In today’s complex and interconnected world, supply chain professionals must navigate a myriad of challenges, from disruptive events like natural disasters and pandemics to geopolitical tensions and rapid technological advancements. To thrive in this dynamic landscape, organizations need adaptable and resilient supply chains that can bounce back swiftly from disruptions and maintain seamless operations.

Supply chain resilience is the ability to anticipate, mitigate, and respond to disruptions effectively. It is essential for organizations to maintain a competitive advantage and ensure the uninterrupted flow of goods and services to their customers.

Our latest ebook dives into the discipline of supply chain resilience and its significance in today’s volatile business environment. We’ll discuss key principles, emerging trends, and best practices that supply chain professionals can embrace to enhance their organization’s ability to navigate uncertainty successfully.

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Adaptability is second nature to supply chain experts. The very role requires them to be fluid and responsive to keep business operations smooth. The mantra “Expect the unexpected” might as well be their guiding principle.

Yet, what transpires when these changes come too quickly and remain unpredictable? When “unprecedented” is the new norm? This is the challenging transformation confronting many in the supply chain sector today.

The pandemic profoundly rattled the foundations of global supply chains, propelling digital transitions forward. Companies already equipped with digital resources to oversee their supply chains adjusted faster to pandemic-induced disruptions. Meanwhile, the unprepared struggled to keep pace.

And even as the pandemic’s intensity diminished, the Ukrainian conflict, mounting inflation, and other global tensions perpetuated the volatility in supply chains. The call for businesses now is to be more nimble and proactive than ever.

We’ve talked to supply chain professionals all over the world, and our newest ebook –  6 Trends Transforming Global Supply Chains – outlines what to expect in the supply chain and logistics sector for the next 18 months (and how to survive it).

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As worldwide supply chain networks have recovered from the pandemic, professionals working in the space now have a long list of things that did – and didn’t work – as they tried to keep logistics and operations connected and running smoothly. Now that the new normal has settled in, analysts are trying to stay ahead of the curve in both practice and technology. As we’ve talked to supply chain professionals around the world, here’s what they call out as their biggest 

Disjointed Functions and Operations

Supply chains are complex, multifaceted entities that require a seamless and well-coordinated approach to ensure efficiency and optimization. But, when functions and operations get discrete and disjointed, they can significantly impact performance. A lack of integration between different components, communication gaps, operational inefficiencies, and a lack of visibility can cause chaos.

The ripple effects of discrete operations can be substantial. They can result in higher operational costs, decreased efficiency, and compromised service levels. When information isn’t shared across the supply chain, companies can’t respond to changing market conditions or disruptions, leading to lost sales and increased costs. These inefficiencies accumulate, leading to longer lead times, higher inventory carrying costs, and increased waste. All of these issues can negatively impact the bottom line and weaken the competitive position of a business in the marketplace.

Disjointed supply chain functions also affect decision-making. It’s hard to make informed decisions when there’s a lack of visibility into overall supply chain operations. This could hinder the organization’s ability to effectively manage risks, respond to market changes, or pursue new opportunities. Consequently, disjointed functions and operations limit the potential for supply chain optimization, stifle innovation, and impede the ability of a business to achieve sustainable growth and profitability. In this light, integrating these elements is an operational necessity and a strategic imperative.

Less Visibility and Even Less Collaboration

Limited visibility in supply chains has a profound impact on operations. Without a clear view of all supply chain elements, it becomes more and more difficult to predict potential issues like stock shortages or delivery delays. This lack of visibility leads to inefficiencies in planning and forecasting, resulting in lost sales and increased costs. Even worse, when businesses are unaware of their goods’ precise location or status at a given time, it inhibits their ability to respond effectively to disruptions or unexpected changes in demand, further exacerbating the risk of financial losses.

On the other hand, a lack of transparency in supply chains undermines trust and confidence among stakeholders, including customers, suppliers, and even regulatory authorities. In an era where ethical sourcing and sustainable practices are increasingly important, the absence of transparency can damage a company’s reputation and hinder customer loyalty. Without transparency, companies risk non-compliance with regulatory standards, leading to penalties, fines, or worse. Meanwhile, hidden problems within the supply chain, such as unethical labor practices or environmental degradation, can be significant sources of risk if they remain unchecked.

Poor collaboration within supply chains can also be detrimental. When different parties within the supply chain do not communicate or cooperate effectively, a large range of problems can result, including misaligned objectives, duplication of efforts, and slow responses to market forces. Poor collaboration often prevents the supply chain from functioning as a cohesive whole, leading to an inability to capitalize on potential synergies or improvements. A lack of collaborative culture can impede innovation and the sharing of best practices, limiting the ability of the supply chain to adapt and improve over time. A commitment to fostering better collaboration within supply chains is crucial to enhancing performance, improving resilience, and driving competitive advantage.

