A process manufacturer with a network of assets spread across Europe needed to respond more flexibly to changes in customer demand while maintaining high asset utilisation, low working capital and low transport costs.
The situation was complex. The assets were different and had their own characteristics. The outflow from the installations could not simply be stopped between production runs, and a change of material resulted in a massive production loss – although a product type change without a change of material was doable.
The producer had 25 production lines and served 1,000 customers with a total of 2,500 products. In short, the perfect complex planning issue for which our More Optimal platform was designed.


The planners had been working with a combination of SAP and Excel spreadsheets. They were handling a huge number of variables and attempting to incorporate increasingly shorter delivery times. The planners understood their trade, but the complexity of the puzzle was too great for the resources available. There was much to gain.

Our generic More Optimal platform makes it possible to create a customer-specific application in a short time, with all relevant planning rules built in. The platform is set up in close consultation with the user. First, the relevant Key Performance Indicators (KPIs) were defined. These included (1) demand fulfilment, (2) asset pull / productivity, (3) inventory, (4) transport costs and (5) planning effort.

In a number of joint work sessions, we established the planning process and drew up the rules for allocating products to the various production lines. In addition, the transport options relating to production locations and the rules for product changes were built in. By working closely with the planners at every step, we gradually developed the More Optimal platform, and this now shows in real-time the consequences of the decisions made by the planners and gives advice on how to improve the planning process.

The application is also used to evaluate what-if scenarios and their impact on the KPIs. The manufacturer uses this functionality as part of the annual planning and budgeting process and relies on it for concrete operational issues on a more regular basis.



This article covers the introduction to machine learning and the directly related concepts.

Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. It is a subset of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate the way humans learn, and in doing so it gradually improving its accuracy. By using statistical learning (link resides outside Axisto) and optimisation methods, computers can analyse datasets and identify patterns in the data. Machine learning techniques leverage data mining to identify historic trends to inform future models.

According to the University of California, Berkeley, the typical supervised machine learning algorithm consists of three main components:

  • A decision process: A recipe of calculations or other steps that takes in the data and returns a guess at the kind of pattern in the data that the algorithm is looking to find.
  • An error function: A method of measuring how good the guess was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify how bad the miss was?
  • An updating or optimisation process: The algorithm looks at the miss and then updates how the decision process comes to the final decision so that the miss will not be as great the next time.

Machine learning is a key component in the growing field of data science. Using statistical methods, algorithms are trained to make classifications or predictions and uncover key insights from data.


The technology company Nvidia (link resides outside Axisto) distinguishes four learning models that are defined by the level of human intervention:

  • Supervised learning: If you are learning a task under supervision, someone is with you, prompting you and judging whether you’re getting the right answer. Supervised learning is similar in that it uses a full set of labelled* data to train an algorithm.
  • Unsupervised learning: In unsupervised learning, a deep learning model is handed a dataset without explicit instructions on what to do with it. The training dataset is a collection of examples without a specific desired outcome or correct answer. The neural network then attempts to automatically find structure in the data by extracting useful features and analysing its structure. It learns by looking for patterns.
  • Semi-supervised learning: Semi-supervised learning is, for the most part, just what it sounds like: a training dataset with both labelled and unlabelled data. This method is particularly useful in situations where extracting relevant features from the data is difficult or where labelling examples is a time-intensive task for experts.
  • Reinforcement learning: In this kind of machine learning, AI agents are trying to find the optimal way to accomplish a particular goal or improve the performance of a specific task. If the agent takes action that moves the outcome towards the goal, it receives a reward. To make its choices, the agent relies both on learnings from past feedback and on exploration of new tactics that may present a larger payoff. The overall aim is to predict the best next step that will earn the biggest final reward. Just as the best next move in a chess game may not help you eventually win the game, the best next move the agent can make may not result in the best final result. Instead, the agent considers the long-term strategy to maximise the cumulative reward. It is an iterative process: the more rounds of feedback, the better the agent’s strategy becomes. This technique is especially useful for training robots to make a series of decisions for tasks such as steering an autonomous vehicle or managing inventory in a warehouse.

