“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.
CHALLENGE
A firm specialising in electrical and mechanical engineering had decided to introduce process mining in the organisation, and wanted to develop the skills “in-house”.
Before finalising the details, the firm wanted to gain more insight in three areas: the degree of suitability in its own business process environment, the actual possibilities of process mining and what a well-fitting package would be.
The firm decided to undertake a proof of concept (PoC) for this purpose, and asked Axisto to carry it out.
APPROACH
The initiative was driven by the ICT department. They provided a two-person team for us to work with, and a project sponsor was quickly found. With the project team in place we decided to run the proof of concept on the auxiliary equipment management (AEM) process.
There were three questions to be answered:
Are the existing IT systems, their current setup and the data quality suitable?
Can the crucial questions related to performance issues be answered?
When compared with the current analysis tools available in-house, does Process Mining add value?
The firm was also interested in additional dashboarding functionality, so for the PoC we chose a process mining package that would provide this extra information. We also demonstrated a package that was more geared around pure project-based process analyses. Each package was deployed very differently in the organisation
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
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.
CHALLENGE
A high-tech industrial equipment manufacturer has a complex supply chain with many SKUs and long lead times. It was struggling to match supply with demand, which was fluctuating strongly. Multiple engineering changes.
APPROACH
Through data analytics we mapped the characteristics of the business and identified key levers for improvement. This led to the design and implementation of a dedicated S&OP application – a decision-support tool.
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.
INTEGRATED PERFORMANCE MANAGEMENT
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
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.
AGILE COLLABORATION
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.
STRATEGIC WORKFORCE MANAGEMENT
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.
STRATEGIC WORKFORCE DEVELOPMENT
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.
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.
CHALLENGE
Global chemical compounds company with multiple factories with multiple production lines.
Medium term decisions on allocation of products to product lines.
Strong influence of batch size and product sequence on output of production lines.
Need to plan allocation of products to production lines at least one year up front.
Uncertain demand and strong influence of market fluctuations.
Complex global supply chain cost and turnover picture including tax regimes.
APPROACH
Build a mathematical optimisation model of the global supply chain.
Embed the model in a user-friendly software system to support decision making.
Involve future users and management to guarantee quality and acceptance of decision support system.
Transfer system to the organisation and remain available for support.
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
Design of optimal and feasible integral supply chain for various companies in High-tech, Consumer Goods, Food and Building Materials
APPROACH
Determine goal, scope, decision criteria and decision-making process.
Involve all stakeholders and experts during the process to get commitment and create trust.
Make sure all relevant data on actual situation and future trends are available.
Tailor and validate a mathematical optimisation model of the global supply chain.
Organise decision preparation workshops in which scenarios are developed and discussed.
Organise session to discuss recommended options as basis for a decision on the design of choice.