Siloed Data Keeps Teams Disconnected

Siloed data in supply chains refers to when data is held, managed, and accessed by individual departments or functions within an organization, instead of being accessible across the entire operation. This data fragmentation leads to significant issues in supply chain management. One of the most apparent problems is the lack of a unified, comprehensive view of the supply chain. This can lead to poor decision-making as decisions are often made based on incomplete or outdated data. As a result, organizations may face difficulties in forecasting demand, managing inventory, and scheduling production, leading to inefficiencies and higher operational costs.

In addition to hampering operational efficiency, siloed also stifles innovation and agility. Without a consolidated view of data, it’s difficult to derive meaningful insights and patterns that could drive innovation and continuous improvement. It also hampers an organization’s ability to respond rapidly to changing market dynamics or disruptions in the supply chain. For instance, if data on customer orders is isolated from inventory data, it would be difficult to adjust procurement or production in response to fluctuations in demand. This lack of agility can result in missed opportunities and a decline in customer satisfaction due to the inability to meet their needs promptly.

Moreover, siloed data can impact collaboration and communication within the organization and with external partners. Data not shared freely can lead to a lack of trust and coordination between different departments and stakeholders. This exacerbates problems and means missed opportunities for synergies. For example, if procurement has limited visibility into the inventory data managed by the warehouse, they might order unnecessary materials, leading to increased storage costs and tied-up capital. Hence, breaking down data silos is critical for enhancing collaboration, reducing inefficiencies, and improving the overall performance of the supply chain.

Misaligned Business Goals Hurt Efficiency

Misalignment between business goals and supply chain functions also considerably impacts operational and strategic effectiveness. At the operational level, it can lead to inefficient processes and wasted resources. When the objectives of various supply chain functions (procurement, production, and logistics) don’t align with the business’s goals, departments might work in a way that optimizes their individual performance but doesn’t necessarily contribute to overall business success. For example, if the procurement department focuses only on reducing costs by buying in bulk without considering the broader goal of reducing inventory, it could lead to excessive inventory costs and poor cash flow.

Strategically, a disconnect between business goals and supply chain processes could hurt the organization’s ability to respond to changes in the market or industry. The organization might struggle to realize these goals if these processes are not designed and managed to support strategic goals – such as entering new markets or launching new products. For instance, if a company has a strategic goal to improve its sustainability, but its supply chain processes do not prioritize sourcing from sustainable suppliers or reducing waste, this misalignment could impede the company’s efforts to become more sustainable.

Additionally, when business goals are not aligned with supply chain functions, it can negatively affect stakeholder relationships and the company’s reputation. For instance, if a business sets a goal to deliver excellent customer service, but its supply chain struggles to fulfill orders on time, it could lead to dissatisfied customers and damage the company’s reputation. Misalignment can also bring internal conflict and inefficiencies, as different departments may be working towards different, and sometimes conflicting, objectives. Therefore, aligning business goals with supply chain functions and processes is crucial for enhancing operational efficiency, achieving strategic objectives, and improving stakeholder relationships.

Poor Forecasting Wastes Time and Money

Poor demand forecasting accuracy and speed can profoundly impact supply chain efficiency and overall business performance. A significant issue arising from inaccurate demand forecasting is inventory imbalance. When demand is overestimated, businesses may find themselves with excess stock, leading to increased carrying costs, risk of obsolescence, and potential waste. Conversely, if demand is underestimated, it can result in stockouts, leading to missed sales opportunities and customer dissatisfaction. The inability to accurately predict demand also disrupts production planning, leading to inefficient use of resources and increased operational costs.

In addition to impacting inventory and production, poor forecasting accuracy can also disrupt other areas of the supply chain. For example, it can hinder logistics and distribution planning, leading to underutilized transport capacity in the case of underestimated demand or expedited freight costs in response to unexpected demand surges. This inefficiency in logistics planning can result in higher transportation costs and potentially late deliveries, which can negatively impact customer satisfaction and the company’s bottom line.

When it comes to speed, the ability to quickly forecast demand is crucial for supply chain agility and responsiveness. In an increasingly dynamic and unpredictable market, businesses need to react promptly to fluctuations in demand. If the demand forecasting process is slow, organizations may miss out on opportunities or fail to mitigate risks effectively. This sluggish response can result in lost sales, erosion of market share, and increased vulnerability to disruptions. Therefore, enhancing the accuracy and speed of demand forecasting is critical for optimizing supply chain operations, improving customer service, and driving business performance.

