What is a middle-mile logistics and how to optimize it?

Why care?

“Middle mile” has less buzz than first mile and last mile logistics and is most often considered the part where not much of an optimization can happen. Much of the focus today is on last-mile deliveries and even first-mile services that link vendors and fulfillment centers. So, logistic professionals and distribution practitioners sometimes consider it as a most “boring” part of the supply chain as middle-mile logistics is relatively easy to manage and automate. But is it so?

Middle-mile delivery distances are different from sector to sector. Nevertheless, with the accelerated growth of e-commerce increasing requirements are creating burdens on the logistics chain, including middle-mile. As it is clear from the report represented by Deutsche Bank in 2019, networks are becoming more fragmented supporting the positioning of inventory near the final destination. For example, forced by Amazon’s “Prime” delivery service traditional retailers on average have cut their delivery times by as much as 30 percent in the past few years. We also observe an increase of fulfillment sites near densely populated demand centers. So a shortened and fragmented middle-mile creates uncertainties and retains higher volatility typical to the last mile.

What is “middle-mile” logistics for different sectors?

Lets first define what is the “middle mile”. Now let’s define what is the “middle mile logistics” for different industries?

Wholesale distribution

Wholesale distribution and middle-mile logistics

Wholesale distributors are the middleman between producers and retailers with slight differences from the industry to industry. It is one of the more complex industries driven by high numbers of SKUs, customers, suppliers and transactions, and their pricing and rebate structures. They can be grouped in major ones such as food, beverages, pharmaceutical, electronics, industrial and building materials. According to the National Association of Wholesale Distributors (NAW) total wholesale distribution revenue ended the year at $5.970 trillion, just slightly below the April 2019 record high. GDP for the year totaled a record high $21.734 trillion with a growth rate of 4.0%. The Wholesale Distribution industry is 27.5% of U.S. GDP1. From the total supply chain perspective wholesale distribution is a combination of first and middle-mile deliveries. While a small distributor can work having only one distribution center serving a limited area, large distributors, may have hundreds of distribution centers in the majority of states and Canadian provinces (we have in mind only the domestic part of the supply chain without considering international movements here). We observe changes in the industry in the form of growing concentration as well as the emergence of disruptive business models. For example, Amazon’s sales platform is supported by more than 2 million third-party sellers worldwide. Of those sellers, 26% sell products using a wholesale sales model. So, changes drive growth for some distributors or create significant disruption in the value chain for others. Managing these shifts to ensure sustainable business performance is a priority for wholesale distributors, particularly as new competitors emerge and apply additional pressures.​

Retail distribution

Unlike the wholesale distribution, retail distribution can have distinct first, middle and last miles in their logistic supply chains. While smaller retailers do only the last mile, larger retailers with multiple distribution centers may have all three. So, the middle-mile deliveries for retail businesses are the part of the distribution from DCs to their stores. Retailers may use both their private fleets, 3rd party carriers or the combination of both.
According to the latest report by PwC and the National Retail Federation (NRF) – released in May 2020 – the industry’s total GDP impact was $3.9 trillion, accounting for 18.7 percent of US GDP in 2018. The supermarket & Grocery stores industry grew at a rate of 1%, reaching $654.6 billion in 2019. Speciality Stores such as Home Depot, Best Buy, etc. currently represent 11% of retail sales. According to the latest figures by USDA, grocery stores, including supermarkets and smaller grocery stores (except convenience stores) accounted for the largest share of store sales (92.2 percent), followed by convenience stores without gasoline (4.5 percent). Specialized food stores, including meat and seafood markets, produce markets, retail bakeries, and candy and nut stores, accounted for the remaining 3.3 percent of the total. According to NRF latest data3, the main retailers in US are:

  • Walmart, with a turnover of $ 387.66 billions in retail sales
  • Amazon.com ($120.93 billion)
  • The Kroger Co ($119.70 billion)
  • Costco ($101.43 billion)

Transportation and logistics

U.S. freight transportation in 2018


Third party logistics providers, trucking companies and owner operators serve both retail and wholesale distribution industries. According to the U.S. transportation data the freight transported by private trucks is almost equal to the for-hire sector deliveries.

