Insights

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways

Following a rigorous evaluation process, Fellowes chose Brookes’ SO99+ platform to support its global supply chain transformation. The partnership will begin with implementation across Fellowes European operations and then a phased expansion to North America operations. 🌍

“Richard Chandler, Head of Customer Experience and eCommerce at Fellowes said “Brookes took the time to really understand our issues and through a series of demonstrations of the SO99+ software gave us confident that we chose the right platform to give us the visibility and control we need to serve customers better and faster.”

“We’re thrilled to welcome Fellowes to the Brookes family,” said Martin Woodward, CEO of Brookes. “Their team brought a high level of rigour and clarity to the process, and we’re excited to be deploying ToolsGroup’s AI based supply chain planning solution.”

The solution, SO99+, will enable Fellowes to streamline forecasting, automate replenishment, and improve service levels across its global network. Brookes’ experienced implementation team and proven record in advanced analytics were key factors in the selection.

“Brookes impressed us with their knowledge and pragmatic approach to solve the pain points in our current technology and processes. I am confident they are the right partner in the first stage of our digital transformation.” said David Haerle, Director of Global Sourcing & Supply Chain at Fellowes.

About Brookes

Brookes is a specialist supply chain planning consultancy that designs, builds, and supports solutions for retail, distribution, and manufacturing industries. With over 30 years of experience and a 97% customer retention rate, Brookes is a trusted partner for organisations seeking to optimise inventory, improve service levels, and unlock value through advanced analytics.

About Fellowes

Fellowes Brands is a global provider of business products and workspace solutions, offering innovative products that improve productivity, organisation, and wellbeing. Founded in 1917 and operating internationally, Fellowes delivers a diverse range of products including record storage solutions, air quality management, business machines, contract interiors and workspace ergonomics. With a strong focus on quality, sustainability, and customer-centric design, Fellowes continues to support modern work environments through smart, scalable solutions. For more information visit www.fellowes.com

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways

This strategic partnership marks a significant milestone in Neon’s commitment to operational excellence and future-ready logistics. By leveraging Brookes’ advanced AI-driven planning capabilities, Neon aims to enhance forecast accuracy, optimise inventory levels, and improve service performance across its supply chain.

🤝 Voices from the Partnership

Martin Woodward, CEO at Brookes
“We’re delighted to welcome Neon as a customer. Our collaboration will deliver a transformative planning solution that empowers Neon to navigate their complex supply chain with confidence and agility.”

Steve Moloney, Head of Supply Chain at Neon Healthcare
“We’re excited to work with Brookes—a partner who understands the importance of collaboration, transparency, and continuous improvement in supply chain success.”

The implementation will be led by Brookes’ expert team, ensuring seamless integration and rapid value delivery. With Brookes’ proven technology deep domain expertise, Neon is set to unlock new levels of supply chain resilience and responsiveness.

🏢 About Brookes

Brookes is a leading provider of supply chain planning solutions, helping organisations optimise operations and drive growth through intelligent planning technologies.

💊 About Neon

Founded in 2015, Neon Healthcare is a privately owned British pharmaceutical company operating across multiple developed markets. Neon manufactures and supplies a range of licensed generic and legacy branded medicines.
Neon is committed to delivering high-quality, often niche medicines to patient populations with specific and often underserved needs.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways

According to McKinsey’s 2025 State of AI report, AI adoption in supply chain is growing—but slower than in Marketing, IT, or Product Development. 

📊 GenAI Adoption by Function (McKinsey 2025) 

• Marketing & Sales / Product Development: 40–55% 

• Service Ops / Software Engineering: 30–40% 

• Supply Chain & Inventory Management: 20–26% 

Supply chain is mid-tier: ahead of HR and Legal, but behind customer-facing and digital functions. 

🏭 Sector-Level Breakdown: Supply Chain GenAI Use 

• Advanced Industries (auto, aerospace): ~26% 

• Retail & Consumer Goods: ~22% 

• Healthcare & Pharma: ~20% 

• Finance / Energy / Media: 13–17% 

Industries tied to physical goods are leading AI adoption in supply chain. 
Service-driven sectors often prioritize digital or customer touchpoints instead. 

⚠️ Why Is Supply Chain Lagging? 

• 🧩 Complexity: AI needs tight integration with physical systems and cross-silo data. 

• 🎯 Prioritization: Many firms began AI efforts in Marketing or IT. 

• 🧱 Cultural Barriers: Risk aversion and silos slow transformation. 

Yet where AI is deployed—in forecasting, inventory, logistics—ROI is among the highest across the enterprise (McKinsey, BCG, Gartner). 

🚀 What’s Next? 

Over 50% of CSCOs plan to scale AI in 2025, especially in: 

• Strategic Planning 

• Risk Management 

• Autonomous Decision Support 

GenAI is emerging in control towers, planning copilots, and real-time simulation tools. 

📈 Supply chain may be a follower today—but it’s catching up fast. 

💡 Takeaway 
AI isn’t just a tech trend—it’s a strategic enabler for future-ready supply chains. 
CSCOs who delay AI risk falling behind their peers—not just competitors. 

🔗 Sources: 
McKinsey – State of AI 2025 
BCG – Where’s the Value in AI? 
Gartner – The Future of Supply Chain Planning 
Hackett Group – 2025 Key Issues Study 

🔎 Are you seeing this shift in your organization? 
Let’s compare notes. 👇 

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways

This article examines the gradual erosion of doubt – “the slow death of doubt” – in technology. We will explore how general public trust in everyday tech is increasing, compare it to trust in cutting-edge GenAI, and then focus on the supply chain planning realm where professionals must decide how much to trust automated planning systems. Finally, we conclude with recommendations for supply chain leaders on responsibly building trust in technology within their organisations.

Rising Trust in Everyday Technology

Personalised content recommendations (like those on Netflix) have become mainstream, as consumers grow to trust AI-driven suggestions.