Manual Processes Slow Everyone Down with Busywork

Manual processes and analysis in supply chains can lead to inefficiencies, errors, and slower decision-making. This is especially true for tasks that involve managing large volumes of data, such as inventory management or demand forecasting. Continuing to rely on manual processes, businesses may find themselves struggling to keep up with the pace of operations, resulting in delays, increased costs, and missed opportunities.

Manual processes also open the door to human error.  Data entry, calculations, or analysis errors can lead to inaccurate forecasts, inventory discrepancies, and faulty planning. These errors ripple throughout the supply system, causing a variety of problems such as stockouts, overstocks, or production delays. In turn, these issues can lead to lower customer satisfaction, higher operational costs, and even regulatory compliance issues in some cases.

Manual analysis can hinder the ability to make informed decisions quickly. With the deluge of data generated daily in modern supply chains, manual analysis may not be able to keep up with the speed of business operations. Manual processes can also fail to identify complex patterns or trends that advanced analytics tools could readily detect. Without these insights, businesses may miss out on opportunities for optimization, innovation, and risk mitigation. Manual analysis is typically less scalable than automated analysis, making it ill-suited for companies looking to grow or manage complex, multi-tiered supply chains. Therefore, reducing reliance on manual processes and analysis is often a key step toward improving supply chain efficiency, accuracy, and agility.

Systems Can’t Handle New Data Sources or New Types of Data

The ability to incorporate new sources and types of data is critical for supply chain optimization in today’s data-driven business environment. A lack of flexibility in accommodating these new data dimensions can limit a company’s ability to make accurate forecasts, identify trends, and respond to market changes effectively. New data sources, such as social media sentiment, web traffic, or real-time logistics data, can provide valuable insights into consumer behavior, market trends, and operational performance. If a company’s data management systems or processes cannot accommodate these new data sources, they may miss out on these insights, leading to suboptimal decision-making and reduced competitive advantage.

In addition, new types of data, like knowledge graph data, present unique opportunities for enhancing supply chain operations. For instance, machine learning algorithms can analyze graph data to predict consumer trends and improve visibility into the real-time status of inventory or transportation assets. A lack of flexibility to handle this type of data could prevent a company from leveraging these opportunities, limiting its ability to optimize operations, improve service levels, or innovate.

Beyond limiting the potential for optimization and innovation, the inability to accommodate new sources and types of data can also increase the risk in supply chain operations. Emerging data sources can provide early warning signs of potential disruptions or changes in the market, enabling companies to proactively manage risks. Without the flexibility to incorporate these data sources, companies may react to problems rather than anticipate them, leading to increased operational disruptions, higher costs, and lower customer satisfaction. Therefore, developing the flexibility to handle new data sources and types is essential for enhancing supply chain performance, innovation, and resilience.

Keep an Eye Out

The above mistakes are just a few of the many pitfalls and pratfalls that can cause supply chain chaos. The deeper the alignment between processes, systems, and people, the more like you are to have great success.

If you’re looking to build resiliency and improve visibility across your supply chain operation, Gemini Explore’s no-code solution helps supply chain professionals minimize risk, identify single points of failure or bottlenecks, and develop contingency plans. Learn more at: geminidata.com/supply-chain.

From drone-driven delivery networks to supply chain resiliency, we’ve been featuring our favorite supply chain-related TED talks over the past several weeks. Here are our six favorites:

The Key to Fixing Broken Supply Chains

Generative AI and related technologies are in the news more than ever. While these innovations hold immense promise, addressing the prevalent misconceptions and myths that often surround them is essential. Let’s look at some of the top misinterpretations surrounding Generative AI and shed light on the realities shaping this cutting-edge field.

There are several common misconceptions and myths about generative AI that deserve attention:

1. Generative AI will replace human jobs: While it’s true that generative AI has the potential to automate some tasks across many industries (ask Hollywood right now), it’s unlikely to replace human jobs entirely. Rather, in most cases, generative AI is expected to augment human capabilities, enabling us to accomplish previously impossible or impractical tasks – or simply get more done.
   
   
2. The bigger the AI model, the better: As the titans of tech duke it out to be the LLM to rule them all, you’ll hear a lot of bragging about the size of a systems training data or the many bazillion parameters it uses in its models. And that matters, but it’s not the sole determinant of a model’s performance. The quality of the training data and the training approach employed are equally – if not more – important.
   
