Who knew that too many lemons could sour the supply chain so quickly? More lemon doesn’t seem as problematic as a lemon shortage, does it? However, the situation serves as a reminder that an abundance of products can present just as many challenges as a shortage.

The bitter taste of waste

When life gives you lemons or any other produce, it’s usually a good thing – unless you’re a farmer facing a surplus of lemons that nobody wants. Because the overproduction has led to a mismatch with consumer demand. With the surplus in production, farmers are left with an excess of products that are not being sold, which leads to prices dropping faster than a lemon falling off a tree.

According to the Spanish agricultural union COAG, around 400,000 tons – which is about 27% of the planned production- of lemons cannot be sold and will go to waste, and loss is expected to exceed 120 million euros ($129 million).

As lemon prices do not cover production costs, it results in diminished profits and potential financial strain in the main cultivation areas, which even leads farmers to abandon farms and the uprooting of trees.

This has already raisedĚýĚýabout environmental impact as valuable resources such as water, labor, and land are wasted. Additionally, the situation highlights the need for more sustainable practices in agriculture, such as crop diversification, efficient water usage, and responsible waste management.

The food and beverage industry may find themselves with excess inventory that could go to waste if not used in a timely manner. Similarly, the cosmetic and pharmaceutical sectors incorporate lemons in their products for their positive attributes, potentially impacting their sustainable production and risking harm to their companies’ sustainability objectives or brand reputation in the eyes of consumers.

The bad taste of logistics costs

The surplus in lemon production may kickstart the inventory and transportation challenges. The excessive products take up the valuable warehouse space where more sellable stock could have been stored. This may affect the transportation and storage costs, as distributors are forced to find creative solutions to accommodate the overflow of lemons. This could also lead the lemons to expire and lose their freshness, which increases waste, inflates costs, and decreases the profitability for producers.

Squeezing fresh solutions

To avoid being caught off guard by potential disruptions such as overproduction or waste, supply chains must be well managed and carefully planned, and visibility is crucial in achieving this.

On a recent episode ofĚý, Koray KĂśse from Everstream emphasized the significance of visibility as the cornerstone of effective supply chain management.

‘‘Everything starts with visibility,” said KĂśse. “That’s the foundation of knowing and then turning that knowledge into an agile operation that can react quickly to incidents that are comprehensively reported in detail, where you can analyze, specifically, the prioritization of that incident in your value chain.”

Modern supply chain requires modern technologies to help turn risks into opportunities. That is whereĚýĚýcomes into a play to help optimize your risk-resilient and sustainable supply chain.

Richard Howells, VP of Thought Leadership at SAP’s ERP, Finance, and Supply Chain Solutions, stresses the significance of visibility in a recentĚýĚýon the role ofĚý.

“Data visualization and modeling and intelligent summarization came in joint second to help supply chains and operations make sense of data quickly and in a people-intuitive way,” said Howells.

Employing such supply chain planning tools, such asĚýĚý˛š˛ÔťĺĚýĚýsystems, can greatly benefit farmers, wholesalers, and retailers and help in accurately predicting demand and adjusting production accordingly. By utilizing these tools, stakeholders can ensure a more sustainable and risk resilient supply chains for their production processes.

By reducing waste and environmental impact, companies can improve their sustainability efforts and maintain a positive brand reputation. This not only leads to cost savings and improved operational efficiency but also addresses excess inventory in a sustainable and responsible manner.

By acknowledging the necessary tools for the supply chains and implementing the right tools to work together to find innovative solutions that promote efficiency, reduce waste, and support sustainable food system, we can mitigate the negative impacts of overproduction and waste and ensure more resilient and sustainable future for the future generations.

 to learn more about the importance of AI in Supply Chain.

This article first appeared in .

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One such truth is smbolised by the global #BlackLivesMatter movement, which has once againĚýhighlighted the embedded biases in our interconnected social fabric, forcing us all, to re-evaluate long standing notions of morality, fairness and ethics.

It is worth taking pause, to consider whether the exponential technological progress is not also amplifying some of the very same challenges we are trying to overcome, as a global society.

As we strive to meet the needs of customers, we continuously look towards technology. We see leading companies globally investing heavily in technologies such as cloud computing, internet of things, advanced analytics, edge computing, virtual and augmented reality, 3D printing and of course artificial intelligence. And it is AI, which many experts tout as one of the most transformational technologies of our time,in terms of sheer impact on humanity.

