabled a 360-degree full visualization of the entire network, which was the data basis for the analysis and the models.

The journey
Our network & route evaluation solution (or NRE for short) uses a combination of statistical and machine learning models to identify and recommend routes. They are maximized up against weighted costs, CO2 emissions and different types of customer scores. Via Optilon´’s web interface, the company’s employees who work with the optimization, are given several route alternatives to choose from and recommended actions.

A combination of statistical modeling and machine learning algorithms analyzes large combinations of existing “legs” in either existing or new routes and compares with historically used routes.

NRE not only helps with recommendations for routes, but it also provides a digital overview of the overall network. It can be at customer or order data level, transport waybill, data about the “leg” and last-mile data image.

The solution
Access to NRE can be provided via the Optilon web interface. Here, the company employees can easily evaluate, benchmark and approve/reject route recommendations.

Approved routes can be used via data integration in operational routing applications, while at the same time being monitored at a tactical and strategic level via interactive aggregated reports, either via the Internet or third-party BI apps.


Purchasing teams handling inbound products and materials from suppliers often find themselves in a continuos loop of reactive, time-consuming and often manual tracking exercises when suppliers deliver late. Similarly, sales and customer service teams face the reality of handling dissatisfied customers when outbound orders, in turn, are delivered late to the final customer. Regardless if delay is in- or outbound, it is often a contributing factor in overall Supply Chain interruptions, increasing business risk, generating resource cost and increasing customer dissatisfaction.

Our Predictive Order Monitoring solution (or POM in short) uses AI models that can accurately predict which purchase or customer order will arrive late, this long before the order is dispatched. Through the Optilon Web Interface, team members can then view predictive statuses and taken proactive actions to course-correct delay.

90% precision in predicting order delay
We delivered a soluton for a global manufacturer who historically had experienced order delays from the suppliers. The AI model delivered a results close to 90% precision compared to the reality.

Summing up we could say that the solution:

  • Predicts supplier reliability and customer deliverability, order by order, day by day
  • The AI model continuously learns and predicts order delay and recommends proactive actions
  • The solution decreases order and production issues and creates a better customer satisfaction

It is time to say goodbye to manually corrected Supply Chain data
A vast majority of Supply Chain data correction is still today handled either manually in Excel or via static and manually-defined rule-based processes in SQL or other types of data applications. These ways of correcting data is typically very time-consuming, error-prone and do not handle changes in the data very well since it is by default static. Since clean and accurate data is the cornerstone of effective Supply Chain planning and optimization, we want to empower our clients with a data correction solution that quickly and automatically handles this process, reducing overall risk and cost in the process.

Robotic Data Correction utilizes Machine Learning
Robotic Data Correction (or RDC in short) uses Machine Learning models that automatically detect data inconsistencies without any human-defined rules and learns over time from user acceptance of corrections recommendations and new data values. it has not seen before, ultimately being able to quickly to correct any relational data values.

One of our valued customers utilized RDC to improve the quality of their addresses and achieved a 3% annual domestic cost improvement on their transportation costs, as they were able to consolidate their shipments. At the same time it reduced the data correction project time-to-completion with 95%. The accuracy of the model showed up to 90% data correction accuracy.

What data can RDC correct?
RDC can correct any type of Supply Chain data that has relations to other held data, including but not limited to:

  • Customer and supplier addresses
  • Item data such as lead time and cost
  • Network data such as routing codes
  • Material data such as status codes
  • Service data such as service class
  • Demand data such as incorrect peaks
  • Transport data such as delivery mode
Where is RDC data used?
RDC corrected data can be directly integrated with several line-of-business systems and business processes including:
  • ERP source systems such as SAP, Infor M3, Microsoft Dynamics and IFS
  • Planning processes such as demand forecasting and production planning
  • Operational TMS and MES systems

“We believe good AI solutions solve real business challenges, quickly and accurately.”

Learn how AI can drive transparent and undetected Supply Chain insights. Different use cases will be presented.

Watch the Tuesday Guest session for Nov 3rd 10-10:30 CET

Many Supply Chain Executives are nowadays questioning what AI (Artificial Intelligence) in Supply Chain is. It is still the “stuff of the future” for some organizations and to some history’s biggest paradigm shift. Yet, we are still only at the beginning of the AI (r)evolution. In this blog post, we will look into what is AI in Supply Chain.

What is AI (Artificial Intelligence) in the Supply Chain? To some Supply Chain Executives it is still a mystery. How can this technology provide a competitive advantage to the Supply Chain? To learn more about how you can gain a competitive advantage by using AI in the Supply Chain you can read this blog post. In the following, you can learn more about the basics about AI and through an example learn about the logic behind.

The basics of AI in the Supply Chain

In its simplest form, artificial intelligence is when a computer or computer-controlled device runs functions and performs tasks that would normally need human assistance. Depending on its use, this may require a computer to understand speech, have a visual perception, or be able to make decisions that are independent of human intelligence (aka manual intervention).

Many AI machines can also learn on their own and make decisions based on past events. Or, they can interpret massive amounts of data from a company’s supply chain history to predict future outcomes with incredible accuracy.

Terminologies used within AI:

  • Artificial Intelligence: A program that can sense, reason, act and adapt
  • Machine learning: Algorithms whose performance improve as they are exposed to more data over time
  • Deep learning: Subset of machine learning in which multilayered neural networks learn from vast amounts of data

The following three trends have put AI within everyone’s reach.

  • Costs for data storage and processing has gone down
  • Data availability has increased, not only inside the company but also outside
  • Advanced mathematics has enabled complex data modeling

Though the concept of AI is half a century old, interest in the technology has snowballed over the past decade. This has been driven particularly by increased data availability, a lower cost of data processing, and new advanced mathematics, allowing for a significantly lower cost of predictions and analytical capacity.

