Automation and artificial intelligence is estimated to result to $2.2 trillion of economic growth available for Australia in the next 15 years.
But for artificial intelligence to really work for your business, you need data (lots of it), people with AI expertise (expensive to hire or train) and infrastructure (for data collection, processing and analysis, robotics and sensors).
It’s a huge investment upfront and the possible ROI is still unclear. Aside from the hype (e.g. AI will solve all problems), the real use of AI in business (particularly in warehouse management and logistics) is still unclear. You might have heard that artificial intelligence is already disrupting many industries (e.g. self-driving cars, image recognition, fraud detection, real-time product recommendation). But how does AI really affect warehouse management?
Optimisation, prediction & automation
These are the most common uses for AI. For optimisation, AI systems will use historical data to spot opportunities for better inventory and distribution efficiency. For prediction, again this uses data so you can have a more accurate demand forecast (and then react accordingly). And finally, when it comes to automation you also need data to automate operations and workflows.
In most cases it’s only a partial implementation of AI because you’ll still require human expertise to supervise the system (and perhaps diagnose the errors and apply preventive measures). Also, internal data will most likely be used to train the algorithm (more on this later). If you have tons of high-quality data, there’s a good chance that the algorithm will perform well.
So how does AI really work? This is important to understand first before investing significant resources in the area. Let’s then discuss how it works and how does it apply for warehouse management and other business operations.
AI depends on data
It’s a bit similar to humans wherein algorithms (fancy name for processes or set of rules, how things are done) learn from experience (in the case of AI, it’s learning from data).
For example, we want to train our algorithm or system to recognise handwritten digits. First, we need to create or collect labelled data (handwritten digit and its label 0 to 9). Then our system will learn from those labelled data or training examples. Our system will then learn from those examples and automatically infer rules and patterns for recognising handwritten digits.
It’s like showing enough examples to someone until he/she gets it. It’s a simplified explanation but basically it’s how it works (minus the complex mathematics and algorithms involved). As a consequence, the performance of our AI system heavily depends on the availability of data (the training examples) and its quality.
Where will these data come from?
It’s either from an external or internal data source. Perhaps the vendor already acquired data from other clients in the manufacturing and logistics industry. Or, all the data will come from your operations.
How will then this data be used? As discussed earlier, learning comes from data (seeing enough examples to infer rules and patterns). Suppose you have a comprehensive record of forklift breakdowns. You have the number of hours or days of active operation for each and when exactly each incidence occurred. You also have complete information about several parameters such as fluid pressure, speed, temperature, lift count and travel time.
With implementation of artificial intelligence (machine learning specifically), we will have a system that can predict when forklifts will break down. Perhaps there are certain levels of speed and temperature that lead to forklift failure. Or, there are certain numbers of operating hours and/or days that lead to breakdowns.
An effective AI system can detect consistent patterns that lead to equipment breakdowns. This then results to predictive maintenance that actually prevents those breakdowns in the first place. Instead of waiting for the next maintenance schedule, you’ll receive a notification that it’s now best to inspect the forklift and perform the necessary maintenance. The AI system can also be very helpful in prescribing repair solutions in real time (instead of lengthy inspections that lead to a longer downtime).
We saw here how optimisation, prediction and automation are being used to make your warehouse operations more productive. The operation and possible useful lifespan of the forklifts will be optimised due to proactive maintenance. If we’re able to predict when equipment will break down, we can be more proactive in maintenance and repairs. This then leads to fewer downtimes and significant cost savings.
Speed & cost savings
In almost any business the trend is to go faster and at lower costs. Many industries and companies even go a step further by going real-time instead of just going faster. All the data should be available real-time and the actions should also be done in a much faster or automated way.
Artificial intelligence might help in realising higher speed and better efficiencies in warehouse operations. But even without adoption and implementation of AI, there will always be a pressure to achieve higher speeds and better efficiencies. There will always be a demand for new innovations to stay ahead of the competition.
With or without AI, the management always requires sufficient and timely information for better and proactive decision making. This is also required to stay ahead of the competition and attain better efficiencies.
To accomplish this, many managers have invested in fleet management solutions that allow for effective data collection, transmission and analysis. In addition, with detailed reports and graphs (e.g. EquipCommand™ Software), engineers can better communicate the data and analyse those to spot opportunities for reducing costs and improving safety. The program can also be used to transmit notices and alerts to the forklift drivers in real-time.
The key here is to have access to data (whether you implemented AI or not) and then use that to make faster and more accurate decisions. Even in heavy and traditional industries such as construction, agriculture and mining, the trend is to become a data-driven company. Each decision should be based on data so the company heads to the right direction.
Is artificial intelligence for warehouse management?
In the near future there will be a more widespread adoption of AI and robotics in many different industries. It’s inevitable especially if it clearly results to higher cost savings and better efficiencies in inventory and distribution in the manufacturing and logistics industries.
Right now the costs upfront might be too high and the benefits might not be still justified if you decide to adopt AI in your warehouse management. After all, AI is still in its infancy and much of what you read in other online sources is still hype. Vendors might claim that they use AI in their software but the truth is they’re just using better visuals and analytics. Due to the hype and lack of understanding about AI, vendors might take advantage of it to sell expensive “AI solutions” to engineers and managers. Your company might even be the “guinea pig” because the vendor doesn’t have proven results yet.
That’s why it’s always recommended to stick with the basics and optimise current opportunities first. With accurate and timely information in your hands, you can improve operations of your forklifts and management of your warehouse. With data, you can readily detect inefficiencies and spot opportunities that result to significant cost savings.
With or without AI you can realise cost savings right now through better planning. To accomplish that, you need access to accurate information. For example, EquipManager is a wireless fleet management system that ensures OH&S compliance, controls access, detects impacts, and monitors equipment operation to improve safety while reducing damage-related costs. Aside from improving safety, this also helps in minimising downtimes and maximising usage of material handling equipment.
Here at ShockWatch, we’re committed to delivering real fleet management solutions to warehouses. For 40+ years, we’ve been delivering cost-effective solutions to small businesses and multi-national companies — including two-thirds of the Fortune 100 and over half of the Fortune 1000.
Aside from the EquipManager that improve operations, we also have accident prevention solutions to minimise the number of collisions (forklift vs forklift, forklift vs people). Our proximity warning systems have perhaps prevented countless number of accidents in small businesses and multi-national companies.
Contact us today if you require such solutions for your warehouse operations and management. With our modern solutions you can improve safety and efficiencies in your warehouse and forklift operations. Send us an enquiry and our technical staff will prepare a customised recommendation for your company.