* Fully labelled means that each example in the training dataset is tagged with the answer the algorithm should produce on its own. So a labelled dataset of flower images would tell the model which photos were of roses, daisies and daffodils. When shown a new image, the model compares it to the training examples to predict the correct label.

In all four learning models, the algorithm learns from datasets based on human rules or knowledge.

In the domain of artificial intelligence, you will come across the terms machine learning (ML), deep learning (DL) and neural networks (artificial neural networks – ANN). Artificial intelligence and machine learning are often used interchangeably, as are machine learning and deep learning. But, in fact, these terms are progressive subsets within the larger AI domain, as illustrated in Figure 1.

Axisto - Introduction to Machine Learning
Figure 1. Artificial neural networks are a subset of deep learning, which is a subset of machine learning, which in turn is a subset of artificial intelligence.

Therefore, when discussing machine learning, we must also consider deep learning and artificial neural networks.


Unlike machine learning, deep learning does not require human intervention to process data. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required, which means it can be used for larger data sets.

“Non-deep” machine learning is more dependent on human intervention for the learning process to happen because human experts must first determine the set of features so that the algorithm can understand the differences between data inputs, and this usually requires more structured data for the learning process.

“Deep” machine learning can leverage labelled datasets, also known as supervised learning, to inform its algorithm. However, it does not necessarily require a labelled dataset. It can ingest unstructured data in its raw form (e.g., text and images), and it can automatically determine the set of features that distinguishes between different categories of data. Figure 2 illustrates the difference between machine learning and deep learning.

Axisto - Machine Learning and Deep Learning
Figure 2. The difference between machine learning and deep learning.

Deep learning uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human, such as digits or letters or faces.

In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. In an image-recognition application, the raw input may be a matrix of pixels. The first representational layer may abstract the pixels and encode edges; the second layer may compose and encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may recognise that the image contains a face. Importantly, a deep learning process can learn which features to optimally place in which level on its own. This does not fully eliminate the need for manual-tuning – for example, varying numbers of layers and layer sizes can provide different degrees of abstraction. The word “deep” in “deep learning” refers to the number of layers through which the data is transformed.


An artificial neural network (ANN) is a computer system designed to work by classifying information in the same way a human brain does, while still retaining the innate advantages they hold over us, such as speed, accuracy and lack of bias. For example, it can be taught to recognise images and classify these according to elements they contain. Essentially, it works on a system of probability – based on data fed to it, it can make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” – by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.

Artificial neural networks consist of a multilevel learning of detail or representations of data. Through these different layers, information passes from low-level parameters to higher-level parameters. These different levels correspond to various levels of data abstraction, leading to learning and recognition. An ANN is based on a collection of connected units called artificial neurons (analogous to biological neurons in a biological brain). Each connection (synapse) between neurons can transmit a signal from one neuron to another neuron. The receiving (postsynaptic) neuron can process the signal(s) and then signal to neurons connected to it downstream. Neurons may have state, generally represented by real numbers, typically between 0 and 1. Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream. Typically, neurons are organised in layers, as illustrated in Figure 3. Different layers can perform various kinds of transformations on their inputs. Signals travel from the first (input), to the last (output) layer, possibly after traversing the layers multiple times.

Axisto - Artificial Neural Network
Figure 3. Layers in an artificial neural network.


There are many applications for machine learning; it is one of the three key elements of Intelligent Automation and a autonomous operating model within Industry 4.0. The computer programs can read text and work out whether the writer was making a complaint or offering congratulations. They can listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood. In some cases, they can even compose their own music that either expresses the same themes or is likely to be appreciated by the admirers of the original piece.

Neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games, and medical diagnosis. As of 2017, neural networks typically have a few thousand to a few million units and millions of connections. Although this number is several orders of magnitude less than the number of neurons in a human brain, these networks can perform many tasks at a level beyond that of humans (e.g., recognising faces, playing “Go”).



Companies that pack fresh products face massive complexity and unpredictability. They process many different products, all of which have specific requirements in terms of quality, class and size. They deal with a multitude of packaging requirements and variability in price agreements for each customer. And they handle huge swings in supply and demand. But the time frame in which packers must match supply and demand is short.
How do you balance customer requirements with product and process complexity to achieve high customer satisfaction and high ‘valorisation’? And how do you deal with last minute changes in supply and demand – for example, if a batch is rejected because it does not meet the quality requirements?


