Modern business logistics is a complex field. It ensures that products are produced, stored, and delivered as efficiently and inexpensively as possible — making it crucial to any company’s bottom line. Coordinating these many areas of operation means that logistics management must take an astronomical number of variables into account. Without cutting-edge logistics technology, companies can hardly remain competitive in today’s market.
What exactly is modern logistics?
Over the years, logistics has been defined in many different ways. Currently, the Council of Supply Chain Management Professionals (CSCMP) defines logistics management as “…that part of supply chain management that plans, implements, and controls the efficient, effective forward and reverse flow and storage of goods, services and related information between the point of origin and the point of consumption in order to meet customers’ requirements.”
Most significantly, they point out that: “Logistics management is an integrating function, which coordinates and optimizes all logistics activities, as well as integrates logistics activities with other functions including marketing, sales manufacturing, finance, and information technology.”
So coordination is the essence of logistics — but it hasn’t always been that way.
The development of modern logistics
Until around the 1950s, the many different functions that logistics is now involved in — warehousing, production, shipping, finance, and more — were viewed as separate areas of business operations. Logistics was seen as simply a matter of transporting goods from one place to another. It hadn’t begone to coordinate the other areas of business. And that meant that businesses overlooked key tradeoffs and decision-making factors that could have drastically improved the bottom line. Logistics costs were astronomical.
Beginning in the 1950s in the US, analysts began to understand how the interplay of the different factors affected overall costs, leading to the development of the modern field of logistics. Modern logistics recognizes the many interconnections and tradeoffs along the logistics chain and seeks to exploit them as effectively as possible. For example, there are times when a faster, more expensive shipping option might reduce inventory carrying costs, leading to lower spending overall.
Since the 1950s, analysts have developed increasingly sophisticated methods for calculating the lowest overall costs in any given logistics situation. The recent introduction of artificial intelligence and machine learning is allowing those calculations to become ever more sophisticated.
As artificial intelligence gains traction in logistics—and in related areas of operation such as warehousing and production—it will offer even more opportunities to improve efficiency, cut costs, and increase overall reliability. And since these factors all contribute directly to a company’s bottom line, AI-powered logistics management can be expected to drive success across practically all market sectors.
What variables does logistics currently work with?
The overarching goal of logistics is to get the right product to the right customer at the right time — at the lowest possible (total) cost. Given that the total cost must factor in not only warehousing and transportation but also purchasing and production, there are a huge number of variables that must all be considered. According to Logistics Management by Shailendra K. Singh, Subhash C. Kundu, Shoba Sing, some of the most important variables include:
- Inventory Reduction: A company generally seeks to minimize the total inventory it has on hand, as this reduces the carrying costs and frees capital to be invested elsewhere. However, there must be enough inventory on hand to meet customer demand or sales and customers could be lost. Additionally, potential cost benefits from larger production volumes must also be factored in.
- Minimum Variance: “Variance is any unexpected event that disrupts system performance. Whether the variance results from order cycle uncertainty, an unexpected disruption in manufacturing, goods arriving damaged, or delivery to an incorrect location, the result is unexpected variance that must be accommodated. […] To the extent that variances can be minimized, logistical productivity will be improved.”
- Reducing transportation costs: This can apply to both inbound and outbound shipments, and often involves consolidating shipments to the greatest extent possible. Consolidation can sometimes conflict with the demand for inventory reduction, however, so it is important to look at the total cost for both inventory carrying and transportation.
Return transportation can also be a significant cost factor, especially as many companies add packaging or battery recycling to the after-sales services they provide. According to Singh, et al. “…innovative programs to assist grouping small shipments into consolidated movements must be incorporated in logistical system design.” As we will see later, this is the perfect type of complex task for artificial intelligence and machine learning.
How AI is changing logistics management
Artificial intelligence allows logistics management systems to perform their integrative and coordinating functions at an unprecedented level. We already know that production, warehousing, financing, and delivery are all interrelated factors. Cutting costs in one area will affect the others, meaning that calculations must factor in the tradeoffs between the variables. Precisely that is the challenge of modern logistics.
AI algorithms take this to the next level. They make possible complex analyses that incorporate larger numbers of variables than humans would be capable of handling. And, although it is a complex task for humans, AI could easily find the “sweet spot” at which production levels, warehousing costs, and distribution speed and cost are all optimized. It could even do so for multiple products with multiple production processes — for multiple seasonal sales periods.
But that’s just the beginning. The previous example involves established variables: production, warehousing, finance, and delivery. Machine learning algorithms, however, can be used to identify variables that would otherwise go unrecognized. They do this by analyzing massive volumes of data and finding patterns and relationships that would remain buried using any other means of analysis.
Because logistics is responsible for coordinating and optimizing so many different areas of business, machine learning has incredible potential to unlock new factors that companies can leverage for cost savings.
What if a 1-degree temperature change in warehouse storage temperature affected not only the heating/cooling costs but also the number of returns for a certain product? Or if a temporary 2% decrease in inventory levels two years ago had led to a faster pick/pack rate in the fulfillment center?
Which recurrent events within the company, the warehouse, or the pick/pack process affect overall costs? With machine learning, companies can identify these types of incidental effects and capitalize on them — and this potential will only increase as logistics management becomes more networked and feeds more data into the algorithms.
What autonomous systems could offer across the logistics chain
Autonomous, artificially intelligent systems could be used in various capacities along the whole logistics chain, from smart, networked warehouses, to fleet maintenance and autonomous robots, to automated logistics processes and shipment optimization.