Not long ago, many people were uneasy letting algorithms make decisions for them. Today, evidence shows that in many domains trust in technology has grown significantly worldwide. Consider the following examples from everyday life, backed by global data:

• Smart Recommendations: Consumers now embrace algorithmic suggestions for products and content. For instance, Amazon’s recommendation engine drives an estimated 35% of the company’s total sales, and Netflix reports that about 80% of the TV shows and movies its users watch come from personalised recommendations rather than direct searches. These figures indicate that people implicitly trust these AI systems to filter choices and surface what they want.
• Cashless Payments: The shift to digital payments reveals growing trust in fintech. Two-thirds of adults worldwide made or received a digital payment in 2021, a huge increase from previous years. In developing economies especially, the share of people using cashless methods jumped from 35% in 2014 to 57% by 2021. Whether swiping a phone at a checkout or transferring money via an app, billions now trust invisible digital processes with their money – a notion that would have seemed risky just a decade ago.
• Biometric Identification: Facial recognition and fingerprint scanning have quickly moved from novelty to normalcy. More than half of consumers now use biometric authentication (such as Face ID or Touch ID) on a daily basis. Using your face to unlock your phone or authorise a payment is widely viewed as safe and convenient. This comfort with biometrics underscores how people have overcome initial doubts about privacy or accuracy in exchange for security and ease of use.
• Digital Assistants: Voice-activated assistants like Siri, Alexa, and Google Assistant are ubiquitous in homes and phones. In fact, as of 2025 there are 8.4 billion voice-activated devices in use, exceeding the human population. From asking for the weather forecast to getting driving directions, a significant share of the public now trusts these AI assistants enough to use them regularly (roughly 20% of people worldwide use voice search actively). The sheer volume of assistant usage reflects growing confidence that these tools are useful and reliable.

Across these examples, the general trajectory is clear: trust in everyday technology is rising. Familiarity, proven convenience, and consistent performance have led to greater public confidence. People have largely stopped wondering “Will this tech work for me?” and started assuming it will. This sets the stage for emerging technologies – but it also raises the bar. If consumers trust technology in their personal lives, they will expect business leaders (including supply chain executives) to leverage trustworthy tech solutions in the enterprise as well.

Generative AI and ChatGPT: A New Trust Frontier

If the examples above represent technology that has earned public trust over time, generative AI is the latest newcomer being scrutinised. Tools like ChatGPT burst onto the scene in late 2022, capturing imaginations with their human-like responses. Adoption was rapid – ChatGPT became one of the fastest-growing applications in history, reaching 100 million users just two months after launch in 2023. In the United States, a Pew Research survey in early 2024 found that 23% of adults had already tried ChatGPT, up from 18% a half-year earlier. Businesses similarly raced to pilot generative AI; a global McKinsey study noted that by mid-2024, 71% of companies were using gen AI in at least one function (up from 65% earlier that year). These figures signal considerable curiosity and optimism about AI’s potential.

However, early adoption does not equate to full trust. In that same Pew survey, the vast majority of people expressed wariness about ChatGPT’s reliability on important matters. For example, when asked about information related to the 2024 U.S. election, about four-in-ten Americans said they have “not too much” or no trust in ChatGPT, whereas only 2% said they have a great deal of trust in it. In other words, virtually nobody is yet willing to blindly trust the chatbot on high-stakes factual questions. This highlights a significant trust gap – people find ChatGPT impressive and useful, but remain sceptical of its accuracy and judgment, often with good reason (early users observed that generative AI can sometimes produce confident-sounding but incorrect answers).

Globally, attitudes toward AI show a mix of enthusiasm and caution. A 2024 KPMG international survey found notable differences by region: in emerging economies, roughly three in five people trust AI systems, whereas in advanced economies only about two in five people trust AI. In other words, even as AI permeates more aspects of work and life, a majority in many developed countries still approach these systems with doubt. Concern about risks is high – one global study reported that four in five people acknowledge AI’s benefits and are simultaneously concerned about its risks and unintended consequences. Issues like misinformation, bias, data privacy, and job displacement temper the public’s trust in AI.

Is trust in generative AI growing? The trend is cautiously upward, but from a low base. Each month, more users experiment with ChatGPT or similar tools, and positive use cases (from coding assistance to drafting reports) are building confidence. Business adoption continues to accelerate: by the end of 2025 many enterprises plan to integrate generative AI into workflows, indicating that organisational trust in these tools is rising as they mature. Yet scepticism remains high in absolute terms. For now, generative AI has not achieved the level of implicit trust that, say, cashless payment or smartphone face recognition enjoy. Instead, it is going through the early-phase scrutiny that all disruptive technologies face – a period in which leaders and users test its limits, verify outputs, and establish guardrails. We can expect that doubt in AI will recede slowly, not overnight. The “slow death of doubt” is exactly that: slow. Generative AI will have to earn trust through reliable results, transparency, and effective risk management, especially in critical applications.

Trust in Supply Chain Planning Technology

For supply chain professionals, the conversation about trust in technology hits close to home. Supply chain planning – which encompasses demand forecasting, inventory and replenishment planning, and production scheduling – is increasingly augmented (and in some cases automated) by advanced software. Terms like “algorithmic planning,” “autonomous planning,” or “touchless planning” are gaining currency. Touchless planning refers to a highly automated planning process with minimal human intervention. Instead of planners manually adjusting every order or forecast, the system itself makes many decisions, only alerting humans for exceptions. This vision promises huge efficiency gains. But it absolutely depends on human trust: planners and managers must trust the system’s recommendations enough to let the “autopilot” run many of their day-to-day decisions.

As supply chain planning systems become more capable, companies are testing “touchless” automated planning – but its success depends on humans trusting these digital decision-makers.

Are supply chain teams ready to trust planning algorithms? There is evidence of both enthusiasm and a trust deficit. On one hand, adoption of AI-driven planning tools is underway. A 2025 industry survey found that 46% of supply chain leaders are already using AI in some part of their supply chain operations, albeit often in early stages. Companies are drawn by clear benefits – AI-driven solutions have demonstrated the ability to cut transportation costs by 5–10%, improve delivery reliability by up to 20%, and reduce logistics costs by 15%, according to the same study. These tangible improvements naturally encourage users to trust the technology more. It’s telling that logistics and transportation (where results are measurable) are the areas where nearly 40% of respondents reported seeing performance improvements from AI. Success stories in areas like route optimisation or automated inventory replenishment are gradually building confidence that advanced planning systems can outperform purely manual methods.

