CHALLENGE
Earlier this year Axisto was invited to support a global oil & chemicals company to stop a long series of ever declining Turn Around performances. Together with the TA-team we managed to deliver a truly great TA.
Now a next Turn Around was scheduled; at another site. More work had to be done in less time in an old plant. Would the company be capable of repeating the high-performance at another site with another team?
APPROACH
The experiences and evaluation of our ‘Best Ever TA’ were used in the preparing and executing this short, high-pace TA for which the term ‘Squat’ was used.
The expectations of corporate were high and the Squat team felt all eyes focused on them. 80% of the team members were new and without the experience of the ‘Best Ever TA’. On top of that the standing organisation on site was strongly siloed and morale was low. All in all distinctly different from the other site.
The Axisto approach to create deep ownership of the scheduled activities and of the Squat as a whole, that proved itself in the ‘Best Ever TA’, was applied again. And the learnings were implemented.
The overall result was an even better performance than the ‘Best Ever TA’!
These days, customers expect shorter fulfilment timeframes and have a lower tolerance for late or incomplete deliveries. At the same time, supply chain leaders face growing costs and volatility. how process mining creates value in the supply chain is by creating transparency and visibility across the supply chain and providing proposals for decisions with their trade-offs for real-time optimisation of flows.
FULL TRANSPARENCY
Instead of working with the designed process flow or the process flow that is depicted in the ERP system, process mining monitors the actual process at whatever granularity you want: end-2-end process, procure-2-pay, manufacturing, inventory management, accounts payable, for a specific type of product, supplier, customer, individual order, individual SKU. Process mining monitors compliance, conformance, cooperation between departments or between client, own departments and suppliers, etc.
VISIBILITY ACROSS THE SUPPLY CHAIN
Dashboards are created to suit your requirements. These are flexible and can be easily altered whenever your needs change and/or bottlenecks shift. They create real-time insights into the process flow. At any time, you know, how much revenue is at stake because of inventory issues, what root-causes are and which decisions you can take and what their effects and trade-offs will be.
If supplier reliability is not at the target level at the highest reporting level, you can easily drill down in real-time to a specific supplier and a particular SKU to discover what is causing the problem in real-time. Suppliers could also be held to the best-practice service level of competitive suppliers.
MAKING INFORMED DECISIONS AND TAKING THE RIGHT ACTIONS
The interactive reports highlight gaps between actual and target values and give details of the discrepancies, figure A. By clicking on one of the highlighted issues, you can assign an appropriate action to a specific person, figure B. Or it can even be done automatically when a discrepancy is detected.And direct communication with respect to the action is facilitated in real-time, figure C.
Fig. A, details of the discrepancies. Fig. B, pop up to write a task. Fig. C, exchanging information.
HOW PROCESS MINING CREATES VALUE IN THE SUPPLY CHAIN – WRAP UP
Process mining is an effective tool to optimise the end-2-end supply chain flows in terms of margin, working capital, inventory level and profile, cash, order cycle times, supplier reliability, customer service levels, sustainability, risk, predictability, etc. Because process mining monitors the actual process flows in real-time, it creates full transparency and therefore adds significant value to the classic BI-suites. Process mining can be integrated with existing BI-applications and can enhance reporting and decision-making. We consider process mining to be a core element of Industry 4.0.
THIS INTERVIEW WAS PUBLISHED BY THE GUARDIAN
Zoë Corbyn
Sun 6 Jun 2021 09.00 BST
‘AI systems are empowering already powerful institutions – corporations, militaries and police’: Kate Crawford. Photograph: Stephen Oxenbury
The AI researcher on how natural resources and human labour drive machine learning and the regressive stereotypes that are baked into its algorithms
Kate Crawford studies the social and political implications of artificial intelligence. She is a research professor of communication and science and technology studies at the University of Southern California and a senior principal researcher at Microsoft Research. Her new book, Atlas of AI, looks at what it takes to make AI and what’s at stake as it reshapes our world.
You’ve written a book critical of AI but you work for a company that is among the leaders in its deployment. How do you square that circle? I work in the research wing of Microsoft, which is a distinct organisation, separate from product development. Unusually, over its 30-year history, it has hired social scientists to look critically at how technologies are being built. Being on the inside, we are often able to see downsides early before systems are widely deployed. My book did not go through any pre-publication review – Microsoft Research does not require that – and my lab leaders support asking hard questions, even if the answers involve a critical assessment of current technological practices.