   
3. Generative AI models always generate accurate content or “hallucinate”: Language Learning Models (LLMs) are trained on massive text and code datasets, and they sometimes generate text that certainly sounds authoritative and correct but is factually wrong or nonsensical. This highlights the reality that these models are imperfect and can make mistakes – and we need to keep re-reminding ourselves of that.
   
   
4. Generative AI will enable plagiarism and ruin education: Generative AI can indeed generate text and other content similar to human-created work, raising concerns about plagiarism and cheating. However, generative AI can also be used positively in education, such as providing customized content and learning experiences personalized to the ways a student best learns.
   
   
5. Generative AI is a black box: The process of training these models can be complex and opaque (and biased), and so can their results. Right now, there’s a lot of shrugging and saying, “Well, I mean, who really knows?” As more businesses use these technologies, we see a strong call for techniques to make generative AI models more transparent, providing explanations for their outputs.
   
   
6. Generative AI is dangerous: Some fear that generative AI could be used to create fake news or generate harmful content. However, like any tool, generative AI can be used positively and negatively. Its use should be regulated responsibly to ensure it serves constructive purposes. We are already seeing companies and countries start to work this out in the public sphere.
   
   
7. Generative AI can think and create like humans: They generate output based on learned patterns from training data and do not have “thoughts” or “creativity” in the same way that humans do. Whether this approaches the flash of insight or virtuosity of humans remains to be seen.
   
   
8. Generative AI can learn and improve on its own: AI models don’t continue to learn or improve after their training phase without additional data or retraining. Fine-tuning and re-training with new training data is a key part of improving and developing any machine learning system.

And for a halfway myth, let’s look at a switcheroo we see in a lot of tech companies and tech press:

8 ½ General AI is the same as Generative AI. General AI means a system that has the same cognitive functions as a human and can learn just like we do. Think of it as an all-purpose robot that can learn and do any task, sort of like the Terminator. Compare that to a robot built for a particular task in a particular place. General AI is one of those holy grails that most tech companies in the space are working on. But it is not the same as advancements in generative AI, which are still milestones in machine learning – but not the promised land we’ve been promised.

Understanding these myths allows us to appreciate the potential and challenges of generative AI. Despite its powerful capabilities, generative AI is not a magical solution to all problems and must be utilized responsibly, bearing in mind its limitations.

In the realm of Generative AI, separating fact from fiction is crucial for harnessing its potential responsibility. The journey through these misconceptions reveals a more nuanced perspective. While Generative AI has the potential to redefine industries and push the boundaries of creativity, it’s not a silver bullet that replaces human ingenuity or foresight. By acknowledging its limitations and potential pitfalls, we pave the way for a more informed and ethical integration of Generative AI into our lives. As technology continues to evolve, a balanced understanding of its capabilities and constraints empowers us to harness its transformative power while safeguarding against its potential misuse.

One billion people around the world lack access to all-season roads, and modern infrastructure, making it difficult to transport critical supplies, medicine, and goods to market. This lack of infrastructure is a major obstacle to development, and it can have a devastating impact on people’s lives.

In this TED Talk, “No Roads? There’s a Drone For That,” founder and CEO of Matternet Andreas Raptopoulos envisions a new way to transport goods in these challenging environments. His firm’s technology uses a fleet of small, autonomous drones to fly goods between designated points. These drones are able to carry small payloads over short distances, and they can navigate autonomously using GPS and other sensors.

When Ikea decided to put sustainability front and center in their business strategy, what happened?

Ikea veteran and sustainability expert Steve Howards talks about how sustainability has gone from a nice-to-do to a must-do.

Howard discusses how he has dedicated his life to sustainability leading him to join IKEA, a company that takes sustainability seriously. He highlights three significant numbers:

• 3 billion – The number of people joining the global middle class by 2030
• 6 degrees – The increase in global temperatures we are heading towards
• 12 cities – The number of cities with a million or more people at the beginning of the last century. 

These numbers emphasize the importance of sustainability in combating the challenges of rapid urbanization, increasing wealth, and climate change.

What is data philanthropy and how could it revolutionize NGOs and the way we respond to humanitarian crises? 

Mallory Freeman – analytics expert at UPS – shares her experience with the World Food Programme in Rome, where she intended to solve world hunger through data optimization. She initially proposed using data to create the shortest, fastest, cheapest routes for food delivery but discovered the necessary data wasn’t available. Though initially unsuccessful, she remained involved in the humanitarian sector.

Over six years, she witnessed a transformative increase in data usage in the humanitarian world, specifically in decision-making processes. She shared an example where her team used a tool to sift through 900 million options for planning meals within a $6.5 million budget, which successfully reduced costs by 17% and fed an additional 80,000 people in Iraq.