On the other hand, deep-rooted scepticism still exists among planning practitioners, especially when it comes to core functions like demand forecasting. A recent article on supply chain technology noted that “trust, or the lack thereof, is often the stumbling block” for adopting new digital tools in planning. Planners may be wary of the “black box” nature of AI algorithms – if they don’t understand how a forecast was generated, they might doubt its validity. There are also human factors at play: some planners fear that highly automated systems could make their roles obsolete, fuelling resistance or superficial adoption. This trust deficit manifests in behaviour: instead of letting the system run, planners may override or tweak the system’s suggestions because they simply feel more comfortable relying on personal judgment and experience.

The irony is that lack of trust can undermine performance. Numerous studies have found that excessive manual intervention often worsens results. For example, in one case a global beverage manufacturer observed that when planners made significant overrides to the system-generated forecasts, more than 90% of those manual adjustments did not improve accuracy. In other words, the algorithm was usually right, and the human tweaks were usually wrong. This kind of outcome highlights the cost of unwarranted doubt – by second-guessing the system, well-intentioned planners may actually be introducing error or bias. It underscores why building trust is so critical: if end users don’t trust the tool and constantly intervene, the tool cannot deliver its full value.

To bridge this gap, companies are focusing on why users don’t trust the system and how to address it. Common challenges include a knowledge gap – planners might not fully understand the advanced analytics or AI logic underpinning the recommendations. That lack of understanding naturally breeds distrust. Additionally, the planning system’s suggestions might sometimes conflict with a planner’s intuition or recent anecdotal information, creating a “misalignment with reality” in the user’s mind. For instance, an AI forecast might predict a surprisingly high spike in demand for next month; a planner, not seeing an obvious reason for it, might instinctively slash that forecast because it “feels too high.” The system, however, could be factoring in subtle leading indicators that the planner is unaware of. If the spike truly materialises, a lack of trust in the system could result in missed sales or stockouts.

Experts stress that cultivating trust in planning technology is both possible and necessary. The Boston Consulting Group, in a study on AI in supply chains, observes that technical capability alone isn’t enough – “Success requires fostering people’s trust in AI” alongside process changes. They advise companies to “double down on building people’s trust in AI” during implementation, including providing transparency, training, and phased handovers of decision-making. Gartner analysts have similarly noted that to get value from “touchless” or autonomous planning, human planners must understand and buy into the system’s recommendations. In practice, this means giving users the tools to validate or verify predictions. If planners can see why the system suggested a certain production level – say, it detected a surge in online search traffic for a product – they will be more likely to trust that suggestion. Over time, as the system proves its accuracy, the planner’s confidence grows and they intervene less. In one supply chain publication’s guidance: “planners need to trust the system and avoid the temptation to make unnecessary or wrong interventions” if automation is to deliver its promise.

It’s also worth noting that trust is building gradually in this domain. As more digital-native professionals enter supply chain roles, they bring greater inherent trust in data and technology. Vendors of supply chain planning software are incorporating explainable AI features to illuminate the rationale behind forecasts or replenishment suggestions, which can ease the “black box” fears. Consulting firms report an uptick in companies moving from pilot projects to broader deployments of AI in supply chain planning, indicating that early successes are convincing stakeholders to rely more on these tools. The direction is clear: the future of supply chain planning will be heavily automated and analytics-driven, and doubt in these technologies will steadily diminish. But reaching that future requires deliberate trust-building efforts today.

Conclusion: Building Trust – A Leadership Imperative

The analysis above shows that the trajectory is toward greater trust in technology – truly a “slow death” of doubt as familiarity and proven results accumulate. But slow is the key word. For supply chain leaders, the takeaway is that trust in new technology does not happen by default; it must be nurtured. CSCOs and supply chain directors have a pivotal role to play in accelerating the acceptance of advanced planning technologies within their organisations. The end goal is not blind faith in every new gadget or algorithm, but earned trust – confidence built on transparency, experience, and solid governance. Achieving this will enable organisations to fully harvest the benefits of innovations from AI-driven forecasting to autonomous supply chain ecosystems.

In practical terms, supply chain executives should take concrete steps to responsibly build trust in technology on their teams. Here are several key recommendations and calls to action:

• Educate and Empower Your Team: Invest in AI literacy and training so that planners and supply chain staff understand how your technology tools work. Demystifying the algorithms reduces fear. When people grasp why a model is making certain predictions (for example, by reviewing its drivers or assumptions), they are more likely to trust its outputs. Encourage a culture of continuous learning about data analytics and AI.
• Start Small – Pilot and Prove Value: Don’t rush skeptical users into a fully autonomous planning approach without preparation. Instead, start with pilot projects or controlled trials of the technology in a specific area (for example, automating replenishment for a stable product line or using AI to forecast one region’s demand). Measure the results and share the quick wins. When the team sees, for instance, that the AI-driven forecast outperformed the old manual method for three months running, their confidence in the tool will grow organically. Use these wins as internal case studies to build momentum.
• Maintain Human Oversight and Governance: Building trust doesn’t mean removing all human control overnight. Especially in early stages, maintain a human-in-the-loop approach where planners review and approve critical decisions made by the system. Set up governance policies for how AI is used – for example, guidelines on when to override versus when to let the system run. This gives employees a safety net and reassurance that the company uses technology responsibly. Over time, as comfort increases, you can relax the degree of manual oversight – but always keep a mechanism for humans to intervene when needed. Responsible use of AI, with attention to ethics and risk (such as preventing biased or rogue decisions), is crucial to sustaining trust.
• Redefine Roles and Processes: To truly integrate advanced technology, you may need to redefine some job roles and planning processes. Rather than positioning AI as a “replacement” for planners, frame it as a tool that amplifies their impact. For example, planners can shift from spending time on tedious data crunching to focusing on exceptions and strategy – the tasks where human insight adds the most value. Communicate a clear vision of how roles will evolve alongside new systems, and involve the team in shaping that vision. When people see that technology will enhance their work (and not simply render them obsolete), they will more readily embrace it. As one consulting report emphasised, companies should embed trust-building into their operating model, including creating new roles for former decision-makers so they work in tandem with AI rather than feel displaced.
• Celebrate and Communicate Successes: Finally, actively promote the successes that technology enables. When your supply chain planning system accurately predicted a surge in demand and the company met it without stockouts, broadcast that story. Share metrics that matter to leadership and the team – service levels improved, inventory turns increased, forecasting error reduced, etc., and tie those improvements to the new tools or processes. This not only justifies the investment but also reinforces trust: it shows everyone that relying on the technology produced a better outcome. Over time, these stories help turn even former skeptics into advocates.