The packer had been using Excel spreadsheets to allocate products on packaging lines and carry out detailed line planning. This had caused misunderstandings and mistakes – and a higher workload than necessary for the planners. They were losing time creating iterative plans, and there was uncertainty about which version of the plan was most up-to-date and about which numbers were correct.

We knew that the More Optimal platform would resolve these problems and explained the benefits to our client. The need was so great and the benefits so obvious that the packer did not even want a ‘proof of value’, but immediately decided to develop and implement a dedicated application based on the More Optimal platform.

The goals were (1) to arrive at a workable schedule faster, (2) more efficiency in the operation, (3) shorter lead times relating to product freshness, (4) better demand fulfilment and (5) increased flexibility.

The More Optimal platform makes it possible to build a customer-specific application in a short time with all relevant planning rules built in. The application is set up in close consultation with the user. First, the relevant Key Performance Indicators (KPIs) are defined to quantitatively determine the quality of the allocation plan. Two of these KPIs were demand fulfilment and lead time (related to product freshness).

In a number of joint work sessions, we drew up the allocation rules for products and determined how products from suppliers should be allocated to customers. By working intensively with the packer, we developed a dedicated application that shows the consequences of the decisions made by the planners and gives advice for better planning. This application was further expanded with support from the planners in order to optimise the detailed planning per packaging line to minimise changeover times on the lines and to increase the throughput capacity (OEE) of the lines. The application measures the operational performance based on the agreed KPIs.



In 2018 the World Economic Forum (WEF) launched an initiative, Shaping the Future of Advanced Manufacturing and Production, to demonstrate the true potential of Industry 4.0 technologies to transform the very nature of manufacturing. Learnings from 69 frontrunner companies boosting 450 use cases in action reveal that organisations investing in Industry 4.0 technology are realising significant improvements in productivity, sustainability, operating cost, customisation and speed to market.

Here are just a few numbers from the 450 use cases: labour productivity up by 32% to 86%, order lead times down by 29% to 82%, field quality up 32%, manufacturing costs down 33%, OEE up 27%, new product design lead time down 50%.

Additionally, frontrunner companies showed that by investing in Industry 4.0 technologies they can solve business problems while simultaneously reducing environmental detractors such as waste, consumption and emissions. While the greatest environmental benefits come from core green sustainability initiatives (such as commitments to renewable energy), Industry 4.0 use cases have shown significant environmental impact as well, reducing energy consumption by more than one-third and water use by more than one-quarter.

Out of the 69 frontrunners within the WEF initiative that exist to date across the globe, 64% have been able to drive growth by adopting Industry 4.0 solutions. In all those cases, with little to no capital expenditure, they were able to unlock capacity and grow by coupling some of the technology solutions together with a much more flexible production system. The business case is big and the pay back is short, both for large companies and for SMEs.


Most companies struggle to start and scale an Industry 4.0 transformation because they lack people with the right skills and knowledge and because of a limited understanding of technology and vendor landscape. On average, 72% of companies don’t get beyond the pilot phase.

AIMA enables manufacturing companies to understand where they stand and to design an implementation roadmap that helps them start their Industry 4.0 implementation journey or progress to the next level. AIMA assesses your operations along eight elements, as shown in Figure 1.

Axisto - The AIMA comprises 8 elements with in total 33 categories
Figure 1. The AIMA comprises 8 elements with in total 33 categories

In total, the eight elements are made up of 33 categories (see Figure 2), and each category spans the four fundamental building blocks of Industry 4.0: processes, technology, people and competencies, and organisation.

Figure 2. The eight elements of the AIMA with the 33 categories, each spanning processes, technology, people and competencies, and organisation.
Figure 2. The eight elements of the AIMA with the 33 categories, each spanning processes, technology, people and competencies, and organisation.


AIMA helps you:

  • build knowledge
  • tear down interdepartmental walls and create strategic alignment
  • understand where your operations stand – what is strong and must be maintained
    and what needs to be improved
  • understand what your key areas are and what you need to focus on.

AIMA helps you establish a company-specific interpretation of key principles and concepts. It creates an improved case for change and provides more momentum to implement the change.