Many retailers and wholesalers outsource their logistics to 3PLs and 4PLs hence delegating the middle-mile logistics management function. Speed is becoming the main factor in organizing logistics from ports or production sites to distribution centers. Unlike many retailers and wholesalers, 3PLs manage freight for multiple customers hence have wider options in consolidation tactics. They particularly can alternate between full truckload (TL) or Less than truckload (LTL) movements to fasten up the delivery process. While it’s obvious in the case of first and last-mile deliveries, middle-mile delivery optimization is less visible but not a less important and difficult task.


This is a movement of goods from large DC’s to the growing number of smaller fulfillment centers. It is where much of the work occurs that makes logistics run, chiefly, consolidations and de-consolidations. It encompasses a phalanx of large distribution centers used to shift products around the country. It is also where much of the delivery distance is covered. The flagman of middle-mile distribution operationations is apparently Amazon.com Inc. In the upcoming future Amazon plans to open 1,000 small delivery hubs in cities and suburbs all over the U.S., according to people familiar with the plans5. The facilities, which will eventually number about 1,500, will bring products closer to customers, making shopping online about as fast as a quick run to the store. Other large and small companies are also keeping up the pace so the growth of smaller regional fulfillment centers is expected to rise. According to research from commercial real estate firm CBRE Group, in 2019 rents for warehouses between 70,000 and 120,000 square feet rose by 33.7 percent in the past five years. The average price is now $6.67 per square foot. Availability for these smaller warehouses also shrunk from 11.3 percent to 7.4 percent in the same time frame. Despite their help to speed up deliveries, more fulfillment centers mean higher cost and more pressure on making them efficient.

Two big questions of the middle-mile logistics

Are lanes static?

The number one misconception about the middle-mile is that it is “boring” and there is nothing to optimize. As we already pointed out, changing supply chains add volatility and increases the need of applying different strategies and scenarios. Moreover, it is important to deploy efficient strategies and tactics both for long term supply chain and daily delivery planning. For example, middle-mile logistics might alternate between three “legs”:

  • Shipment of high volume items directly to DCs
  • Truckloads of multiple SKUs can be used to reallocate inventories between distribution centers
  • Deliver multiple SKU loads to smaller footprint urban fulfillment centers

The volatile demand and tight delivery windows may force shippers to continuously find balance in the logistic network orchestration. There should be proper tools to do that having in mind multiple optimization goals and constraints.
Retail distributors with multiple DCs also face the same dilemma of efficient logistics planning. Decisions about DC location and configuration change faster than before as retailers should adapt to changing demand. Volatile demand also creates inefficiencies for day to day operational planning. Retailers can deploy the same routes as delivery volumes so the service times vary quite a lot. Some DCs cover quite large areas so there could be 2-3 days multi-stop trips. So retailers should either add smaller DCs closer to their stores or optimize the delivery process to get more work done with the same or fewer resources. As transportation costs can reach up to 50% of total logistics costs, route and vehicle planning can either assure or dry up profits in a low margin environment.

Why visibility matters?

When it comes to visibility, logistic managers usually imply to the consumer that they know where your truck is. While it is good information to have, especially in real time, locating your delivery is one (small) part of the total visibility. It should help to not only track deliveries but also make preventive operational management and long term data analytics and efficiency optimization.
Capturing data: Capturing data is essential as it is created in real time or near time and distributed across different operational decision makers. It particularly includes stops or order level updates of estimated times of arrivals (ETA) at a driver, customer, dispatcher or DC level. Post factum delivery data such as late deliveries and actual service times is also important to capture and store properly. To minimize late deliveries by implementing dynamic routing, dispatchers need timely data that shows ETAs with service times and other variables not being produced by GPS tracking software.
Aggregating data: Capturing and aggregating delivery data per different instances and layers is the key for efficient operational and strategic middle-mile network management. The difficulty of it is that systems across different parts of the logistics chain are captured and stored differently, so it is not always possible to have different levels of data aggregation without a synchronized ecosystem.
Supporting decision-making: Aggregated data is a foundation for retrospective and prospective data analytics. One of the major problems of growingly complex logistic supply chains is the absence of full digitization and synchronization of all the necessary information across different layers of an organization.