In a smart, networked warehouse, autonomous AI systems can automate warehouse logistics and improve efficiency during order fulfillment. Here are just a few of the possible uses for an AI-based system:
- Path optimization: an autonomous fulfillment system could automatically identify the most efficient storage location for each item, based on its order frequency and other relevant factors, as well as dynamically determining when to relocate items. These calculations could even factor in the relocation workload. That would ensure that the item would only be scheduled for relocation if this would result in a net savings in working hours. Such a system could also create pick/pack lists that are dynamically optimized for efficiency, or even map out the most efficient route for the picker to take through the warehouse.
- Augmented reality pick/pack assistance: an AR headset with an autonomous AI assistant could guide warehouse employees to the appropriate warehouse location and identify and highlight the required item on the shelf. This would allow pickers to work more efficiently while simultaneously reducing errors.
- Autonomous warehouse robots: various types of autonomous warehouse robots can be used to speed up work and improve safety. These range from picking robots that reduce foot traffic in the warehouse to self-driving forklifts, and on to inventory control robots that can check and optimize stock levels quickly and reliably. Depending on how advanced the warehouse’s AI system is, the robots could be controlled by warehouse managers or autonomously by an Intelligent Virtual Entity (IVE). And an intelligent system could even optimize the vehicle’s paths to maximize fuel efficiency.
AI-assisted fleet maintenance
An autonomous, artificially intelligent vehicle management system could make fleet management, maintenance tasks, and vehicle operation more efficient than ever before.
- Scheduled maintenance: prescriptive vehicle maintenance can be scheduled during off-peak times and coordinated with other similar vehicles, and the AI system could even automatically order replacement parts in advance. This allows regular maintenance to be carried out as non-disruptively as possible.
- Predictive maintenance: With a more advanced AI system, algorithms could analyze historical data and current vehicle data to predict device failure before it occurs. With predictive maintenance, the algorithms can schedule repairs when it is least disruptive — before the situation becomes urgent. As with prescriptive maintenance, replacement parts could be ordered automatically before the service appointment.
- Optimized electric vehicle/robot charging: an AI-powered system could monitor the battery charge levels of electric vehicles or robots. By coordinating the charging schedules, the maximum number of units could be made available during peak operating hours.
AI-assisted back-office logistics processes:
Many repetitive logistics tasks are perfect candidates for robotic process automation (RPA). RPA robots “live” inside a company’s software systems and can be used to automate data processing tasks, especially those that involve transferring data from one software to another. Some possible tasks could include:
- Shipment scheduling and tracking: these tasks involve a great deal of repetitive data input. Information frequently needs to be extracted from carrier invoices or bills of lading and entered into the company’s internal system, a task that is time-consuming and prone to error. RPA bots can easily do automate tasks like these — and an advanced RPA bot could even extract data from handwritten labels.RPA systems could automatically check the shipping status and tracking data from multiple carriers, then update the information in the logistics management system. This would allow both human employees and AI planning algorithms to access the information from a central location.In a more sophisticated setup, an AI-powered RPA bot could even use natural language processing to send and read emails. This would allow it to email shipping partners to schedule shipments, update the internal system when a shipment date is confirmed, and notify the appropriate employees.
- Shipment consolidation: an advanced AI system with planning and decision-making capabilities could be trained to consolidate and schedule shipments to reduce overall transportation costs. With machine learning, its ability to do this task would even improve over time. An AI system could also analyze the data to determine whether it would be more cost-effective to send the shipment to a local, intermediate warehouse or to ship directly from the main warehouse to the final destinations.
Next-level AI technology: predictive/anticipatory logistics
Predicting demand is an essential part of any business. It offers the unparalleled potential to optimize processes, react promptly, and ultimately cut costs. Machine learning and predictive algorithms are opening up a whole new world of possibility in this field.
Predictive analysis and machine learning allow companies to analyze past data in unprecedented detail. As the algorithms identify patterns, they can compare them to current data and use the results to predict future events. These predictions could include things like changes to demand or potential disruptions to the supply chain.
For example, machine learning can predict the likelihood of a vehicle or machine malfunction — but it could also predict things like seasonal waves of illness that could impact staffing levels. This information can help logistics teams avoid disruptions of all different kinds—no matter whether human or machine.
Because the areas of logistics are closely intertwined, the results of a single predictive analysis can benefit the entire logistics chain. Preventing a disruption in warehousing could keep transportation running smoothly. And the results are even more dramatic for demand predictions.
If an algorithm predicts an increase or decrease in demand, that finding can be cross-checked with other logistics variables, such as warehousing costs and volume discounts. The AI algorithms can then calculate the production levels that would result in the lowest overall costs. This ability is exactly what makes artificial intelligence so promising — not only can new developments be predicted more accurately, but those predictions be used more effectively than ever.
In fact, these types of insights could improve efficiency along the entire supply chain. If an increase in demand is predicted, manufacturers can ramp up production, transport businesses can ensure adequate capacity, and retailers can order and store larger stock volumes in advance. They could even adjust their personnel planning in response to the predictions.
Machine learning, predictive analytics, and artificial intelligence are bringing exciting changes to the world of business, and to logistics in particular. Because it coordinates such a wide range of interconnected activities, the field of business logistics is in a unique position to benefit from the advances offered by AI and machine learning. AI developers like those at Blackzendo are excited to be pushing the envelope of possibility for this and many other areas of business.