How Is AI Improving Supply Chain Management Processes in 2021?

Lerma Gray
Published 09/18/2021
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Supply Chain ManagementDo you experience shipping delays at your company?

The job of a supply chain manager is filled with challenges.

An unforeseen traffic jam, perhaps due to unexpected weather conditions, means having to explain to impatient customers why they’ll not get their orders in time.

Alternatively, because of traditional forecasting strategies, your anticipation of market demand is off the mark, resulting in under or overproduction. Either way, these are missed sales opportunities or inflated storage expenses.

Additionally, vendor selection is not optimal as you rely on manual document processing, and key contract details consequently go without notice until it’s too late.

With AI-driven supply chain management solutions, and mostly through the power of intelligent data and automation, you can nip some of these challenges right in the bud.

In this article, we’ll be discussing how artificial intelligence is improving supply chain management processes in 2021.

Let’s get started.



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1. Warehouse Efficiency

A supply chain is only as efficient as its warehouse processes.

When warehouse processes are not ideal, supply chains tend to pay the price. With suboptimal picking, sorting, and order handling procedures in general, shipping delays are inevitable.

As a result of these delays in fulfillment, customer satisfaction falls causing brand loyalty to plummet as market confidence erodes.

The rate of chargebacks ultimately increases too as scorned customers begin to reconsider their options.

Warehouse robots are already aiding supply chain managers to avoid chargebacks. By automating basic iterative work like product identification and sorting, AI tools speed up delivery processes.

Collaborative bots like Chuck assist and guide human pickers to perform warehouse chores more effectively.

Chuck works in the same way as automated guided vehicles. Using AI-powered engines, this intelligent mobile unit is able to find its way around sorting towers without human assistance. This is made possible by computer vision and proximity sensors.

As a production manager, you can assign orders to Chuck, who guides your human sorters to the day’s duties. The bot displays the item alongside the desired quantity for the order on a screen, leading the picker to the product destination.

The collaborative bot feeds from data during its day-to-day routines. This data is stored in the cloud and is used to help the bot improve its duties courtesy of machine learning algorithms.


2. Predictive Modelling

Is equipment failure a big headache?

If your maintenance strategies are more reactive than proactive, you’re not alone.

According to market research by Vanson Bourne, over 80% of companies fall victim to unexpected equipment outages.

As a result, many companies experience shipping delays, as a loss of productivity hours leads to a pile-up along the chain.

Artificial intelligence software is helping supply chain managers by offering predictions on equipment failure.

Through the power of predictive models, it is possible to establish a failure probability guide, from operation data. Therefore, production managers can implement effective contingency plans.

As a real-life example, Kone is using IBM Watson for its smart elevator projects.

The company is able to estimate individual faults by machine learning software which analyses the operational data of each elevator. Consequently, maintenance is done in anticipation of predicted problems through the power of intelligent data science.

Do you struggle with unexpected equipment failure at your company?

If so, you’ll find this article on interesting data science tools of 2021 quite helpful.

The article contains a curation of the best data science tools of today. It includes open-source packages that’ll help you to make more accurate predictions about equipment downtime.


3. Route Optimization

If you’re involved in transport logistics at your company, you’ll know how crucial route optimization is.

Often, freights don’t always take the best possible routes, leading to traffic holdups, or rerouting and increased shipping costsin the long run.

According to the American Transportation Research Institute, the traffic industry loses $74 billion every year to traffic congestion.

With AI-driven route planning software, companies can monitor traffic conditions as they change, and alleviate a significant amount of congestion losses.

Via machine learning algorithms, route planning software can predict weather and other factors that might make a usually reliable route unreliable.

Further, the algorithm can learn from peak traffic hours, and other environmental or infrastructural patterns, for better route decision making.

Speaking of traffic congestion, you should also check out this incredible article on how intelligent transportation is solving traffic jams and pollution in smart cities. Italian researchers are making great progress with smart mobility units, and you can learn all about it via the link above.

AI-powered route optimization analytics is already in implementation as we speak.

The Valerann smart road system is providing companies with large supply chains the ability to plan out routes based on real-time data.

And smart-road systems do more than just find the shortest paths between supply chain anchors.

Via a web-based platform, Valerann software, for instance, observes real-time traffic, weather conditions and ongoing road works to suggest the shortest or problem-free paths.


4. Demand Forecasting

Do you struggle with demand volatility?

It’s quite normal for perennial market trends to change direction in a minute, leaving production managers stranded with fully-stocked warehouses and few customers.

By adopting AI-driven demand forecasting, you can make better supply decisions based on real-time market data, as well as buying predictions made by machine learning algorithms.

And the good news is, you don’t even have to be a data scientist to learn how to make sense of market data for demand forecasting.

With this article on how to become a data scientist without a college degree, you can learn how to predict customer behavior. You’ll be able to estimate buying trends for better supply planning, armed only with the working knowledge you have, and a couple of excellent resources.

As an example, French manufacturer Danone Group has implemented machine learning systems for more accurate market forecasting.

The food producers were in dire need of a better method of demand forecasting, given the short shelf-life of their products. Danone found the answer in machine learning software, which was able to learn and improve from past errors in market judgment.


5. Document Processing

What procurement challenges do you face?

Dark data is probably high up on that list, as procurement information can be locked in separate spreadsheets across data systems, or even dispersed across countless physical files.

As a result, vendor selection can be tough because of data silos hiding important details.

With intelligent document processing (IDP) software, you can get a full view of important data you need across your organization to make well-informed procurement decisions.

IDP tools such as Adlib, for instance, enable you to digitize files through optical character recognition, among other AI-powered data extraction tools. After which, the data can be classified for better retrieval. As a result, you can perform better contract analytics of your vendors with intelligent document processing software.<

After digitizing past records and new contract proposals, it’s easier to highlight important clauses such as:

  • Liability for damaged goods.
  • Conditions of delivery, e.g. payment after or before delivery.
  • Automatic renewals and contract duration
  • Delegation of authority or responsibility, etc

Through the power of natural language processing, IDP platforms enable procurement officers to go through voluminous legal paperwork involving partners, vendors, and even customers.

Additionally, it becomes easier to identify privacy and compliance concerns.


Brining it all together

So how is artificial intelligence improving supply chain management processes in 2021?

With big data getting bigger each day, supply chain managers are using artificial intelligence software to pierce through the noise and single out the important information.

AI is helping to better assign duties to employees, consequently enabling better management of company workflows.

Crucially, intelligent document processing platforms are making procurement processes easier, not forgetting as well how transport logistics is reaping the benefits of AI as well.

Artificial intelligence is making supply chains all that easier to manage, easing the burden on supply chain managers who have, for a long time, taken the brunt of the work in its absence.

Is AI part of your supply chain workflows?

If not, there are many good reasons that it should.