What’s the aim of the book? We are commonly presented with this vision of AI that is abstract and immaterial. I wanted to show how AI is made in a wider sense – its natural resource costs, its labour processes, and its classificatory logics. To observe that in action I went to locations including mines to see the extraction necessary from the Earth’s crust and an Amazon fulfilment centre to see the physical and psychological toll on workers of being under an algorithmic management system. My hope is that, by showing how AI systems work – by laying bare the structures of production and the material realities – we will have a more accurate account of the impacts, and it will invite more people into the conversation. These systems are being rolled out across a multitude of sectors without strong regulation, consent or democratic debate.
What should people know about how AI products are made? We aren’t used to thinking about these systems in terms of the environmental costs. But saying, “Hey, Alexa, order me some toilet rolls,” invokes into being this chain of extraction, which goes all around the planet… We’ve got a long way to go before this is green technology. Also, systems might seem automated but when we pull away the curtain we see large amounts of low paid labour, everything from crowd work categorising data to the never-ending toil of shuffling Amazon boxes. AI is neither artificial nor intelligent. It is made from natural resources and it is people who are performing the tasks to make the systems appear autonomous.
Unfortunately the politics of classification has become baked into the substrates of AI
Problems of bias have been well documented in AI technology. Can more data solve that? Bias is too narrow a term for the sorts of problems we’re talking about. Time and again, we see these systems producing errors – women offered less credit by credit-worthiness algorithms, black faces mislabelled – and the response has been: “We just need more data.” But I’ve tried to look at these deeper logics of classification and you start to see forms of discrimination, not just when systems are applied, but in how they are built and trained to see the world. Training datasets used for machine learning software thatcasually categorise people into just one of two genders; that label people according to their skin colour into one of five racial categories, and which attempt, based on how people look, to assign moral or ethical character. The idea that you can make these determinations based on appearance has a dark past and unfortunately the politics of classification has become baked into the substrates of AI.
You single out ImageNet, a large, publicly available training dataset for object recognition… Consisting of around 14m images in more than 20,000 categories, ImageNet is one of the most significant training datasets in the history of machine learning. It is used to test the efficiency of object recognition algorithms. It was launched in 2009 by a set of Stanford researchers who scraped enormous amounts of images from the web and had crowd workers label them according to the nouns from WordNet, a lexical database that was created in the 1980s.
Beginning in 2017, I did a project with artist Trevor Paglen to look at how people were being labelled. We found horrifying classificatory terms that were misogynist, racist, ableist, and judgmental in the extreme. Pictures of people were being matched to words like kleptomaniac, alcoholic, bad person, closet queen, call girl, slut, drug addict and far more I cannot say here. ImageNet has now removed many of the obviously problematic people categories – certainly an improvement – however, the problem persists because these training sets still circulate on torrent sites .
And we could only study ImageNet because it is public. There are huge training datasets held by tech companies that are completely secret. They have pillaged images we have uploaded to photo-sharing services and social media platforms and turned them into private systems.
You debunk the use of AI for emotion recognition but you work for a company that sells AI emotion recognition technology. Should AI be used for emotion detection? The idea that you can see from somebody’s face what they are feeling is deeply flawed. I don’t think that’s possible. I have argued that it is one of the most urgently needed domains for regulation. Most emotion recognition systems today are based on a line of thinking in psychology developed in the 1970s – most notably by Paul Ekman – that says there are six universal emotions that we all show in our faces that can be read using the right techniques. But from the beginning there was pushback and more recent work shows there is no reliable correlation between expressions on the face and what we are actually feeling. And yet we have tech companies saying emotions can be extracted simply by looking at video of people’s faces. We’re even seeing it built into car software systems.
What do you mean when you say we need to focus less on the ethics of AI and more on power? Ethics are necessary, but not sufficient. More helpful are questions such as, who benefits and who is harmed by this AI system? And does it put power in the hands of the already powerful? What we see time and again, from facial recognition to tracking and surveillance in workplaces, is these systems are empowering already powerful institutions – corporations, militaries and police.
What’s needed to make things better? Much stronger regulatory regimes and greater rigour and responsibility around how training datasets are constructed. We also need different voices in these debates – including people who are seeing and living with the downsides of these systems. And we need a renewed politics of refusal that challenges the narrative that just because a technology can be built it should be deployed.
Any optimism? Things are afoot that give me hope. This April, the EU produced the first draft omnibus regulations for AI. Australia has also just released new guidelines for regulating AI. There are holes that need to be patched – but we are now starting to realise that these tools need much stronger guardrails. And giving me as much optimism as the progress on regulation is the work of activists agitating for change.
The AI ethics researcher Timnit Gebru was forced out of Google late last year after executives criticised her research. What’s the future for industry-led critique? Google’s treatment of Timnit has sent shockwaves through both industry and academic circles. The good news is that we haven’t seen silence; instead, Timnit and other powerful voices have continued to speak out and push for a more just approach to designing and deploying technical systems. One key element is to ensure researchers within industry can publish without corporate interference, and to foster the same academic freedom that universities seek to provide.