AIMA consists of four steps:

  • Preparation – get to know the members of the leadership team and understand: the vision and strategy, how the team views market developments, challenges, opportunities and how the company develops within this context and inventory of expectations for the next days.
  • The first workshop day – identification of and alignment on the case for change: introduction to Industry 4.0 and explore how it affects the strategy (execution), test the extent of alignment within the leadership team and identify the (/ check if there is a) case for change.
  • The second workshop day – the Industry 4.0 Maturity Assessment: assessing operations using a selection from the AIMA categories, prioritising the KPIs and identifying the focus areas.
  • The third workshop day – design of the implementation roadmap: sequence of steps that address processes, technology, people & capabilities and organisation, identification of risks and design of a risk mitigation plan.

Focusing on these areas will accelerate performance improvements in operations. AIMA provides the insights, designs an implementation roadmap and is a strategic tool to regularly assess progress and refine your roadmap based on new insights. Starting at the operations leadership level allows us to create an overall framework. AIMA is then deployed at the next level down into respective factories. Again, we begin with a preparation; followed by three workshop days, now with the factory leadership team:

  • Preparation – get to know the members of the factory leadership team and understand: the factory vision and strategy, how the team views market developments, challenges, opportunities and how the factory develops within this context and inventory of expectations for the next days.
  • The first workshop day – identification of and alignment on the case for change: introduction to Industry 4.0 and explore how it affects the strategy (execution), test the extent of alignment within the factory leadership team and identify the (/ check if there is a) case for change.
  • The second workshop day – the Industry 4.0 Maturity Assessment: assessing operations using a selection from the AIMA categories, prioritising the KPIs and identifying the focus areas.
  • The third workshop day – design of the implementation roadmap: prioritisation of factory KPIs and the identification of focus areas, sequence of steps that address processes, technology, people & capabilities and organisation, identification of risks and design of a risk mitigation plan.

Making improvements in these focus areas will make the biggest impact on the factory’s performance within the overall framework. Leveraging this cascaded approach creates the biggest wins for the whole business rather than just a sub-optimisation of an individual factory.


AIMA provides four key outcomes:

  • Understanding of Industry 4.0, its key principles and concepts, and how they affect strategy (execution)
  • Alignment within the operations leadership team and factory leadership teams
  • Understanding of your Industry 4.0 maturity level / readiness
  • Priority of focus areas to create short-term business value within a long-term context


AIMA will generate initial momentum. However, it is worth noting that any Industry 4.0 implementation will only be successful if you put your people at the centre of it.

The biggest challenge for a company is not in choosing the right technology, but in having a lack of digital culture and skills in the organisation. Investing in the right technologies is important – but the success or failure does not ultimately depend on specific sensors, algorithms or analysis programs.

The crux lies in a wide range of people-oriented factors. Axisto supports you in the development of a robust digital culture and ensures change is developed from within and is driven by clear leadership from the top.


Axisto was founded in 2006 to help companies accelerate their operational performance – fast, measurable and lasting. We have executed more than 150 projects across Europe.

We have concrete on-the-ground experience, which is why our approach is practical and pragmatic. We combine subject-matter expertise with excellent change management skills.

We see change through and do whatever it takes to make our clients successful.



The goal of using Intelligent Automation (IA) is to achieve better business outcomes through streamlining and scaling decision making across businesses. IA adds value to business by increasing process speed, reducing costs, improving compliance and quality, increasing process resilience and optimising decision results. Ultimately, it improves customer and employee satisfaction and improves cash flow and EBITDA and decreases working capital.


IA is a concept leveraging a new generation of software based automation. It combines methods and technologies to execute business processes automatically on behalf of knowledge workers. This automation is achieved by mimicking the capabilities of knowledge that workers use in performing their work activities (e.g., language, vision, execution and thinking & learning).IA effectively creates a software-based digital workforce that enables synergies by working hand-in-hand with the human workforce.

On the simpler end of the spectrum, IA helps perform the repetitive, low-value add and tedious work activities such as reconciling data or digitising and processing paper invoices. On the other end, IA augments workers by providing them with superhuman capabilities. For example, it provides the ability to analyse millions of data points from various sources in a few minutes and generate insights from.