How Less Platform helps to plan and orchestrate middle-mile logistics

Unlike last-mile deliveries, middle-mile loads can take several days to deliver, although companies are usually trying to locate their DCs within a daily dispatch limit. It creates problems for automatization of the load building and route planning process as the majority of solutions in the market does only daily planning. Less Platform tuned its route planning and load building algorithms to factor in multi-day multi-stop routing.
The other important feature of Less Platform is the multiple depot or DC planning functionality. It is very convenient for the companies that have centralised operational planning activities. Secondly, it helps to generate data for separate DCs as well as aggregate it on a company level. As we have already mentioned, the number of DCs are increasing and moving closer to customers making both operational and strategic routing harder and potential inefficiencies bigger.

LessPlatform helps to optimize middle-mile logistics

Two important constraints that make it hard to plan are daily delivery windows and variable service times. These two constraints can also drastically affect capacity used and routes planned. The absence of proper planning tools can make a planner work daunting and consume too much time. It is also the main cause why distributors use excessive capacities, drive unnecessary miles and have late deliveries. These effects are being amplified due to increased demand variability, speed of deliveries and tightening delivery windows.

Delivery windows

Automatic routing easies the job but there are plenty more situations requiring manual planning. Less platform provides dynamic planning functional for both pre-dispatch and post-dispatch stages. After getting a route plan, a route planner can reroute different stops by assigning them to different routes both individually and in bulk. Then sequencing algorithms will re-optimize the total plan. After making changes algorithms assess feasibility of delivery windows by checking with recalculated ETAs. All the changes are instantly visible for drivers post-dispatch.

load planning for middle-mile logistics

And finally, planned loads can be scheduled both daily and weekly to respective drivers. Less Platform’s dispatch management software is connected to the driver app so all drivers have planned loads once they are assigned to them.

Schedule the load

After the dispatch, the driver app updates all ETAs and synchronizes them with the dispatch board. Less Platform’s algorithms then check potential late deliveries by comparing ETAs with delivery windows. This way dispatchers can be proactive to avoid late deliveries or dynamically optimize the delivery process otherwise.

Please contact info@lessplatform.com if you want to learn more about Less Platform middle-mile logistics orchestration capabilities.


Vardan Markosyan is the CEO at Less® Platform
MBA from the University of Chicago Booth School of Business
PhD in Economics from the Institute of Economy of NAS RA
He spent decades of research and consultancy on business process optimization and system design

Static vs stochastic multi-stop route planning in distribution

The most common theoretical framework for solving a multi-stop delivery problem from the same location (s) is known as a Vehicle Routing Problem (VRP). One of the specifications of all VRP’s is that they are stochastic (random) in nature, so every time the user runs it, the route plan is going to be randomly compiled. The majority of distribution firms are using routing software or do manual routing using what’s called a “static routes” system. Companies first create “static” routes and then try to squeeze in additional orders or manually reroute those orders with decreased demand on a daily or weekly basis.

This system was created long before the internet and PCs as well. Theoretically, building an optimal route plan for a medium-size distribution company with 1000 deliveries (nodes) is hard. It should be done by solving a Capacitate Vehicle Routing Problem with Time-Windows. This is a time-complex problem. It also requires substantial resources and computational power. While static routes are easier to compile, medium to large companies lose hundreds of thousands and even millions of dollars for underused capacity and overdriven miles.

Pros of static multi-stop route planning

  • Drivers assigned to “static” routes so can more easily get to a location
  • Drivers deal with the same customers. So, they know service times and have other customer-specific information
  • Drivers know rush hours and parking specifications for locations

Cons of static multi-stop route planning

  • Lower truck utilization
  • More miles are driven
  • Harder for planners to plan
  • Less dynamic capabilities

There are a number of obstacles that make the “static routes” approach hard to implement. They also amplify the shortcomings of the approach, making it even more ineffective:

  • Demand is not static for the same customers
  • Orders from new customers never served before
  • No orders from existing customers

Static routes create more inefficiencies during times of fluctuating demand or when the company is at a higher growth stage. It also can create inefficiencies when a company adds more depots or comes out from the M&E process.

Less Platform and stochastic multi-stop route planning

We have created daily route plans using Less® Platform’s weekly sample data and compared it to our customer’s historical delivery data, which used static routes (Figure 1).

less platform chart for multi-stop route planning

Figure 1

We have also implemented tests for other periods and customers. The result is that the Less® Platforms stochastic optimization produced 10-30% less mileage compared to historical data.