For Supply Chain Directors and CSCOs, building trust in technology is becoming as important as implementing the technology itself. It’s the human element that determines whether a promising tool actually delivers results. The rewards for getting this right are significant. With trust, your organisation can move faster, act on data-driven insights, and confidently push the envelope in efficiency and innovation. Without trust, even the best technologies will sit underutilised or actively resisted, yielding mediocre gains. As you lead your supply chain teams through this digital transformation, remember that confidence is contagious – when leadership sets the tone by championing technology (with proper due diligence), it encourages the whole culture to be forward-looking and open to change.

In the broader context, the gradual death of doubt in technology is a positive trend. It signifies that people are finding real value in new tools and that, as a society, we are overcoming the fear of the unknown that often accompanies innovation. For businesses, it creates an imperative: those who foster trust will thrive, and those who cannot engender trust in new technologies will fall behind. As one study put it, business adopters must learn to trust AI’s ability to learn and make optimal decisions – the first companies to master this will be the ones to “capture the full value of a self-regulating supply chain,” reaping outsized advantages. In sum, supply chain leaders should seize this moment to drive doubt out of their operations. By thoughtfully building trust in the tools of tomorrow, you will position your organisation to deliver better outcomes today and stay resilient in the face of whatever comes next. The technology is ready – the question is, are we ready to trust it? Each step you take now to cultivate trust is an investment in your supply chain’s future success.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways.

AI won’t simply “take over” traditional supply-chain planning applications so much as it will transform them—and the role of planners—with three key shifts:

From Rules-Based to Data-Driven Forecasting

• Today’s SCP engines rely on statistical models and heuristics (safety-stock formulas, lead-time buffers, basic regression).
• AI augments—or in some cases replaces—those modules with machine-learning models that constantly retrain on real-time sales, supply, and external signals (weather, commodity prices, social sentiment).
• Impact: Forecast accuracy improves, and planners spend less time tweaking parameters and more time interpreting exceptions and driving strategic decisions.

From Periodic What-Ifs to Continuous, Autonomous Planning

• Legacy S&OP cycles run monthly or weekly, with planners manually creating scenarios and iterating.
• AI-driven digital twins simulate your end-to-end supply chain 24/7, flag risks (e.g. supplier delays, demand spikes), and even propose (or automatically enact) adjustments—rerouting orders, shifting inventory, expediting production.
• Impact: The notion of a “plan freeze” window fades. Your plan is dynamic, self-healing, and responsive around the clock.

From Planners as Operators to Planners as Strategists

• Current roles are heavy on data gathering, report-building, and “keeping the lights on.”
• With AI doing the heavy lifting—data ingestion, forecast generation, constraint optimization—your planners become decision architects, focusing on supplier collaboration, risk management, network redesign, and continuous improvement initiatives.
• Impact: Higher-value work, faster reaction times to market changes, and deeper cross-functional alignment.

What AI Means for Your Existing SCP Application

• Embedded AI Modules: Most leading SCP suites (ToolsGroup, Kinaxis, Blue Yonder, o9, SAP IBP, Oracle) are racing to embed ML-based demand sensing, inventory optimization, and autonomous supply response. You’ll see “GenAI for planning” features—chat interfaces that let you ask natural-language questions (“What happens if China’s port labour strike extends another week?”) and get scenario summaries instantly.
• Open Ecosystems & Microservices: Rather than rip-and-replace, vendors are exposing AI-driven planning microservices and APIs. You can bolt on best-of-breed ML engines to your existing planning backbone, reducing risk and preserving your data investments.
• Governance & Explainability: As AI takes a larger seat at the table, robust model-governance, explainability dashboards, and “human-in-the-loop” guardrails will be non-negotiable—especially in regulated industries like pharma and aerospace.

Bottom Line

AI won’t render your supply-chain planning application obsolete. Instead, it will supercharge it:

• Expect smarter forecasts, faster scenario modeling, and automated exception handling instead of rigid batch jobs.
• Your planners will shift from number-crunchers to high-impact strategists.
• The plan will become a living, breathing digital twin, updated and optimized in near real time.

In short, AI is not here to “take over” supply-chain planning applications—it’s here to empower them, and the people who use them, to achieve levels of agility and resilience that were previously out of reach.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways.

Diagnostics isn’t just a tool; it’s a lens. A lens that reveals what’s working, what’s not, and, most importantly, where to go next.

The Evolving Role of Diagnostics in Supply Chain Management

Most companies have systems in place. ERPs, inventory platforms, and forecasting tools—they’re part of the landscape. But these systems are rarely joined-up. Spreadsheets inevitably fill the gaps. Visibility is very poor and usually too late.

End-to-end visibility can provide the data, the basic building block for improvement, but Diagnostics are needed to turn that data into actionable insights. Diagnostics plus scenarios allow insights to be turned into better decisions.

This is where Diagnostics is essential. It bridges the gap between raw data and better decisions.

Key Benefits of Supply Chain Diagnostics

Let’s talk outcomes. Diagnostics impacts three critical dimensions:

• Financial Impact: Imagine reducing your inventory levels by 20% while improving service levels. The released working capital can fuel innovation, fund expansion, or simply strengthen your balance sheet. It’s cash you didn’t realise you had.

• Operational Efficiency: Complex supply chains often mask inefficiencies. Diagnostics uncovers them. By streamlining forecasting and decision-making processes, your team can focus on high-value activities rather than firefighting.

• Service Excellence: Customers expectations are high. Diagnostics ensures that you meet demand consistently, even as market conditions shift. Improved service levels translate into stronger customer loyalty and competitive advantage.

Case Study Insights

Take this example: A UK-based retail leader increased sell-through (inventory turns) by 100% within two years while elevating service levels from 83% to 93%. The secret? Diagnostics uncovered inefficiencies that had gone unnoticed for years. By addressing these with strategic precision, they unlocked millions in working capital which was used to fund growth.

Why CEOs, CFOs and CSCO’s Should Prioritise Diagnostics

For CEOs, it’s about positioning your organisation ahead of disruption. For CFOs, it’s about delivering measurable financial outcomes and for CSCO’s it’s about balancing customer service, operating costs and cash. Diagnostics offers solutions across the organisation.

