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AI Predictive Supply Chain Management
Categories: Computer Vision, General, Machine Learning

by Anna Melkova

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We know that efficient supply chains can be a crucial part of differentiating an organization from its competitors. But how can a company truly leverage the advances in artificial intelligence and neural networks we've seen in just the last decade?

Particularly for Fast Moving Consumer Goods (FMCG), supply chain trends are emerging that must not be overlooked if a company wishes to become or remain a market leader. Supply chain agility, optimization, sustainability, and ethical considerations challenge established players and give disruptive newcomers opportunities to capture significant market share. This article covers the emerging role of AI in supply chain management. We discuss how it could help companies to more effectively analyze and improve the efficiency of their operations. There is an evolving role advanced technologies play in the FMCG industry. They have potential to dramatically impact supply chain optimization and supply chain productivity.

Demand Forecasting and Inventory Management

If you're able to meet demand more effectively, you can reduce the capital trapped in inventory. And of course, no one likes to be out of stock! Lost sales opportunities are all too often the result. A growing number of businesses are already adopting advanced supply chain management methods to keep costs down and productivity high. And while the competitive landscape is constantly changing, some potential entrants may pose more of a threat than others.

The more supply and demand data you can collect and feed into the AI systems from historical demand and inventory flow, the better the AI's predictions will be on future outcomes. The data must be accurate as well. This is of course why good data collection practices are paramount. Without data, there is no AI.

Internet of things (IoT) devices also play a big role in supply chain management improvements. They can not only collect, but also process, analyze, and share complex and multi-modal data streams. Machine learning can then be used to more effectively conduct business decisions. For example, identifying patterns and anomalies in sales data and identifying areas where delivery resources can be redirected in real time.

Route Delivery Optimization

The high volume of FMCGs places extra strain on the delivery side of the supply chain. Route optimizations have drastically improved within the last decade thanks to advances in neural networks that work to solve the famous, computationally-intensive traveling salesman problem.

AI systems make decisions before the driver even knows a decision needs to be made. When AI learns from previous data, it learns from the very best actions and routes them accordingly. These systems can forecast and create daily routes, and even routes spanning across multiple days, and multiple drivers. There really is no limit to the number of input variables they can accommodate.

In addition, algorithmic maintenance and repair decisions allow for a high level of availability and performance with significant reductions in cost. This is in stark contrast to traditional, reactive repair processes that often happen after negative impact of downtime is felt.

Utilize Virtual Assistants

Virtual assistants can be put to good use internally inside an organization between departments. They can also be used externally between supply chain members. Also called Chatbots, these AI-driven systems can handle trivial conversations with suppliers, place purchasing requests, send and receive governance and compliance materials, receive and categorize documents and so much more. If done correctly, they can handle the lion's share of customer inquiries while providing a pleasant, expeditious service experience.

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Share the Potential of AI

What you know about the capabilities of AI will only take your organization so far. It is crucial with new, innovative technologies that different people with different needs and experiences understand what is possible, and take part in coming up with potential applications. This is especially true for those software engineers who will be implementing the artificial intelligence code first-hand.

Introduce your engineering teams to these new algorithms through webinars, training courses, key speakers, and sharing thought leadership content. Ask them for feedback, and leverage those individuals in your organization, who express an interest in the technology, for full adoption. Workforce readiness is key.

Having a system in place to understand, compare, and contrast the intelligent software systems out there is also important. Not all AI software is created equal, and you'll need all departments in your organization to weigh in on those features, qualities, functions, and outputs expected from any AI supply chain system. Apply those weights to the AI solutions under consideration to narrow down contenders to just 2 or 3 choices eligible for test deployments.

Understanding Feedback at Scale

Whenever a company dealing in FMCG interacts with a retailer or a supplier, a touchpoint exists at which an experience is had whether good, bad or neutral. Feedback from these retailers and suppliers is often collected but rarely understood and fully utilized. Especially when there is a large amount of feedback or when the company is so large that those who receive feedback are organizationally separated from those who have the ability to affect change.