Mixed model

The main issue regarding planning in distribution operations is how to find a balanced model when the real-life benefits of the static routing will sustain the status quo, but stochastic optimization will lead to better operational results. We have engineered a model that combines additional constraints to our CVRPTW algorithms.

  • Assigning specific customers to specific drivers
  • Using advanced sequencing algorithms
  • Appling supervised Machine Learning (ML) algorithm to train on driver-specific data

While those constraints reduce results further from the optimality by about 3-7%, they still allow the combination of both worlds and secure hundreds of thousands of saved miles.

Less® Platform can turn your data into loads within 10 minutes and compare it with your historical results! You can also test all the necessary features for your logistic model and consult with our experts on
your challenges.

Vardan Markosyan is the CEO at Less® Platform
MBA from the University of Chicago Booth School of Business
Ph.D. in Economics from the Institute of Economy of NAS RA
He spent decades of research and consultancy on business process optimization and system design

Solving delivery planning problems of asset-based 3pls

We have seen a surge in B2B 3PL and fulfillment operations during the last few years. This trend continues accelerating as e-commerce grows at a steady phase and delivery times shorten. Thus, traditional asset-based logistic providers such as; FedEx Logistics, J. B. Hunt, Hub group, Kenko Logistics, Distribution technology, Atlanta bonded, Cardinal logistics, have to deal with larger volumes and faster deliveries. It should be noted that 3pl’s specializing in B2B distribution usually keep their fleet of trucks in combination with contracted carriers.

Why is this difficult?

Route Planning. Unlike city dispatch and delivery, 3PLs might have a multiday planning horizon from multiple distribution centers (DCs). The asset-based 3PLs main goal is to have the maximum number of orders delivered in the shortest possible time while simultaneously taking into consideration variable distances, delivery windows, and service times of orders. Ideally, companies should be able to reach a maximum loaded mileage to achieve this goal. The best loaded mileage can be reached by;

  1. increasing the loading ratio and/or
  2. decreasing driving time or mileage

Along with the need to consider different delivery days, time windows and service times, reaching the best loaded mileage is becoming a next to impossible task. No solution in the market can compile optimal routes for 1000s orders for multiday horizons with tight delivery windows, variable service, and flexible start times. Less Platform does that in a matter of minutes.

Multi-terminal operations. Large 3PLs usually operate using multiple DCs, so they need to have an option to plan their routes simultaneously. The absence of robust solutions makes 3PLs dedicate more resources than required and create massive inefficiencies. Less® has a multi-depot functionality and a wide range of as-if scenario analysis to plan orders from DCs offering maximum deliveries. 3PLs can even use the tool to plan the next DC location for optimal logistics.

Visibility. Having visibility on potential late deliveries beforehand helps to mitigate their occurrence. This can be done only through maximum visibility. Most companies are connected to ELDs or they use other GPS tracking software these days. While it helps to trace truck locations, it does not allow us to anticipate potential late deliveries at the stop level. The problem is that delivery time equates to driving time and stop level service (or dwell) and other HOS required stops time. Less® works as a sophisticated system by getting information from the driver app, recalculating ETAs, checking feasibilities with delivery windows, and warning about potential late deliveries. This information is visible both to dispatchers and drivers. Companies need to have a unified dashboard of ongoing deliveries and should be able to identify potential risks beforehand. This unified visibility is the key to decreasing late deliveries and increasing customer satisfaction.

Integration with current tech infrastructure. 3PLs use different TMS’, WMS’, and other company-specific ERP software. So any complex solution should be able to integrate with all these systems in a matter of hours. Less® Platform’s API enabled configuration helps connect to ordering, TMS’, and WMS’ even at the SKU level. If 3PLs have their homegrown ERP’s, they still can integrate with less at the engine level. This helps to use the customized interface while still getting all benefits that Less® Platforms route planning engine offers

Reporting and analytics

A well-thought-out reporting and analytics system helps to get sharpen a firm’s competitive edge. Businesses operate in times of growing complexity of operations and an ambiguous external environment. Adding to this growing issue is that there is a considerable lag in terms of current solutions being offered in the market. 3PLs should be able to understand inefficiencies in their distribution operations at corporate, DC, and even dispatcher and driver levels. Due to a current lack of solutions providing proper visibility over analytics, this is not possible at an appreciable level of complexity. Along with performance analytics, accumulated information should be analyzed and turned into insightful forecasts for getting better plans and decreasing uncertainties. Data aggregation also should help to assess the performance of even more complex operations with thousands of trucks and 100s of fulfillment centers.