IA consists of three key components:

Axisto - Process MiningBusiness Process Management with Process Mining to provide greater agility and consistency to business processes.


Axisto - Robotic Process AutomationRobotic Process Automation (RPA). Robotic process automation uses software robots, or bots, to complete repetitive manual tasks. RPA is both the gateway to artificial intelligence and can leverage insights from Artificial Intelligence to handle more complex tasks and use cases.

Axisto - Artificial IntelligenceArtificial Intelligence. By using machine learning and complex algorithms to analyse structured and unstructured data, businesses can develop a knowledge base and formulate predictions based on that data. This is the decision engine of IA.


Implementing Intelligent Automation might come across as a daunting endeavour, but it doesn’t need be. Like any business leader, you will have a keen eye on accelerating operations performance, which in essence is improving the behaviour and outcomes of your business processes. Process Mining is a perfect tool to help you with that.

Process Mining is a data-driven analysis technique, i.e., analysis software, to objectively analyse and monitor business processes. It does this based on transactional data that is recorded in a company’s business information systems. The analysis software is system agnostic and doesn’t need any adaptation of your systems. Process Mining provides fact-based insight into how processes run in daily reality: all process variants (you will be surprised how many variations of one process there actually are in your business) and where the key problems and opportunities lie to improve process efficiency and effectiveness.

Process Mining is also an excellent way to prepare the introduction of Robotic Process Automation, which could be the most relevant next step on your IA journey. Process Mining can be purely used as an analysis tool, but it can also be installed permanently to constantly monitor the performance of and the issues in the processes. It is a non-intimidating approach and a gradual implementation of Intelligent Automation.


However, at some point, rather sooner than later, it is important to establish and communicate a comprehensive, company-wide vision for what you want Intelligent Automation to achieve: how will automation deliver value and boost competitive advantage. You need a shared roadmap for a successful implementation that covers processes, technology (including legacy systems), people & competencies and organisation.

Such a shared Intelligent Automation/Industry 4.0 Roadmap ensures a consistent, thoughtful approach to selecting, developing, applying, and evolving the IA/I4.0 structure to achieve the intended impact. The Axisto Industry 4.0 Maturity Assessment (AIMA) is an effective way to create such a shared implementation roadmap.


Axisto - Change InsiderImportantly, the biggest challenge for a company is not in choosing the right technology, but in having a lack of digital culture and skills in the organisation. Investing in the right technologies is important – but the success or failure does not ultimately depend on specific sensors, algorithms or analysis programs. The implementation and scaling of Intelligent Automation/Industry 4.0 requires a fundamental shift in mindset and behaviours at all levels in the organisation. The crux to success lies in a wide range of people-oriented factors.


A container terminal reached the limits of its capacity due to a further increase in the number of units to be processed. The terminal also had to become more attractive for ships to dock by faster loading and unloading for shorter waiting times. Furthermore, container ships are getting bigger, increasing complexity and time pressure at the terminal.

The assignment was to increase efficiency to make more inbound and outbound truck movements possible and to shorten ship waiting times.


Based on data from the ERP system regarding plan and actual over a representative period, the current working method of the terminal was reconstructed in our Planning Platform. The actual operation was visualised and animated, allowing the movements of each individual container to be tracked from position to position. The reconstruction was validated and further fine-tuned in a highly interactive process with the client.

Subsequently, with our Planning Platform, the current operational performance of the terminal was determined based on jointly identified Key Performance Indicators (KPIs), such as the mooring time per barge, the number of crane movements in/out and the number of truck movements in/out. Subsequently, a simulation of an optimised operation was performed using the exact same dataset and boundary conditions. The comparison of the KPIs of the current and optimised operations immediately gave a clear picture of the improvement potential.

In close collaboration with the client, the plan for a number of containers was then optimised step-by-step until the total was finally optimised. After each step, the improvement was measured against the identified KPIs.


A global insurance company was receiving 40-50 claims a day which needed to be evaluated and verified according to several factors before being approved for payment. Most of the claims were arriving as unstructured data, either as PDFs or scanned documents, making it difficult to pull information from them to be entered into various systems – in a timely manner. As a result, claims weren’t being processed fast enough. The company was concluding each year with millions of dollars in claims left open, impacting customer service.