Atlas of AI by Kate Crawford is published by Yale University Press (£20). To support the Guardian order your copy at guardianbookshop.com. Delivery charges may apply.
CHALLENGE
The success rates of Turn Arounds (TAs) within a global oil and chemical company have continued to deteriorate over the years in terms of safety, time and budget overruns.
The last TA in the Netherlands had been difficult and had delivered, among other things, a number of people suffering from burnout.
Axisto was invited to help to turn the tide and to support the TA team to make this TA an international showcase.
APPROACH
Together with all key people we developed a crisp and clear vision on ‘The Best Ever Turn Around’. It was built on their collective knowledge and experience.
In a coordinated process the vision was shared with next levels of the organisation. It was carefully explained and everyone was invited to contribute. This resulted in a vision that was owned by everyone involved, including the contractors.
The vision was combined with a simple but effective performance board as part of the overall visual management approach. Together with some light-touch coaching on-the-job, this resulted in a great teamwork not seen before in previous TAs.
Both production plants are back in operation and the results are impressive. The final evaluation showed additional improvement opportunities in Preparation and dealing with the schedule during execution. Good input for an even better next TA!
CHALLENGE
All current shift supervisors were due to retire within the next 10 years.
Their successors needed to be able to manage their teams in a modern way, focussing on inclusiveness, ownership, good discipline, openness and continuous development.
Furthermore, they had to be able to cope with the ever-increasing digitisation of their workplace.
APPROACH
Our client had come to the conclusion that the current lead operators could not progress to become the “new breed” of shift supervisors. Incidentally, most of them didn’t want that either.
However, working with HR, we developed an approach and a process to begin working with the first group of eight trainees.
The approach is based on the 70-20-10 principle: 70% development through practical assignments, 20% on-the-job coaching and 10% classroom training.
Currently, we are half-way the programme and both the trainees themselves and the site leadership is very enthousiast about the impact
We are look forward to the remainder of the programme. The programme is being codified and documented along the way for a future rollout to the next group of operators.
Massive change ahead
We’ve just had a long period of ever-increasing economic growth, but this has been halted in its tracks because of the Corona virus. The impact of the global lockdowns is huge, both generally, on the economy and society as a whole, and specifically, on companies, families and individuals. A major recession is a given. Consumer behaviour is likely to change in a fundamental way – and so are the ways in which business will be conducted in the future.
How will you navigate these current challenges and position yourself for new growth? Now is the time to review your company, the products and services it offers, the markets it serves and the way it conducts its business. Significant change is required to create the “new normal”. Your strategy needs to be reviewed. New (perhaps digital) operating models need to evolve. And all this needs to be implemented fast so that you can capitalise on new opportunities and build your competitive advantage.
The change challenge
Transformation, strategy execution and performance improvement programmes are notoriously hard to deliver successfully. Most change initiatives struggle to accomplish and sustain the initial programme goals – in fact, only 30 percent are successful. The delivery of a mission critical initiative on time and in full is, therefore, a real determinant of competitive advantage for any company. The vast majority of change initiatives stumble because of precisely the thing they are trying to transform: attitudes and behaviours of people at all levels in the organisation.
Successfully implementing change
The ability for people to change their attitudes and behaviours is primarily driven by their perceptions and intentions. These must be changed first before any change in attitude and behaviour occurs. So how can we do this? People’s perceptions and intentions change due to experiences and information. Perceptions and intentions capture people’s motivations and are indicators of how hard they are willing to try or how much effort they plan to exert in order to perform the required behaviours.
During a change process, people are confronted with two forces: first, change tension (the perceived necessity and urgency of the initiative) and, second, the power to change (the willingness to support and adopt the change and the ability to contribute effectively). Both forces are required in a programme to make change happen.
The way people experience these forces is the key indicator for people’s perceptions and intentions towards an initiative. The rating for these two indicators gives the best prediction about a person’s intention to adopt the attitude and behaviour that is needed. In different parts and at different levels in the organisation, the two forces are likely to develop differently. This drives the need for differentiated interventions for various parts of the organisation. Our Change Insider® provides insights and fact-based guidance for precisely these differentiated interventions to enable the on-time-in-full delivery of a company’s mission critical initiative.
Is your cost structure still fully aligned with your vision?
The global lockdowns are having a significant – if not devastating – effect on most businesses. Many companies will survive the crisis, but they’ll emerge a lot smaller and in a changed environment. Reviewing your vision has now become a necessity. Your current cost structure is most certainly no longer optimal. Now is the time to ensure that every euro spent contributes 100 per cent towards achieving your ambition.