Unlike traditional consulting, which often fades after a project ends, Diagnostics embeds sustainable improvements. It’s a partnership, not a pitch. And it ensures that your supply chain evolves in step with your organisation’s ambitions.

Your supply chain holds untapped value. Diagnostics is the key to unlocking it.

Let’s explore how you can transform complexity into clarity, inefficiency into impact, and uncertainty into confidence. Contact Brookes for a consultation today and take the first step toward rewriting your supply chain story.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways.

Shein is now China’s largest cross-border fast fashion e-commerce company. Selling to consumers in over 150 countries including the US and the UK, profits last year were $2 billion on a turnover of $45 billion, an increase of five-fold in the last 3 years.

Their success can be attributed in large part to their supply chain strategy, and in particular their ability to predict and respond to emerging demand trends, in close to “real time” – as little as 7 days from product inception to listing for sale. The transformational model of Fast Fashion pioneered by Zara, is further transformed by Shein into “Instant Fashion”.

Shein was founded in 2012 by Sky Xu a Chinese entrepreneur while in his late twenties. He remains the CEO today. According to Sam Chambers writing in the Times “In 2020, covid presented a golden opportunity. Housebound teenagers lapped up his cheap clothes, posting videos of themselves on social media, tyring on their “Shein Haul”. Shein’s algorithms relentlessly scoured social media to identify social trends for Shein’s 250 designers to shape into garments. Orders are then placed automatically across its network of more than 5000 exclusive suppliers all connected via a proprietary system that enable the fashion group to see their available production capacity.”

Today this process supports a breathtaking rate of new product introduction – up to 2,000 new items every day! Clearly, this process cannot be driven by historical sales. It has to rely on “outside-in” market signals, and Shein excel at curating and exploiting this data. The Shein app with over 260 million downloads in 2023 (up from 68 million in 2019) is an immense source of customer information.

The TikTok hashtag #Shein had over 80 billion views in 2023, and an army of fashion bloggers support the brand with videos of their Shein Haul, and a constant stream of coupons and discount codes drive sales.

On YouTube the strategy is similar, with affiliate programs paying for referrals. Shein’s sheer range of items sold through the store and the fact that not everything is always in stock create a somewhat gamified experience. Customers are happy to film themselves unpacking their clothes to try them on and show how they found these inexpensive goods. Not surprisingly, cost conscious Gen-Z is the most important target consumer (equally unsurprisingly, Shein’s brand collaborators include Katy Perry, Lil Nas X, Rita Ora, Nick Jonas, and Hailey Bieber).

In supply chain terms, the rest is relatively straight forward. Orders placed on their apps are fulfilled from a single, huge warehouse in China, shipped by air direct to consumers in around 2-3 weeks – much longer than Amazon, but also much cheaper. Designers will typically commission trial runs of as little as 100 pieces, with visibility of supplier production capacity they receive products in weeks not months. If the algorithms monitoring buying patterns and social media detect that an item is selling it will be reordered. Scarcity in fashion can fortunately be an advantage so short term out of stock are not seen as a disadvantage, and in fact can enhance the brand.

There are some anomalies in this model which can’t be ignored. The fashion industry is the second most polluting industry on the planet. Shein’s argument that producing only what is in demand reduces waste, is true, but doesn’t negate the temptation to replace rather than reuse – the scourge of fast fashion. The cost of shipping by air (itself environmentally damaging) is offset by a tax anomaly – Shein’s sells clothes cheaply, and customer orders are shipped individually to avoid paying VAT, which is only levied on imports over £135. Finally, sourcing ethically, at low cost from over 12,000 factories inevitably presents challenges and raises questions.

In summary, Shein has built their business on a remarkable and very modern supply chain model. They carry out an extraordinary amount of demand sensing across social media platforms, which drives a barely believable level of new product introduction. Manufacturing is closely coupled to the design process. Short lead times, small batch sizes and frequent reordering minimise stock. Since customers pay in advance and suppliers are paid in arrears, working capital will be exceptionally low. Small batch sizes aligned with demand will minimise mark-downs and write-offs.

The Shein demand driven model may not be applicable to every supply chain, but it’s disruptive potential is being felt around the world.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways.

No one outside of Finance is really sorry.

Supply chain teams aren’t sorry because they need inventory to deliver to customers “on-time-in-full” – which is how they are measured. Manufacturing like to build inventory because it allows them to produce in large, efficient batches. They are measured by cost. Purchasing gets better prices if they buy in large quantities, and even Sales inflate their forecasts, just to make sure the products they sell will always be in stock.

In her Forbes article How Manufacturers Lost the Inventory Battle, Lora Cecere asserts that:-

“Today’s average manufacturing company carries thirty days more inventory than at the beginning of the 2007 recession.”

She goes on to explain that inventory is the most significant source of waste and the most important buffer for the supply chain. As variability increases, organizations experience tension in managing inventory trade-offs.

“Caught in a system of dysfunctional metrics, inventory piles grow as teams push for manufacturing efficiency. The management of inventory is typically the responsibility of everyone, and as a result, it becomes no one’s responsibility.

From 2004 to 2022, average global manufacturing inventories grew by thirty days. This is despite the increase in supply chain planning and Enterprise Resource Planning (ERP) investment. Supply Chain Insights research found that 93% of manufacturers greater than $500M in annual revenues deployed this type of technology.

There are three primary reasons for the increase.

1. Rise In Product Complexity. Increasing product complexity increases manufacturing cycle stock requirements (the time to cycle through a product line in manufacturing).
2. Shifts In Sourcing Cycles. The lead time cycle increases due to global sourcing, manufacturing, and distribution outsourcing increased in-transit inventories. (Inventory on trucks, barges, containers, and third-party locations.) Leadtime variability over the last thirty-seven months acerbated the issues.
3. Increases In Demand Variability. As product complexity increased, product forecastability decreased, growing the need for safety stock. A company with a product portfolio with a long tail (low-volume products with high demand variability) requires more significant safety stock inventory levels. (Traditional approaches for demand planning as less effective as it becomes more difficult to forecast an item.)

What To Do?

Educate the organization on the basics of inventory and stop using spreadsheets to make supply chain decisions. (Companies using only spreadsheet analysis on cost miss the impacts on inventory of shifts like tax efficiency, outsourcing, or sourcing strategies.)