With the advent of modern natural language processing systems, understanding, classifying, and prioritizing this feedback is more than just possible. All that important feedback data (usually unstructured) can be processed and reported on in near real-time at any scale.

This can help improve relationships between suppliers, retailers, partners, customers and others as they manage their consumer goods supply chain.

Real Time Everything

Actionable reports must be delivered from your AI insights as frequently as they will be acted upon. Often this means real-time dashboards, but daily or weekly reports can also suffice so long as the underlying data is fresh.

If you don't automate inventory management to this degree, chances are you'll be making decisions with "stale data." This leads to suboptimal supply chain performance. And of course, this real time data must be fed into the artificial intelligence algorithms with well designed pipelines. These data sources should include both forward-most demand and downstream supply. This "community data" is what gives your business a full understanding of everything that goes into the supply chain. It allows you to identify in advance areas of friction, unnecessary costs, and leverage points.

Smart Warehousing

Amazon isn't the only distribution company that has rolled out smart warehouses. These facilities contain fully or semi-automated equipment and specialized software to eliminate repetitive, time-consuming tasks. Sorting, packing, loading, and unloading during shipping are all automated. Reduced human errors, lower costs, and increased throughput are just some of the benefits. At the same time, humans do work side-by-side in the warehouse to ensure quality control and to maintain the systems.

The sheer volume of goods in the FMCG category is only increasing, and these types of delivery automations will become crucial. However, AI technology not only helps FMCG companies save time, but also makes their businesses more efficient and productive. There are other aspects of logistics that can also be automated, like determining the optimal shipment weight.

Smart warehouses, while expensive on the front end, can efficiently use inventory space, the latest technology, and the minimal personnel to do the work, saving costs in the long run.

Autonomous Vehicles

This high-profile advancement in AI technology is still on the cusp of implementation at scale in the supply chain. Monitoring advancements in this field is a must for any supply chain business, especially for high frequency, high volume chains.

Fully autonomous vehicles may not soon deliver on expectations in the consumer space. Driving environments in cities are highly variable and dynamic. But chances are we'll soon see frequent use of semi or fully autonomous trucks on long-haul routes thanks to the more stable, predictable driving conditions typically of those routes.

In addition, driverless trucks have a significant safety advantage over their human-driven counterparts. In most US trucking accidents, 50% or more of the vehicles involved have a driver as a direct cause. Without a driver, trucks are statistically safer.

Challenges in Implementing AI for FMCGs

Though AI could help CPG manufacturers increase capacity and reduce costs, the CPG industry still faces several challenges:

  • Difficult economic conditions: AI requires significant upfront capital investments, and it is not yet an economic option for some manufacturers.
  • A tough competitive environment: Many consumer goods and packaged goods manufacturers face difficult, intense competition, and market growth is slowing.
  • Pressure from shoppers to deliver a top-notch customer experience: Customers expect a high level of service and transparency, and are looking for solutions that make their lives easier. At the same time, some retailers are fighting for their share of the home shopping market, with consumers moving to online shopping.
  • Conflicting Priorities: Initiatives such as eliminating marginal costs, lowering margins or adding new layers of management and infrastructure all compete for attention and budget.
  • Scalability: There is a need for CPG manufacturers to keep a close eye on its effect on their business as it's rolled out, and to apply tailored solutions on a case-by-case basis to optimize the overall impact.
  • System Complexities: Managing a distributed infrastructure can be complicated. Companies will need to consider a range of cloud options for AI deployment, data protection strategies, and security concerns.
  • Costs of Training: Both the algorithms and the employees are going to require varying levels of training before benefits of AI are realized.

In Closing

Traditional supply chain system designs are shifting to dynamic, simulated ones. This shift is impacting the way companies think about supply chain management and logistics. We are already starting to see many exciting new AI technologies being applied across all sectors. The benefits have been worth the effort for most companies.

Supply chain management and logistics have evolved, and those changes are redefining how technology is used. The future is coming, and it will be a future enhanced by machine learning.