We tried to consider the major pain points that 3PLs have regarding increasing volumes and incorporate them into an enterprise-grade cloud-native SaaS solution. Please reach us out at sales@lessplatform.com for more info.

Vardan Markosyan is the CEO at Less® Platform
MBA from the University of Chicago Booth School of Business
Ph.D. in Economics from the Institute of Economy of NAS RA
He spent decades of research and consultancy on business process optimization and system design

How can distributors adapt to the post-covid19 environment?

  1. Status of the new normal

The new normal requires firms that want to stay afloat or grow to understand its peculiarities and take action. Low Touch Economy is the new state of our society and economy and has been permanently altered by Covid-19. It is characterized by low-touch interactions, health and safety measures, new human behaviors, and permanent industry shifts. For the distribution industry, it creates both internal and external challenges.  

The most prominent and probably lasting external hardship is the behavioral shift. Particularly, people will know how they interact with each other and businesses. Thus, we can expect lower demand for the entities which are most affected by the behavioral shift such as restaurants, schools, and other places for public gatherings. People are also changing their behaviors relative to how they try and touch things with health and safety issues being paramount.   

Secondly, some of these changes seem like they are here to stay. Thus, for some distributors such as foodservice and apparel, demand shock of this new normal can last longer and changes in demand patterns can be permanent.  

 And lastly, behavioral changes also affect how people co-exist in the workspace and interact with clients. Working from home has particularly become more widespread. Additionally, fieldwork will produce interactions inherently riskier.   

  1. Adapting your organization’s external strategy 

Consider the instability in supply and demand. As the recent meat crisis in the US shows, sudden supply chain disruptions are possible. To mitigate demand shock or even prepare for further growth companies should focus on their competitive position and expand on it by searching for new opportunities or moving into new subsectors. One example could be adopting better e-commerce practices by creating tools to support online purchasing. Improving the interface for online food menus is a good example. Changing product assortment and looking for new verticals is also a viable option.     

  1. Adapting your organization’s internal strategy

a) Taking care of the health and safety of field-service personnel by limiting close interaction with customer representatives and move to electronic document exchange.

b) Change office and warehouse rules. For example; less personal interaction and meetings, office space redesign to lower the proximity between employees.  

c) Adapt new remote managerial and operational technologies. Try to keep as many people working remotely as possible, especially vulnerable groups. It also requires adapting cloudbased load and route planning and delivery execution platforms.  

d) Adapt to new staffing and HR procedures. 

Adapting to the new normal is not going to be an easy task and will require a good amount of brainstorming by everyone. The key is to understand industrial, behavioral, social, and regulatory changes and to be able to mitigate current losses and seize new opportunities.   

How to choose multi-stop load and route planning software?

Its 4 in the afternoon and a load planner is in the office trying to plan the next day’s deliveries. She has one thousand orders to plan for the whole day. First, she needs to keep volumes of nearly 10,000 Stock Keeping Units (SKU) in her head. Then she needs to visualize all 1000 delivery destinations on the map, some of which she has seen for the first time or for occasional clients. Then she needs to memorize delivery windows for the 1000 locations. She knows that timely deliveries are the key to the company’s success, so her main goal is making these 1000 deliveries happen as accurately as possible, otherwise there will be a real-world impact.

So, what’s wrong with this process? Isn’t this how its always been done? Well yes but this job requires hours of focused work for even an experienced planner who has spent years in a firm (imagine what happens if she is inexperienced). Additionally, a planner primarily takes care of the level of service for her clients. What she is not able to focus on to the same degree, or even at all – is the efficiency of her own company’s operations. This results in unnecessary miles driven and inefficient utilization of trucking capacity.        

What is the solution?

Routing optimization. Routing “optimization” is a buzz word used very loosely nowadays. For the majority of cases, people understand it as the sequencing of truck stops to get the best driving time. At most this is a partial automatization of a planner’s work, but it doesn’t mean that the route plan is “optimal”.