The company implemented RPA with ABBYY Flexicapture to streamline claims processing and payments. The software Robots took scanned claims sent through email and ran them through Flexicapture to turn the unstructured data into structured formats readable by robots. From there, the robots took the data, verified that all the information was correct and checked all exceptions. Claims that were accurate were approved for payment and sent back to the brokers. If any information was incorrect or there were exceptions, the claims were routed to an employee for further investigation.

“Tell me where you spend your money and I will tell you what your strategy is.” There is probably no better sentence to describe the potential difference between an intended strategy and a defacto strategy. Zero-based budgeting (ZBB) is a powerful approach to accelerate growth, create value and make your strategy happen.


ZBB starts from a blank sheet of paper, not from last year’s budget. On a very granular level, you start by determining what resources various business units require to deliver the strategic goals. You then address individual cost categories across all business units and justify all expenditure. In ZBB the base line is not last year’s budget, but “zero”.

ZBB was introduced in the 1960s and was slow in getting traction. It had a brief spell of popularity and then sank away into obscurity. Now, supported by progressed digitisation, it is on the rise again. But it’s no longer just being used in the consumer packaged goods industry, nor focused only on sales and general administrative expenditure. It has begun to spread across industries and functions. And rightfully so because ZBB is appropriate for any industry and all functions: procurement, supply chain, sales and marketing, service and support, and others.


Many companies use it as a cost-control tool. However, this is vastly underestimating its real power. When used in a strategic context, ZBB can reconfigure cost structures, free up investment funds and accelerate growth. Successful companies start with a solid “What by How” objective that gives the company direction. The related goals then lead to questions about which investments are necessary and what the total cost structure needs to be to enable these investments. This way, ZBB is tightly integrated with the company’s strategy. It addresses both the cost discipline and the investments and opportunities that drive growth. However, using ZBB as a one-time exercise won’t cut it.


ZBB is not a one-time exercise; it is a way of doing business and part of the DNA of an organisation. Its implementation not only redesigns your processes, policies and systems, but also instils new mindsets and behaviours. ZBB establishes clear cost accountability and disciplines to reduce and permanently eliminate costs that add little or no value. At the same time, it establishes a clear accountability to maximise the added value of the right expenditure. ZBB challenges companies to operate more efficiently and effectively across functions, geographies, divisions and business units to grow the top line and margin. It drives people to make conscious, strategic decisions and to get the right things done.


During a recession – and more so just afterwards – successful companies grow their EBIT whereas others stall. So why do some companies win while others lose? The common denominator with the winners is that they maintain a strict cost discipline and fund their growth levers in both the high and low phases of the economic cycle. They maintain strategic momentum regardless of market conditions.

We know that the total shareholder return a company achieves is mainly determined by its margin. The companies that generate a significantly higher long-term value grow their EBIT most and implement the required change during economic highs – i.e., pre-emptively. So the earlier a company transforms, the better its future performance.


Lean is often talked about as being an extensive toolbox. This misses the point. Lean is all about mindset and behaviours – it’s about strict cost discipline and fast cash conversion cycle. Lean originated at Toyota when it was rebuilding its business just after World War II. The company was cash strapped – as were its customers.

The whole concept of flow within Toyota’s way of working was, and still is, to ensure a fast cash conversion cycle and eliminate low value-added costs. What’s more, they approached everything from the customer’s point of view – what is the customer willing to pay for? Everything else is waste. Having a fast cash conversion cycle creates the opportunity to grow faster. And that is what they did.

Similarly, Six Sigma is often talked about as being an extensive toolbox. But Six Sigma is also all about mindset and behaviours – one of relentlessly eliminating variation. Six Sigma was developed at Motorola in the late 1980s. The company was crippled by the cost of poor quality, which drained their margins and eroded their revenue. For the company to have a viable future, it had to drive down variation.


Zero-based budgeting is the overarching approach to drive the short- and long-term success of a company. From a business strategy point of view, first the “What by How” objective is set and then the top goals and targets are set. ZBB views the company as a whole from the highest level, informed by its purpose, vision and ambition. It affects every aspect of a company: the operating model including the organisation structure and policies. ZBB thrives on the right mindset and behaviours that are incorporated in the DNA of the organisation.

