#10: Optimizing ATM Cash Accessibility, Enhancing Perishable Product Profitability, and Streamlining Product Recall Procedures
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Every issue uncovers 3 outstanding AI applications that are reshaping businesses and offering solutions to complex challenges. In this tenth, celebratory issue of Good AI Vibes, we're excited to unveil:
πΆ Money Matters: ATM Cash Flow Prediction (Industry: Financial Services / Business Function: Operational Efficiency & Production)
π₯ Keeping it Fresh: Perishable Inventory Management (Industry: Retail, Consumer Services & Travel / Business Function: Supply Chain, Logistics & Sustainable Operations)
β©οΈ Product Recall Conundrum: Proactive Quality Control (Industry: Industrial Manufacturing, Transportation & Logistics / Business Function: Operational Efficiency & Production)
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Money Matters: ATM Cash Flow Prediction
What's the critical hurdle?
Meet Shantanu, the operations manager at a large commercial bank. Every week, he faces a daunting task. Shantanu must accurately predict how much cash to load into each of the bank's Automated Teller Machines (ATMs). It's like playing a never-ending guessing game. Load too little, and customers are left frustrated when they can't withdraw cash. Load too much, and the bank faces a surplus of idle money that could otherwise be put to better use. More so, excess cash ties up capital that could be invested profitably elsewhere, not to mention the security risks of stocking too much cash in ATMs. The scale of operations, with each of the bank's hundreds of ATMs having different cash demands depending on the locality, day of the week, and numerous other factors, makes it an even more complex puzzle.
How was this initially tackled?
To solve this predicament, the bank initially turned to statistical models. This technique analyses past data to forecast future values. For example, if an ATM generally has higher withdrawals on Fridays, the model would suggest loading more cash on Thursdays.
Why did the initial approach fall short?
Unfortunately, this approach wasn't quite the crystal ball Shantanu had hoped for. The problem? Cash withdrawals from ATMs aren't as straightforward as they might seem. They're chaotic, influenced by a complex interplay of factors like payday cycles, local events, and unexpected circumstances. The statistical model, with its assumption that future patterns will reflect the past, fell short in capturing this complexity and unpredictability. So, Shantanu found himself back at square one, facing frustrated customers and surplus cash.
How did AI revolutionize the solution?
Stepping into the scene, AI provided a sophisticated deep learning solution, revolutionizing Shantanu's approach to managing ATM cash flow.
Instead of merely projecting past patterns into the future, the AI system applied a deep learning technique, learning from a multitude of variables and their complex relationships. Specifically, the AI system was trained on a vast dataset of past cash withdrawals, accounting for various factors such as location-specific events, holiday periods, economic fluctuations, and more. This ability to absorb and analyze vast amounts of data and identify intricate patterns makes it stand apart from traditional statistical models.
For instance, if the system detects a pattern of increased cash withdrawals at ATMs near event locations during the weekend, it would suggest higher cash loadings in anticipation. This also takes into account sudden fluctuations in demand which may not have a historical precedent, such as those caused by a sudden local festival or event.
This deep learning approach drastically improved the prediction accuracy. With AI, Shantanu could now manage cash in ATMs more effectively, minimizing customer dissatisfaction from cash shortages, reducing surplus cash in machines, and improving overall operational efficiency.
Main reference: Sarveswararao, V., Ravi, V., Vivek, Y. (2023). ATM cash demand forecasting in an Indian bank with chaos and hybrid deep learning networks. Expert Systems with Applications, 211, 118645.
See other references at the end.
Keeping it Fresh: Perishable Inventory Management
What's the critical hurdle?
Meet Sam, the Head of Supply Chain at a large grocery retail company with stores spread across various cities. Sam's role requires ensuring that every store has an adequate supply of goods that align with customer demand. While he's adept at managing non-perishable goods, handling perishable products like fruits, vegetables, dairy, and bakery items gives him sleepless nights.
Sam faces a unique challenge every day: perishable goods going unsold and expiring on the shelves. This ongoing problem incurs significant financial losses and contributes to the global food waste problem. There are two core uncertainties that Sam struggles with. First, the unpredictability of customer demand, which can change dramatically and without warning. Secondly, the lead times - the duration from when Sam places an order with a supplier to the time it arrives in the store can fluctuate due to various unpredictable factors.
Take a typical scenario: Based on past data, Sam anticipates a demand for 1000 liters of milk over the next three days and places an order accordingly. However, due to unexpected shipping delays, the milk arrives a day late. Meanwhile, demand for milk plunges unpredictably, leading to a surplus that expires before it can be sold. Such a situation is detrimental to business, tarnishes the brand's image, and contributes to escalating food waste.
How was this initially tackled?
Traditionally, Sam, like many other supply chain managers, used a static inventory control policy to tackle this problem. He would examine past sales data, calculate average demand, and then plan orders based on those figures. Similarly, the lead times were estimated based on supplier commitments or historical trends.