The most widespread way of modeling multi-stop delivery operations is by solving the so-called Vehicle Routing Problem (VRP’s). In the majority of cases, the VRP goal is to optimize driven mileage, which, in formal terms, means getting minimum possible mileage for the given plan. Combinatorial optimization is the mathematical apparatus to solve VRPs. There are two major ways to solve this problem – using exact methods or metaheuristics (approximate solutions). The discussion of combinatorial optimization is a topic for another post, so we won’t go deep into it now. What we can say is that the exact methods are limited to 100 nodes (deliveries), hence if one wants to get a plan within a reasonable time the use of some kind of metaheuristic is absolutely necessary. A consequence of this process is that no solution can get an “optimal” result but rather should try to get as close to optimum as possible. For example, if the theoretical minimum for the mentioned 1000 deliveries is 10000 miles with 40 trucks, different algorithms will certainly get more miles than that (could be both 11000 and 14000). In fact, as no one knows what the theoretical optimum is, she can’t realistically assess the quality of the solution through a comparative process. In our experience, manual planners get somewhere between 60-80% to optimal. So, what is the best way to assess how good the solution is? We must compare the firm’s historical data (if that exists) or run it in parallel with manual planning to see the difference in optimization rates. As an industry standard, good routing solutions should give results close to 95-97% to optimal.

While there are a number of solutions for coping with this problem producing varying levels of success, there are huge differences both in (a) optimization quality and (b) application usability. Some of the distinctive characteristics of a “good” application are described below.

1. Complexity. The main problem of VRP is time complexity or, in other words, the fact that the amount of time needed to solve the problem grows exponentially when the number of deliveries or nodes increases or when the algorithm incorporates more constraints. To give you an idea of the problem encountered when scaling up nodes/deliveries consider that solving a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) for 100 nodes takes about 10 minutes using current commercial optimization solvers but takes a whopping 8 hours for 300 nodes. Why, because solving for optimization goals for the first set of nodes takes tens of millions of permutations and for the latter – hundreds of millions.

Below are a few more examples of variables dramatically adding to the numbers of permutations and thus increasing the time for a potential optimal solution.

1.1. Time complexity rises when an optimization algorithm tries to incorporate more real-life data whose absence would massively degrade the usability of the solution. A primary example is that many firms have a variety of trucks and equipment. An average firm can use 4-8 types of trucks with different capacities. The majority of solutions either don’t include the capacity constraint into calculations or are using just one capacity constraint.

1.2. Delivery windows is another variable that adds complexity to the problem. The planner should make sure that capacity is maximally leveraged while ensuring ETA’s are within delivery windows.

1.3. Service requirements are different for different clients. Dwell or loading/unloading time can vary for each of these 400 deliveries and the optimization algorithm should be able to incorporate it into calculations.

1.4. Priority orders. There are orders which should be prioritized over the others. Thus, the optimization algorithm should be able to keep strict delivery requirements for the high priority deliveries while still assuring the best optimization goals.

1.5. ELD’s (Electronic Logging Device) are mandatory nowadays and Hours of Service (HOS) requirements should be considered from the planning phase. This should go as a constraint variable into the algorithms as well.         

Industry constraints that need to be considered when examining the role of optimized planning:

2. Product data availability. An average distributor operates with 1000’s SKUs and a large distributor with 100’s of thousands. Each SKU has a different volume and weight characteristics which should be taken into account when planning deliveries. Not all applications incorporate product-specific accurate data into calculations or use capacity as a constraint at all. This results in a lot of capacity violations and too many routes becoming obsolete when it comes to filling and dispatch. There is also a highly variable human factor in play because many companies don’t have all the needed information in their Warehouse Management Systems (WMS). As a result, algorithms should be able to work with incomplete data. Needless to say, the software should be able to seamlessly integrate with a firm’s order and warehouse management systems or other Enterprise Resource Planning (ERP) modules

3. Remote work capacity. As the routing problem is complex and requires lots of data and computational resources most of the routing applications available on the market should be installed on-premise. Conversely, it is becoming imperative that people who are not in the field (and even people who are in the field) should have access to multi-user plug and work opportunities. This creates a high demand situation requiring a novel solution. The routing solution should offer robust multi-user functionality. At the same time, it should be able to process billions of combinatorial permutations in seconds. An advanced enterprise-grade cloud-native architecture is needed, otherwise, the firm will be creating additional problems rather than solving old ones.        