The mindset and behaviours behind Lean Six Sigma (LSS) fit fully with the mindset and behaviours behind zero-based budgeting. ZBB will steer the selection of tools from the LSS toolbox that best contribute to the business needs in the company’s drive to deliver on its vision and ambition – in the same way that Toyota and Motorola developed and acquired skills and tools that were in line with their business needs and informed by their mindset.

Industry 4.0 means the growing together of the digital and manufacturing industries. All physical assets are digitised and integrated into digital ecosystems with partners in the value chain.

Industry 4.0 represents a huge step in performance. You can improve your speed, flexibility and productivity by 40%. You can develop a new business strategy and take the opportunity to innovate your products and services portfolio.

Axisto works with you to map the digital maturity of your business with our AIMA (Axisto Industry 4.0 Maturity Assessment) and choose the elements that will deliver the most value in line with your vision. Well-chosen pilots will help you get on the learning curve and achieve some initial success. You will gain insights into the skills gap, and this can direct your HR strategy. We can help you to properly organise data analytics and develop your organisation more digitally. Axisto’s experience will ensure you avoid any pitfalls on your journey to becoming a digital enterprise.

Importantly, the biggest challenge for a company is not in choosing the right technology, but in having a lack of digital culture and skills in the organisation. Investing in the right technologies is important – but the success or failure does not ultimately depend on specific sensors, algorithms or analysis programs. The crux lies in a wide range of people-oriented factors. Axisto supports you in the development of a robust digital culture and ensures change is developed from within and driven by clear leadership from the top.

An autonomous operating model is not just a digital upgrade of your current operating model. It is a radically different way of conducting your business.


Primary and support business processes are integrated. This allows the financial department to act in a much more agile manner. The cash flow is visible on an ad-hoc basis, which improves planning and analysis abilities. A forecast supported by the IT system replaces manual forecasts. Once determined, KPIs make controlling easier through automated warning messages, thus allowing immediate intervention to take place.

The budget process is changed and no longer runs along the individual business functions (such as Sales, Marketing, Production, IT), but along value drivers (sales quantities linked to market data, prices in combination with customer clusters, etc.). At any time, the balance and P&L for the company as a whole and for each of the departments can be determined. This makes it possible to sail sharply close to the wind.

The entire supply chain uses a single point of truth for real-time information The transparency makes it possible to simulate different scenarios quickly and easily, but ultimately people make the decisions. The effect of decisions is calculated and communicated in real-time throughout the end-to-end supply chain. Margin, order cycle time and cash can be predictively optimised based on a holistic view of supply chain performance, stock levels and trend analyses.


Mobile devices are an essential interaction channel for both customers and employees. As a result, the management and control of the integration of different mobile devices and of the mobile applications are strategic factors. New and existing mobile technologies are easy to integrate.


Collaboration is largely multidisciplinary and without hierarchy. Knowledge and skills are not things that sets you apart from others in the company – they are things you make available to the team.

Collaboration must be able to be set up ad hoc at any time, from anywhere – even across geographic boundaries. Active exchange of ideas, knowledge and expertise requires an appropriate incentive system. This system focuses on the group outcome and allows them to participate in the overall success.

Social media and collaboration technologies are a central element of communication, knowledge transfer and teamwork. This applies to interaction with customers, employees and business partners. The technologies are used for the interactive exchange of information and content, thus making collaboration more effective, and they are increasingly focused on establishing interaction patterns in a digital culture.

The aim of redesigning the office environment is to increase cooperation and creativity in the company. This includes, for example, creating zones of creativity in offices, building open structures where there are no fixed desks and integrating the employee’s own home office.


Digitisation requires new skills and abilities on the part of employees. The development of these competencies in the workforce requires strategic planning to address the requirements in the long term. The use of analysis methods not only enables the optimised deployment of employees, but also clarifies the question about which skills are needed now and in the future and how to get them as quickly as possible.


Knowledge and experience are becoming obsolete at an ever-increasing rate, and roles and tasks are constantly changing. The employees are constantly challenged to learn new things, to participate in training for new tasks and to adapt to role changes.