Use scenario planning in network design optimization and what-if analysis in discrete-event simulation to set inventory levels for each form and function of inventory. Elements of Form and function of inventory Manage Form and Function of Inventory

Analyze the current health of inventories. As companies recover from the past thirty-seven months of disruption, warehouses worldwide are bloated with incorrect inventories. High levels of inventory decrease customer service reliability. (A full warehouse is inefficient, requiring more labor and sophisticated approaches for inventory management. As a result, order reliability drops as inventory levels rise.)

Take the hit—write off slow and obsolete inventories. As you manage the write-offs, analyze the root cause of the inventory write-off as a learning exercise for the organization.

Summary

Traditional methods of focusing on cost management on spreadsheets or using safety stock optimization in supply chain planning must be revised. Train the organization to recognize and manage the form and function of inventory as the organization makes shifts in product complexity, sourcing strategies, and financial tax efficiencies to measure and understand the impact on the form and function of inventory. Manage inventory as an asset. Avoid waste.”

Credit Lora Cecere. Forbes (May, 2023), How Manufacturers Lost the Inventory Battle

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways.

“50% of my orders can be placed automatically, I just don’t know which 50%…”

As humans, we grapple with uncertainty, and our preference for stories over statistics often influences our decision-making processes.

The fundamental issue lies in our cognitive biases, particularly our struggle with probability. Our brains are wired to focus on immediate threats and historical occurrences, making it challenging to engage in thought experiments about future events. This innate bias leads to misconceptions and miscalculations of probabilities, shaping our distrust in technology that deals with uncertain outcomes.

Deterministic technology, which follows fixed rules and algorithms, is relatively easy for us to trust. Computers executing instructions deterministically provide consistent outcomes, adhering to known rules and making us feel in control. However, these systems struggle with uncertainty and adapting to novel situations, risking catastrophic failure when faced with unexpected events.

On the other hand, probabilistic technology introduces statistical methods and probabilities to decision-making processes. Machine learning models and natural language processing exemplify these systems, offering adaptability to changing conditions and handling complex, real-world data. Probabilistic models assess risks and prioritize actions, but their outcomes can be unsettling for users who prefer certainty.

The crux of the matter lies in our familiarity bias. Deterministic technology aligns seamlessly with our intuitive understanding of cause and effect, fostering trust in what we can predict and control. Probabilistic technology, by contrast, introduces an element of risk that provokes unease. The potential for errors due to imperfect data or assumptions, coupled with the challenge of understanding why a probabilistic system made a specific decision, contributes to the skepticism surrounding these technologies.

Which 50% of our orders can we trust the system to place automatically? The (unsatisfactory) answer is ‘it depends’.

The article “AI will transform supply chains but maybe not in the way we expect” (https://bit.ly/3HQXh7m) suggests that demand forecasting could be a game-changing technology thanks to AI. With access to increasingly vast oceans of data, Machine Learning promises to transform our ability to predict the future. Uncertainty won’t be eliminated, but it will significantly reduced and better understood.

We can start by looking at the accuracy of machine predictions, compared to the accuracy of human predictions. This is sometimes called “forecast value add” – positive when human intervention improves forecast accuracy, negative when technology comes up with a better answer. In practice, we see more negative forecast value add, than positive. Humans are usually over confident in their ability to predict the future compared to machines.

It is only part of the answer, but if we start by looking at items with better statistical forecasts than human consensus forecasts we have a good list of candidates for automation.

As we develop this theme, in future articles we’ll explore the importance of intrinsic predictability vs forecast accuracy, and discuss the relevance of six sigma techniques to the automation of supply chain planning.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways.

Gartner’s hype cycle, which gauges enthusiasm for new technology, placed AI at the “Peak of Inflated Expectation” back in August 2023 – a significant time in the realm of AI developments.

While it’s true that many claims are inflated, some even repackage old technology as AI, and others are simply untrue, there’s a consensus that AI will indeed be transformational – perhaps just not in the most obvious places. Drawing on historical examples like SMS messaging, which was initially adopted by college students in the early 2000s to save money on phone bills, shows how seemingly unlikely technologies can become transformative. A generation was born fluent in two-thumb touch typing, shaping mobile phone usage today.

Closer to home, drones provide another example. Four years ago, some pundits (though not all) predicted they would revolutionize final mile delivery. Instead, in the past year, drones have transformed modern warfare rather than supply chain logistics.

While it’s too early to bet on AI’s game-changing impact on Supply Chain Planning, there are leading candidates. At the forefront is the application of AI in demand forecasting. Traditional methods struggled to predict consumer behaviour intricacies, leading to the exploration of AI algorithms armed with vast datasets and machine learning capabilities. Although AI shows promise in providing more accurate insights into demand patterns, challenges persist due to the complexity of market fluctuations.

Inventory optimization, a critical aspect of supply chain planning, envisions improvement through dynamic adjustments facilitated by AI. The promise of maintaining optimal stock levels, reducing carrying costs, and minimizing stockouts relies on AI’s adaptability to real-time data and market conditions. However, practical implementation remains a work in progress, subject to the unpredictable nature of the global supply chain.

Enhanced visibility across the supply chain is a shared goal, with AI touted as a powerful tool for real-time monitoring. While AI does provide valuable insights, the sheer volume of data processed can sometimes introduce complexity rather than clarity, posing challenges for decision-makers extracting actionable information from the data deluge.

Supplier risk management leverages AI’s continuous monitoring capabilities to identify potential risks. However, its efficacy hinges on AI algorithms’ ability to navigate the intricate landscape of geopolitical uncertainties and economic fluctuations, which remains a complex task.

Transportation management witnesses AI integration in route optimization, load scheduling, and overall operations. Promises of cost efficiency and reduced environmental impact are tempered by the realities of unpredictable traffic, unforeseen delays, and the inherent complexities of global logistics.

Order fulfillment, the heartbeat of supply chain planning, undergoes transformation with AI integration. While optimization holds promise, seamless integration into existing logistics systems remains a challenge, affecting goals such as order accuracy and a consistently smooth customer experience.

Dynamic pricing strategies, essential in a competitive marketplace, turn to AI algorithms for insights. The effectiveness of these strategies is a delicate balance, influenced by the ever-changing dynamics of the market and AI’s ability to adapt.