Aligning your vision and cost structure – 4 questions
There are two basic ways to approach cost cutting: targeted and zero-based. As most companies are facing a major, and quite disruptive, change, a comprehensive approach is needed. In our experience, Zero-based Alignment provides the better way to radically redesign the cost structure of a business.
The first step is to work out an appealing vision for your business in the “new normal” and set a challenging cost target. Next, current activities and cost structures need to be mapped and understood in detail. To understand how best to address this, we work with four main questions (see figure below).
The answers to these questions become the input for an iterative process that starts with a redesign of end-to-end business processes. Next, the organisation structure is designed to fit these new processes. The design always starts with a blank sheet of paper. By comparing the existing state with the ideal state, many opportunities are identified for both savings and new growth opportunities.
The Zero-based Alignment workflow
The overall process that Zero-based Alignment follows is shown below.
A new and stimulating future
Zero-based Alignment generates powerful effects: a fresh and energising vision that reflects changed market conditions, significant cost reductions, improved yield on every euro invested, new growth opportunities, and a new and stimulating future for your business. And that’s exactly what every company needs right now?
CHALLENGE
The site had created an appealing vision, and its realisation was seen as crucial for the site’s long-term viability.
However, a year and a half later, little had been achieved and the management team had lost control.
Axisto was hired to help put the realisation of the vision back on track and prevent it from derailing again.
APPROACH
The existing vision and strategic drivers had been relevant and appealing. However, the translation of the vision into strategic goals and targets had been missing. Furthermore, the composition of the Site Management Team (SMT) had changed considerably over the past year.
The first step was to allow a careful onboarding of the new site management team (SMT) members and to strengthen relationships within the SMT. Next, the first step of the Hoshin Policy Deployment process was done, setting level 0 strategy and targets. In a series of consecutive sessions next organisation levels were involved until the shop floor was reached. We followed the ‘catch ball’ process carefully.
Now, threequarters of the year onwards the results are what the SMT was looking for.
To what extent is your organisation able to move quickly and decisively?
Already in normal times companies struggle to deliver business targets on-time-in-full. Typically, we find three related main causes: (1) there is no appealing vision that is owned by everyone in the company, (2) the strategic goals are unclear and (3) the organisation is not aligned. As strategies are not unique anymore, it’s delivery fast and in full is, therefore, a real determinant of competitive advantage for your company.
It starts at the top
All successful change management initiatives start at the top, with a committed and well-aligned group of executives. The first step is to ensure that the team will coalesce around a coherent vision on how the company should look and run like. It is the basis for the identification of the three to five strategic goals with challenging targets.
And cascades down the organisation
Next the top team involves the next organisational level down in refining the vision and gives them the challenging targets set. Collectively, they will need to work out how to deliver them. This requires horizontal within the organisational level and vertical alignment between the levels. This process is iterative and continues right through the levels till shopfloor is reached. What happens is alignment top-down and horizontally at each level.
Catch-ball process
The process is not a one-way street, it is a ‘catch-ball’ process. The higher level throughs the ball, i.e. the target to the next level down. They will sort out how to deliver it. It requires the involvement of the various departments at each level as they are interdependent. If they can’t find ways to deliver and substantiate this, they are allowed to through the ball back with an indication of what is achievable. Our experience is that, if done well, this results in challenging targets which are more often than not higher than the top team started with. The process builds on the collective insight, knowledge and experience. What happens is both alignment and ownership of the targets, a great starting point for moving quickly and decisively.
CHALLENGE
An automotive OEM had experienced a major shift in demand towards new types of product. Demand for established products declined dramatically.
The company reacted to these changes by shifting production to low cost countries. In the remaining high-cost site productivity improvement for direct labour was consistently being addressed, but little attention was paid to the indirect labour. Thus the situation became out of balance, and Axisto was invited to explore indirect cost-cutting opportunities
APPROACH
There are two basic ways to approach cost cutting: targeted and zero-based. As our client was facing major, and quite disruptive, change, they needed a comprehensive approach. Our Zero-Based Alignment (ZBA) provides the better way to radically redesign the cost structure of a business, and this was the chosen approach.
Together with the client team, we turned their deep understanding of the market into a sharp new vision and aimed high with a cost reduction target of € 10 million. After sufficiently understanding the current activities and cost structure, we designed an ideal state from a blank sheet of paper; fully aligned with the vision. Activities were consolidated, the organisation was delayered and built along the end-to-end process. Even after applying the practical boundaries and business risks, the vast majority of the ideal-state design survived the final approval.