Global use of AI has , with estimated revenues of . AI powered technology solutions have become so pervasive, a recent Gallup poll found that .

And yet, a dark side of AI is surfacing with alarming frequency as AI engrains itself in our daily lives.

Bias in the machine

There are ample examples of algorithms displaying forms of bias.

In 2018, of Gmail’s predictive text tool automatically assigning “investor” as “male”. When a research scientist typed “I am meeting an investor next week”, Gmail’s Smart Compose tool thought they would want to follow up with the question: “Do you want to meet him?”

That same year, Amazon had to decommission its AI-powered talent acquisition system . The software seemingly downgraded female candidates if their resumes included phrases with the word “women’s” in them, for example “women’s hockey club captain.”

Many of the large tech firms battle with diversity, with men much better represented than women in most major tech companies. Having gender bias embedded in algorithms designed to support the hiring process presents a significant risk to efforts at achieving greater diversity: Mercer’s Global Talent Trends report for 2019 highlights that 88% of companies globally already use AI powered solutions in some way for HR.

Persecuted by an algorithm

Errant algorithms can be responsible for greater harm than just a few missed employment opportunities.

In June 2020, the on an African American man wrongfully arrested for a crime he didn’t commit after a flawed match from a facial recognition algorithm.

found that facial recognition software, used by US police departments for decades, work relatively well on certain demographics, but is far less effective on other demographics, mainly due to a lack of diversity in the data that the developers used to train these algorithms.

Microsoft and Amazon have halted sales of their facial recognition software until there is a better understanding and mitigation of their impact, on especially vulnerable or minority communities. IBM has even gone as far to halt .

How bias enters our algorithms

McKinsey supports the view that it is actually the underlying data that is the culprit in perpetuating bias, more so than the actual algorithm itself. , the firm argued that algorithms trained on data containing human decisions have a natural tendency toward bias. For example, news articles could instil the common gender stereotypes we find in society simply due to the nature of the language used.

Many of the early algorithms were also trained using web data, which is often rife with our raw, unfiltered thoughts and prejudices. A person commenting anonymously on an online forum arguably has more freedom to display prejudices without much consequence. Any algorithm trained on this data is likely to assimilate the embedded biases.

As observes: “Debiasing humans is a lot harder than debiasing AI systems.”

One example of this is with its chatbot, Tay. Tay was plugged directly into Twitter, where users across the world could interact with it. Users of the popular social media platform promptly got to work teaching the bot racist, misogynistic phrases. Within one day, the bot started praising Hitler, forcing Microsoft researchers to pull the experiment.

The lesson: algorithms learn precisely what you teach them, consciously or unconsciously. And because algorithms learn from data, data matters.

Web data is also not fairly representative of society at large: issues with access to connectivity and the cost of smartphones and data could exclude many – especially minorities – from engaging with online content. This means that data collected from the web is naturally skewed to the demographics that make most use of websites and social media.

Combating bias in our AI solutions

One of the biggest challenges for the creators of AI algorithms trying to eliminate bias, besides merely identifying it, is knowing what should replace it. If fairness is the opposite of bias, how do you define fairness?

Princeton computer scientist Arvind Narayanan argues there are , the problem this creates is that one person’s fairness could be another’s discrimination.

There is arguably a need for greater diversity in the development rooms where AI algorithms are created. A cursory glance at the demographics of the big tech firms shows a disproportionate gender and demographic bias. More must be done to accelerate the synthesis of diverse and inclusive perspectives in the AI creation process, so that AI algorithms and the data they are trained on embody a broad range of perspectives, allowing them to drive more optimal outcomes for all those represented in society.

What can we do to mitigate bias in the AI solutions we increasingly use to make potentially life changing decisions, such as arresting someone or hiring someone? Greater awareness of bias can help developers see the context in which AI could amplify embedded bias and guide them to put corrective measures in place. Testing processes should also be developed with bias in mind: AI creators should deliberately create processes and practices that test for and correct bias. Design should always keep bias in mind.

Finally, AI firms need to make investments into bias research, partnering with other disciplines far beyond technology such as psychology or philosophy, and share the learnings broadly to ensure all the algorithms we use can operate alongside humans in a responsible and helpful manner.

Fixing bias is not something we can do overnight. It’s a process, just like solving discrimination in any other part of society. However, with greater awareness and a purposeful approach to combating bias, AI algorithm creators have a hugely influential role to play in helping establish a more fair and just society for everyone.

This could be one silver lining in the ominous cloud that is 2020.

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