In Optilon we are seeing particularly fast growth in three areas of AI:

  • Text and numbers: Today, a wealth of information can be found in text and number formats.
  • Speech: AI can also be used for speech recognition, which translates spoken words into text.
  • Images: One of the most common uses of AI is image recognition. AI can be used to categorize, edit, and parse this image data.

Explaining AI using examples

In order to take out some of the mystery which is surrounding AI let us take an example from outside the Supply Chain.

The Real Estate Agent

Imagine a real estate agent who sells apartments. Every time a new seller signs up, the first task is to suggest how much their apartment can be sold for. The real estate agent is busy, and it is time-consuming to inspect all those apartments, so he decides to automate the task using artificial intelligence. The formula behind the intelligence sounds simple. Price is equal to the number of square meters times 20,000. Therefore, an apartment of 100 square meters is always priced at two million. After a short while, the real estate agent discovers the challenges of using this method.

Obviously, there are other things than just land, which affects the price. As an example, ground floor apartments are typically not nearly as expensive as apartments with views. Therefore, the next generation of intelligence puts a sum for each floor apartment located above ground level.

After looking at data for actual sales prices, the formula is refined a bit, because it is mainly on the lower floors that the distance from street level makes a difference. In other words, there is a greater difference in price between the living room and the first floor than between the sixth and seventh floors. In the real estate agent’s formula, it is fixed by taking the square root of the floor number and multiplying by DKK 100,000. Then an apartment on the first floor will be priced DKK 100,000 higher than the ground floor, while one on the ninth floor will be DKK 300,000 higher.

The following generations of formula incorporate further conditions: Whether there is a lift in the property, how attractive the area the apartment is in, whether the apartment is in good condition, how busy the road is, and whether one can see the sea.

Along the way, the formula has become somewhat longer and somewhat more complicated. It has been necessary to use both square roots and logarithms, but it can still be shown on an A4 sheet of paper if you use small print. One can question whether it is reasonable to call the formulas in the real estate example and software in self-propelled cars “intelligent”. As we have seen, in both cases, these are just formulas. Very long formulas, but still only formulas.

They are not, in principle, different from the functional machine whose intakes consisted of the formula y = 2x + 1.  Artificial intelligence is, therefore, in no way intelligent. Absolutely not. The name probably describes researchers’ wet dreams about what technique one day they will be able to develop themselves. Not what it actually is.

So now, we hope we have taken out all the mystery, and you should be ready to look at how you can incorporate it into your own Supply Chain.

Are you wondering why you should implement AI to your Supply Chain? Supply Chain is more and more recognized as a key source of competitive advantage and differentiation. Companies strive to build Supply Chains that support company growth, increase transparency, streamline operations, and increases customer satisfaction. Implementing AI to your Supply Chain could become a source for creating competitive advantages. In this blog post, we will look into why Supply Chain AI creates competitive advantages.

The Supply Chain has a great influence on all cost drivers in a company. Optimizing how Supply Chains are operated opens for possibilities to win new market shares, boost sales and establish new business models. Focusing on the Supply Chain can improve the bottom line with 5-20% within 24 months (1). Supply chain AI, is still the “stuff of the future” for some organizations. Some of the world’s most significant leaders and thinkers are even suggesting that artificial intelligence is history’s biggest paradigm shift. Yet, we are still only at the beginning of the AI (r)evolution. Next we will look into why Supply Chain AI supports the company in gaining competitive advantages.

Why Supply Chain AI can support differentiation and lower cost
The Supply Chain is a network of suppliers, manufacturers, distributors, and retailers that act together to control, manage and improve the overall Supply Chain performance. Evaluating the performance of the entire Supply Chain can be a complex task, due to the complexity inherent in the structure and the operations.

No matter how the performance evaluation is done, the company must be able to differentiate against its competitors. Hence, the company must be able to offer value propositions that create an advantage over its competitors. Secondly, the company must operate at a lower cost than its competitors. This is where AI could become a competitive advantage in the Supply Chain, simply because the use of AI in the Supply Chain can both support differentiation but also lower cost.

Why Supply Chain AI can support the new Supply Chain model
The traditional model of the Supply Chain is fundamentally changing. We are shifting towards consumer-led, data driven, highly complex supply networks. The consumer increasingly drives innovation from the heart of the supply network, rather than being on the receiving end of the supply chain. Companies have to act in a more globalized world and balance the global with the local.

Companies have to be agile and willing to change by involving the whole organization. Companies have to put up a sustainable agenda internally as well as externally. They have to build a flexible and resilient culture that makes the organization robust toward big changes and chocs around the world. They have to provide visibility and transparency. Surrounded by an explosion of data, many Supply Chains are also struggling to leverage or take advantage of their data.

Why Supply Chain AI can solve real Supply Chain problems
These shifts in the Supply Chain demand more accurate supply chain planning and synchronization, and faster multichannel responsiveness that go far beyond the abilities of the typical workforce and infrastructure. It requires instant visibility, quick decision making and increased flexibility across the whole network. Also here Supply Chain AI can become a competitive advantage as it is possible, with the use of the right kind of supply Chain AI technologies to solve some of these real Supply Chain challenges.

Would you like to learn more about how you can create competitive advantages with AI then we encourage you to explore some of our content from Optilon Academy right here. 

Alis Sindbjerg Hemmingsen is a Thought Leader at Optilon. She has more than 25 years of experience within the Supply Chain field. Optilon has a long track record helping companies achieve competitive advantages by improving Supply Chain performance. Learn more about Optilon here. 

(1) Arlbjørn et al;  “Supply Chain Management, sources for competitive advantages, 2018 edition.