Let's say, on average, the demand for milk was 800 liters per day, and the typical lead time from the supplier was three days. So, Sam would order 2400 liters of milk every three days, hoping to perfectly meet the demand without leading to overstocking or understocking.
Why did the initial approach fall short?
The traditional approach, while straightforward, overlooks the inherent uncertainties in demand and lead times. If demand unexpectedly surges, Sam finds his stores running out of stock, leading to lost sales. Conversely, if demand drops, he ends up with excess perishable goods that can't be sold before they expire.
In terms of lead times, if the supplier delivers the order earlier than expected, Sam might exceed his store's storage capacity or face overstocking. If the delivery is delayed, the result is empty shelves, missed sales opportunities, and unhappy customers. As a result, despite using a conventional inventory control policy, Sam still grapples with the complex problem of managing perishable inventory.
How did AI revolutionize the solution?
AI introduced a new approach to this dilemma. Instead of static demand forecasts and lead times, an advanced AI solution could dynamically determine the optimal replenishment quantity considering service level constraints in each period. This approach considers the uncertainties in both demand and lead times.
Suppose Sam has two delivery options for milk - a fast but unreliable one (delivery time can vary between 1-3 days) and a slower but more reliable one (always arrives in 3 days). The AI system incorporates these variables along with real-time demand data to suggest the best quantity to order for each period.
The AI model also allows for 'order-crossing' β a characteristic often ignored in conventional methods. This means, even if a new order arrives before an old one, it can still be sold, thus minimizing losses due to rigid 'first-in, first-out' rules.
Moreover, the AI solution differentiates between 'first-expired-first-out (FEFO)' and 'last-expired-first-out (LEFO)', two extreme issuing policies for perishables, guiding Sam to choose the right strategy for his business, hence reducing waste.
Through incorporating AI into his strategy, Sam can now more effectively meet target service levels without falling into the trap of unnecessary waste or underserved customers. Consequently, this not only increases profitability but also significantly reduces food waste, enhancing the company's sustainability efforts.
Sam's story serves as a testament to how AI can revolutionize age-old business problems in unexpected ways, setting the stage for the next generation of inventory management.
Main reference: Hansen, O., Transchel, S., Friedrich, H. (2023). Replenishment strategies for lost sales inventory systems of perishables under demand and lead time uncertainty. European Journal of Operational Research, 308(2), 661-675.
See other references at the end.
Product Recall Conundrum: Proactive Quality Control
What's the critical hurdle?
Meet Khalid, a visionary business leader at the helm of a burgeoning electronics venture. The brand has experienced phenomenal growth, with customers lining up for their products worldwide. However, a lingering concern keeps Khalid awake at night - the potential for quality-related product recalls, a nightmare that could tarnish his brand's reputation and result in severe financial losses. The threat of a recall doesn't stop at his company. It could trigger a domino effect, influencing competitors in the electronics market. Understanding how to navigate the complex interplay of pricing and quality in light of this potential hazard is a tricky conundrum, one that traditional methods can't fully unravel.
How was this initially tackled?
To counter this, Khalid, like many leaders, employed historical data analysis and conventional statistical models. These tools utilized past industry experiences with product recalls to inform their quality control measures and pricing strategies. For instance, if a similar firm experienced a recall due to battery malfunctions, Khalid's company would invest more in battery safety and quality, possibly raising prices to cover the additional costs.
Why did the initial approach fall short?
The conventional approach fell short on several fronts. It was reactive, basing decisions on past failures instead of proactively identifying potential risks. Furthermore, it struggled to encapsulate the intricate dynamics of customer perception and sensitivity towards quality and pricing, especially in the wake of a product recall. Critical questions such as how customers' sensitivity to price and quality would change following a recall remained a blind spot in this approach.
How did AI revolutionize the solution?
AI opened up new avenues to address this challenge. Using sophisticated algorithms, Khalid's company can now simulate and analyze different scenarios involving recalls, pricing changes, and quality investments. This AI tool takes in historical data from the company and broader industry to anticipate the probability of a product recall based on their quality investments. It also predicts how consumer price and quality sensitivities might shift following a recall.
Imagine Khalid's company launches a new product line. The AI tool, after analyzing the data, predicts that a particular investment in quality could reduce the recall probability by 20%. Should a recall still occur, the tool foresees an increase in consumer price sensitivity, while their quality sensitivity remains relatively stable. Equipped with this foresight, Khalid can formulate an optimized strategy, perhaps lowering prices after the recall to retain customer trust.
This AI-based solution provides a more proactive, data-driven approach to navigating the complex dynamics of product recalls, allowing firms to make more strategic decisions. It's a game-changer in industries where product quality and recalls can make or break a brand's reputation.
Main reference: Jafarzadeh Ghazi, A., Karray, S., Azad, N., Li, K., et al. (2023). Price and quality competition while envisioning a quality-related product recall. European Journal of Operational Research, 311(2), 486-501.
See other references at the end.
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