Collaborative Robots (Cobots), embodying harmonious coexistence of AI and human labour, are met with both optimism and skepticism. The adaptability and learning capabilities of machine learning hold promise, but we are only at the beginning of a long and challenging journey.

Sustainability and green supply chains look to AI for optimizing processes and reducing environmental impact. While AI analytics hold potential for sustainable decision-making, practical realization is an ongoing journey, influenced by the ebb and flow of businesses’ commitment to environmental responsibility.

In conclusion, AI integration into supply chain planning is proving to be a complex journey, with promises and challenges coexisting. If history is any guide, the transformative changes won’t be the most obvious ones we’re thinking of today. Staying informed and aware is crucial, as William Gibson observed in 1993, the future is already here – it’s just not evenly distributed.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways.

This, in his vision, represents the logical culmination of automating supply chain planning and was central to the “lights-out factories of the future” – a concept conceived in the 1990’s to describe fully autonomous manufacturing plants which needed no light because they needed no humans.

The rate of change has not slowed down. Technological evolution is currently going through a revolution, fuelled by AI and, for supply chain in particular, Machine Learning. The media is awash with confusing, unsettling visions of how the future may look.

When it comes to the future of knowledge work (in our context supply chain planning) there is no doubt that technology will take on more and more human tasks in their entirety, but equally, a growing number of tasks will be shared between machines and humans.

In his article, “Decoding the Jagged Frontier of AI,” Dan Martines describes a framework from Harvard Business School for classifying tasks. Tasks better done by humans and tasks better done by machines are separated by a “jagged frontier”. Tasks within the frontier are clearly done better by machines and those outside, better done by humans.

Outside the frontier, Martines discusses whether human-machine collaboration improves performance further (the answer from the BCG study he references is, not always). He goes on to describe a second framework of Centaurs and Cyborgs – Centaurs, where work is divided between humans and machines, and Cyborgs, where humans and machines seamlessly interchange roles, making it challenging to distinguish if the machine is working for the human or the human is working for the machine.

While this may be daunting for knowledge workers, it echoes a familiar concept for production workers. We might feel more comfortable in the role of a farmer driving a tractor, than a line attendant replenishing materials at the pace of the packing machine.

These emerging knowledge working models, the cyborg and the centaur, align with Joe Shamir’s historical supply planner models—the cyborg within the loop and the centaur above the loop.

In this evolutionary process, the first consideration is whether the task lies within the jagged frontier. If it does, and once processes, technology, and data are robust, along with sensitive and fair redeployment of the humans affected, the operation can be entrusted to machines, with humans positioned clearly above the loop, monitoring, controlling, and intervening as exceptions arise. (After facing early resistance, the machines are now doing a great job, checking tickets at the gates of the London Underground, hardly noticed.)

Beyond the jagged frontier, the future model is far less clear, and clouded by social and ethical questions. How should we collaborate with our new artificially intelligent colleagues?

Right now, whether the planner is in the loop or on the loop depends largely on the level of autonomy and complexity of the system that executes the plan. For example, in a supply chain planning system that uses AI, the planner may be in the loop if the AI is not fully reliable or trustworthy, and the planner needs to verify or override the AI’s decisions. The planner may be on the loop if the AI is more reliable or trustworthy, and the planner only needs to supervise or audit the AI’s decisions.

The planner being in the loop or on the loop also depends on the preferences and skills of the planner. Some planners may prefer to be more hands-on and proactive, while others may prefer to be more hands-off and reactive. Some planners may have more expertise and experience, while others may have less.

According to the BCG study, centaurs and cyborgs will benefit from AI in different ways. Centaurs tend to perform better on complex tasks that require creativity and judgment, while cyborgs tend to excel on simpler tasks that require speed and accuracy.

For now, at least, the planners themselves, their skills and personalities will help shape the definition of the next human machine collaborations.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways.

(OpenAI’s mission – “to build artificial general intelligence (AGI) that is safe and benefits all of humanity.”)

OpenAI, originally a not-for-profit organization co-founded by Altman and Elon Musk, transitioned into a “capped profit” entity with the involvement of Satya Nadella and Microsoft.

The conflict reportedly revolved around concerns regarding Q* (pronounced Q-Star), a ground breaking model capable of solving previously unseen maths problems—an achievement with significant implications for Artificial General Intelligence (AGI).

It is crucial to distinguish OpenAI’s mission to build AGI from the specific AI applications found in the realm of Supply Chain. While AI refers to machines proficient in specific tasks like image recognition or natural language processing, AGI aims to replicate or surpass human-like capabilities across any task. AGI’s potential to revolutionize industries and match human intelligence is a prospect that has sparked debates and concerns.

To quote Melvin Kranzberg, technology is neither good nor bad; nor is it neutral.

The article “Supply Chain Automation – Should we be Afraid” emphasized the need to approach AI with a combination of engagement and caution. Two weeks later, amidst the turbulence at OpenAI, at least this advice still seems to be valid.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopian ways.

In today’s fast-paced world, relying solely on single forecasts might lead to suboptimal decisions. Probabilistic planning leverages data analytics and sophisticated algorithms to model a spectrum of potential scenarios to:

1️⃣ Mitigate Risk: By understanding the range of possible outcomes, you can proactively plan for contingencies and minimise the impact of unexpected disruptions.
2️⃣ Optimise Inventory: Balancing stock levels becomes more precise, reducing excess inventory costs while ensuring sufficient supplies to meet demand fluctuations.
3️⃣ Enhance Decision-Making: Armed with insights into various outcomes, you can make smarter decisions, from production planning to resource allocation.

At Brookes, probabilistic planning isn’t just about adapting to change; it’s about thriving in uncertainty and turning it into a competitive advantage.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopThe rapid advance of artificial intelligence (AI) has sparked debates and stirred fears, and the predictions from OpenAI, the mastermind behind ChatGPT, are anything but reassuring. According to OpenAI, the most significant threat from this new wave of AI looms over high-paying jobs, with those in the six-figure salary range being three times more vulnerable than their £30,000 counterparts. Leading consultancies add to the ominous forecast, emphasizing AI’s potential to automate the application of expertise, making it seem like the era of job automation is no longer a distant future but a present reality.