4. Big data. Imagine what happens when there are not one but 10’s of planners in the firm who plan data for multiple warehouses and distribution centers. An optimization solution requires multiple installations in different warehouses resulting in massive investments and further support cost. Even without that, the majority of solution applications in the market are limited to planning 500 simultaneous deliveries and are only suitable for small firms. One can plan routes in batches but that will substantially degrade the quality of optimization for the total plan. Growing and competitive firms should have the opportunity to run and compare multiple instances simultaneously, plan tens of thousands of deliveries, and have other collaborative functionality which is only possible with the enterprise-grade cloud-native architecture. 

5. Additional features. There are a number of additional features that should be embedded in any forward-looking solution such as a) data interoperability b) creation of dispatch documentation c) monitoring dashboard e) functional analytics f) dynamic planning, etc. We will return to these topics in other blog posts.        

Current Solutions in the Market 

There are different solutions in the market which help to plan loads and dispatch them. We will list some of them and group them following the descriptions copied from their websites.


Mapping applications offer route optimization, which is limited to getting the best sequence for the given data points. The number of points is limited to 10-20 points. Apparently, maps don’t do VRP optimization for multi-stop deliveries. Maps won’t provide any product and shipment management functional either. Routing functionality would be a good addition for personal or occasional usage.  

Google Maps

Bing Maps


Here maps  

Free routing software



Free applications are one step ahead of current maps routing functions. They can be used by small firms, but it is not clear if these applications use any kind of mathematical optimization. They apparently miss the majority of the ideal functionality previously touched on. 

Paid routing software





All paid solutions state that they have some optimization algorithms under the hood. Routific and OptimoRoute have straightforward pricing while Route4Me has a relatively low base price but additionally charges for different features. Workwave has several products for different industries. All these solutions incorporate delivery windows in the planning and build multi-stop loads. 

In choosing the most suitable software one should consider a couple of other factors 

First, one should look deeper into what has been optimized and how good it actually is? As we have noted above, results can be different (sometimes substantially) depending on the types of algorithms and optimization methodology used. The best way to compare is either to test the same dataset using different applications or compare results with the optimal values for that dataset. 

Second, does software optimize the delivery plan for the day or just updates sequences on fixed routes? Running CVRPTW is hard and requires substantial resources and appropriate algorithms, so many applications just help you to build a route plan once, without taking into consideration specific order details and location time windows. You would assign orders manually afterward by figuring out sequences. Later is a much simpler approach but it substantially reduces optimality. I.e., Less® Platform runs CVRPTW for every group of orders from the scratch, thus assuring the most optimal route plan for that specific order list.

Third, data requirements for foodservice wholesale distributor and cleaning services, for example, can be substantially different. It is not the same to run the Capacitated VRP optimization for the several thousand different SKUs and simple stops routing. Wholesale distribution, trucking, and logistics management companies need a comprehensive product and freight management function. These industries also combine inbound and outbound operations, can have simultaneous pick-ups and deliveries and offer multi-depot operations. 

Forth, the capacity of the platform for handling the number of deliveries should be appropriate to the size of the company. Due to the time complexity of the problem, larger companies should pick an application (more than 300-500 deliveries a day), which is capable to work with large data sets without compromising the solution quality.        


Transportation, distribution, and logistics management firms operate in a highly competitive and increasingly uncertain environment where margins are tiny and even a small disadvantage will keep the firm out of the highest levels of competition. Delivery planning optimization sometimes lags behind other, seemingly more important operations when it comes to decision making. Crucially, this is where your short-term profitability and long-term efficiency rests. The cost of choosing a poor solution will be hundreds and millions of dollars. We have seen many cases where suboptimal planning and poor execution (which are highly interconnected) sink both growing and established firms.

Less® Platform can turn your data into loads within 10 minutes and compare it with your historical results! You can also test all the necessary features for your distribution model and consult with our experts on your challenges.

Vardan Markosyan is the CEO at Less® Platform
MBA from the University of Chicago Booth School of Business
PhD in Economics from the Institute of Economy of NAS RA
He spent decades of research and consultancy on business process optimization and system design