Digital transformation programmes, a new strategy, performance improvement programmes are all notoriously difficult to implement successfully. The vast majority change initiatives struggle to achieve and maintain the planned programme goals. In fact, only 30% are successful. Timely and complete delivery of a critical initiative is therefore the true determinant of competitive advantage for any company.
The vast majority of change initiatives stumble over the very thing they are trying to transform: the attitudes and behaviours of people at all levels of the organisation. Our Change Insider® (CI) measures people’s attitudes and behaviours towards the initiative and how they experience it. Based on these insights, the CI facilitates concrete strategic and tactical actions you need to take to make sure your change initiative is delivered successfully: on time, in full and in a sustainable manner.
HOW THE CHANGE INSIDER® ENSURES SUCCESS
The CI measures people’s perception of an initiative and how they experience it. This is done by asking a number of custom-designed questions as part of a short online survey that takes about 6 minutes to complete. The questions are created by collaborating with people from a cross-section of your organisation and cover the context, objectives, content and approach of your specific initiative. Therefore, the questions are tailored to your organisation and your change initiative. These are the crucial questions that live in your organisation about this specific initiative.
Everyone within the scope of the initiative answers these crucial questions. They do this in a confidential manner and can also add further comments. The survey results are then presented in practical, actionable reports for every relevant cross-section of your organisation.
The reports allow you to compare business units, departments, teams and levels in the organisation. You can see how your people are experiencing the initiative and how the chosen approach influences both the adoption of the change and the actual change itself. The feedback also highlights any differences between different levels or parts of the organisation. Therefore, you can carry out differentiated interventions and keep the entire initiative on track. The survey is repeated at fixed intervals. This way, the CI tracks the effect of interventions on the adoption of change, the actual change itself and the perception of the initiative over time. This provides information about what needs to be done when and where in the organisation to achieve the desired progress and sustainability of the change (see Figure 1).
Figure 1. An example from an CI report that demonstrates how people are experiencing the initiative by showing the development of answers to critical questions over two survey cycles.
The goal of the Change Insider® is very different to employee engagement surveys. The CI focuses on bringing about sustainable change with a specific initiative. Employee engagement surveys measure how dedicated employees are to their workplace or their employer.
THE DYNAMICS OF CHANGE
People’s ability to change their attitudes and behaviours is determined mainly by their perceptions and intentions. So we must first change perceptions and intentions before any change in attitudes and behaviours occurs. But how do we do this? The best way to influence people’s perceptions and intentions is to provide information and encourage people to gain new experiences.
Perceptions and intentions record people’s motivations and are indicators of how hard people are willing to try or how much effort they intend to put in to display the required behaviour. During a change process, people are confronted with two forces: first, a change tension (the perceived need and urgency of the initiative) and, second, the power to change (the willingness to support and adopt the change and the ability to contribute). Both forces are needed in a programme to bring about change.
The way people experience these forces is the most important indicator of 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 required. In different parts and levels of the organisation, the two forces are likely to develop differently, as shown in Figure 2. This drives the need for specific interventions for different parts of the organisation.
Figure 2. An example from an CI report showing the survey results of three teams (A, B, M) and the development of the effects of interventions over two survey cycles. The teams and their development can easily be compared.
The Change Insider® provides fact-based guidance for precisely these differentiated interventions to enable the timely and complete delivery of your mission-critical initiative.
“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.
WHAT IS ZERO-BASED BUDGETING?
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.
ZERO-BASED BUDGETTING IS NOT JUST A COST-CONTROL TOOL
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.
ZERO-BASED BUDGETING TRANSFORMS YOUR BUSINESS
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.
ZERO-BASED BUDGETING IN GOOD TIMES AND IN BAD TIMES – MAINTAIN STRATEGIC MOMENTUM
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.
AND WHAT ABOUT LEAN SIX SIGMA (LSS)?
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.
SO HOW ARE ZERO-BASED BUDGETING AND LEAN SIX SIGMA RELATED?
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.
Disciplined cash and working capital management drives good operational and financial performance. However, performance in order to cash, inventory management and procure to pay slumped over the 5 years prior to the COVID outbreak. A closer analysis reveals that inventory optimisation poses companies the biggest challenge – both in volatile and non-volatile markets. More Cash – Lower Inventory – Better Service, good inventory management is the key.
DELIVER DOUBLE DIGIT INVENTORY REDUCTIONS AND MAINTAIN OR IMPROVE SERVICE LEVELS
Decades of experience have taught us that going straight for the inventories themselves is both the quickest and the surest way of delivering a high-performing supply chain. Inventory sits right at the heart of your supply chain and is both a symptom and cause of your supply chain performance. Getting inventory right keeps your customers happy, increases flow and reduces cost and waste and frees up cash.
At Axisto, we combine the practical business focus of management consulting with the high-speed analytical capability of advanced information technology. We rapidly distil practical insights from data in Enterprise Resource Planning (ERP) systems. Our people concentrate on the human challenges of implementing and sustaining resilient and lean supply chains.
Our unique approach to supply chain puts inventory optimisation front and centre. This allows us to help deliver double digit reductions in inventory while maintaining or improving service levels – at speed in a low risk manner compared to traditional approaches.
OUR INVENTORY MANAGEMENT PROPOSITIONS
Axisto provides three inventory management propositions: inventory optimisation programmes, inventory analytics and inventory maturity assessments.
Our starting point with most clients is a quick scan. On the basis of just 3 standard reports from your ERP system, we quantify improvement potential item by item as well as overall. The output is both an immediate high-level quantification of improvement potential and the basis of a road map to deliver sustainable improvements quickly.
INVENTORY OPTIMISATION PROGRAMMES
We provide expert analytics and effective change management backed up by a clearly measurable business case. Improvements to inventory positions of 20% or more, sometimes much more, are usually achievable within the first year, at a high return on investment.
INVENTORY ANALYTICS
Do you find it difficult to really understand what your inventory data is telling you, or what you should do about it? Do you have optimisation tools that are difficult to use or which give results you know to be wrong, but you’re not sure why? With the proprietary technology that we use, we provide clients with rapid actionable insights into their inventory data.
In addition, we help clients with a range of targeted analytical exercises, ranging from strategic inventory positioning (where in your supply chain should you hold inventory?) through to setting inventory policies for items that are hard to optimise, such as spare parts, or make to order products.
INVENTORY MATURITY ASSESSMENTS
Inventory is influenced by almost every aspect of your business. Therefore, it can be hard to know at an enterprise level where the biggest opportunities for further improvement are, or how you compare to your competitors.
Axisto can take the temperature of your inventory management. We combine a granular, bottom-up quantitative assessment of your potential for improvement with a qualitative overview of your people, processes and systems, including relevant benchmarks, to give you actionable insights into where to find the next step change in your performance journey.
A CASE
CHALLENGE
A medium-sized industrial manufacturing firm with a strong market position and profitability had little historical focus on inventory. The consequence was that inventory was increasing gradually. It was time to act.
RESULTS
Inventory was reduced by more than 50% from the initial baseline over a period of 3 years, while service levels were maintained or improved. Improvements in the underlying data led to a better understanding of how and why to act – inventory management capability was significantly developed within the client’s teams.
SOME QUOTES
“We finally have full transparency of what we have, so we can make fact-based decisions on a weekly basis.” – Automotive manufacturer
“Since starting a programme, we have reduced our inventories by over 50%.” – Industrial manufacturer
“The results are exceptional and have made a major difference to our cash flow.” – Global manufacturing company
“The inventory programme brought a wide range of process issues into sharp focus, with an impact much broader than just inventory.” – Market-leading manufacturer
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.