However, real-world observations paint a nuanced picture. A US study exposes the immediate impact of AI on white-collar freelancers, particularly copywriters and graphic designers, who witnessed a rapid decline in job opportunities and earnings within months of ChatGPT’s introduction. The sting of AI wasn’t selective, hitting even the high earners, raising concerns about not just task substitution but also reducing the value, and consequently remuneration for the remaining human-centric work.

Contrastingly, a Harvard Business School study on the employees of an international consultancy presents a more optimistic outlook. Those equipped with GPT-4, the latest AI offering from OpenAI, showcased remarkable improvements in productivity,  completing 12% more tasks, 25% faster with a 40% improvement in quality. Surprisingly, the biggest performance gains were recorded among the less skilled workers, challenging the notion that AI primarily benefits the highly skilled.

The multifaceted nature of roles reduces the risk of complete automation. Tasks that involve a range of skills are less susceptible to being entirely taken over by AI. Furthermore, the successful integration of AI into the workforce requires treating it as an extension of human capabilities, constantly monitoring and refining its outputs. This nuanced approach involves collaboration rather than submission ensuring that AI complements human skills rather than overshadowing them.

In essence, the evolving landscape of AI and it role in Supply Chain automation demands a delicate balance between caution and collaboration. Whether AI becomes a feared adversary or a fruitful ally hinges on how we navigate these complexities, embracing a future where human and machine capabilities work in tandem for optimal outcomes.

Are you suffering from AI fatigue? It seems like AI is everywhere, with endless and often exaggerated claims about how it will revolutionize the future, both in utopian and dystopThe rapid advance of artificial intelligence (AI) has sparked debates and stirred fears, and the predictions from OpenAI, the mastermind behind ChatGPT, are anything but reassuring. According to OpenAI, the most significant threat from this new wave of AI looms over high-paying jobs, with those in the six-figure salary range being three times more vulnerable than their £30,000 counterparts. Leading consultancies add to the ominous forecast, emphasizing AI’s potential to automate the application of expertise, making it seem like the era of job automation is no longer a distant future but a present reality.

The overall effect is rapidly escalating costs and, perhaps more importantly, extended lead times and stock outs due to problems in the supply chain.

The question is, what should businesses do to meet the new normal? When looking at supply chain strategy it is useful to consider the Donald Rumsfeld quote about known knowns, known unknowns

Known Knowns – the basic design:
Traditional supply chains are developed to optimise them for the current situation. This typically involves network modelling to optimise costs to meet known customer service levels. A lot of focus is on tuning the supply chain using lean and six sigma approaches to produce the optimised cost structure. This produces effective strategies for the known environment … but are often not best placed when the environment changes. For example, a lot of designs are based on sea freight with no allowance for air … but when asked a few years later one finds a portion of air freight to cope with unexpected situations.

Known unknowns – resilience:
Recognising that we do now live in a perfect world and change is a constant, supply chains have been adapting to deliver resilience to cope with changes. Developing these supply chains involves identifying potential changes that might occur and then modelling these as sensitivities to ensure the supply chain is still effective.  This typically involves factoring in areas such as transportation prices, forecast errors, labour cost changes.

Unknown unknowns – agility:
While a resilient supply chain is good, they are limited by the ability to understand and model the potential degrees of uncertainty. Few supply chains will have been modelled to cope with the activities that have happened in the last few years. The pandemic has fundamentally upset the equilibrium with supply chains moving well beyond their design specifications: transport costs from China increasing ten fold, port closures and delays, border closures and even factories going off line due to electricity shortages in China.  These require a quite different thinking for supply chains – building agility to cope with what changes that are almost unimaginable. Critical is rapid access to information, the ability to adapt the supply chain and the ability to make decisions at speed.

Agile supply chains require a different way of thinking and a control tower concept to manage the end-to-end supply chain. While there is no silver bullet, a number of concepts are important: the offer, visibility, strategic stock and scenario planning.

Often overlooked in supply chains but critical in building agility is how businesses need to adapt the offer to the supply chain capability. Some supply chains are too complex and so are too susceptible to meet changes.  Concepts that need to be considered include:

Modularisation:
simplifying the offer so that there are fewer more common parts. While customisation can be important, need to know how to focus and prioritise for shortages

Substitution:
identifying up front potential substitutions that, in an emergency, may be acceptable to customers and then focusing on these in periods of shortage

Localisation:
developing options closer to customers. These may have higher prices and less customisation but many customers will be willing to accept the trade-off.

Most companies fail to recognise that customers are more open to these and similar concepts than they think. Thinking these through ahead of time will enable a more agile response.

In a rapidly changing environment, visibility of what is happening is critical. Advanced supply chains invest in developing this visibility of not only own supply chains but also across the extended supply chain. This requires an understanding of both orders and inventory that support the control tower in making decisions for the supply chain. In customer surveys, one of the top requirements is providing certainty and visibility is a cornerstone of this.

Most of the focus in the past has been on lean just in time supply chains with limited inventory – in lean it is defined as one of the sources of waste. However, what this often overlooks is that not all inventory is waste – who would buy from a shop with no inventory? Inventory can be vitally important to cope with uncertainty as witnessed by the lack of sufficient PPE to cope with the pandemic.  Agile supply chains have digital twins that are used to model the potential vulnerabilities and deploy strategic stock to reduce the risks.

Digital twins can not only look at the absolute levels of stock but also the locations – in the past supply chains were often configured with stocks held near sources and only moved when demand was visible but with transport delays this is changing to get stock nearer customers as soon as possible.  These changes require “real time” modelling to adjust networks at speed.

Coupled with the increased visibility is the ability to understand what this means and to make rapid decisions. While systems can not make all the decisions, having advanced tools that can be used to rapidly replan supply chains and enable decision making is increasingly going to be a key. This capability needs to be deployed before the events leveraging AI so that managers can make rapid decisions through a digital twin that enables rapid evaluation of what is happening. The biggest challenge is not making the wrong decision but making no decision – his needs to be accompanied by a cultural shift and making “no regret” moves. Similar to turnarounds when time is money.

We live in uncertain times and this is not going to get easier. The pandemic has taught us that the best designed supply chains can collapse if they go outside their design parameters. Going forward agility and the ability to change will be critical – requiring accelerated decision making supported by advanced planning systems to free up management time to think about how and what to do.