There has been a lot of talk about machine learning in logistics management. The idea is simple: optimize, infer, implement and repeat. Here is: machine learning and exception management — a logistics tech game-changer.

What is included in the different pillars of logistics management?

A system optimizes the different pillars of logistics management that include order planning; vendor performance management; fleet capacity optimization (management); dispatch management; in-transit shipment tracking; and delivery management.

Next, the system infers the points or bottlenecks within these pillars (logistical processes) which can be fixed, improved, or enhanced. These inferences or analytics are then ‘implemented’ back into the logistics set-up. The learning mechanics start back from optimization. Over-time the system evolves and improves all the connected logistics management processes. This is machine learning in logistics management.

What is exception management in logistics?

A logistics exception (issue) is a deviation from planned or expected process execution. Here are a few examples.

• Shipment loads aren’t mapped properly to available fleet options (creating capacity-mismatches and loading/dispatch delays).
• In-transit shipments are detained at a spot for more than two hours (or are violating service level agreements with speeding or harsh braking).
• Consignees didn’t receive all the SKUs (stock-keeping units) as per the initial purchase order.

Every transportation management system (TMS) involves some or many human touchpoints. A person supervises these system or process interactions (touchpoints). This can be anything from checking the shipment assignment schedule and ensuring that the handlers are following the planned loading patterns. Similarly, many other touchpoints work to ensure that the gap between plans and ‘actuals’ is minimal.

The goal of exception management is to minimize this gap between planned and on-ground results. Overall, the machine-learning aspect of exception management induces accountability and efficiency within the company’s and logistics network’s culture. This can be with the supervisors, warehouses, freight forwarders, logistics service providers, consignees (distribution points), etc.

6-stages of machine-learning enabled exception management system.

The 6 stages are Discovery, Analysis, Assignment, Resolution, Records, and Escalation.

Discovery:

It detects and reports issues or anomalies within the processes. This can be through temperature sensors (cold-chain logistics), real-time movement tracking, order journey tracking (in-scan and out-scan of each SKU), etc.

Analysis:

It analyses and processes the issue or exception as per protocols (or learnings). It categorizes and pushes ahead all exceptions – either to an assignment or to an escalation.

Assignment:

It matches the exception with the right person or department (best-suited to resolve the exception on time).

Resolution:

It tracks the speed and effectiveness of the person’s (assignee) resolution. It moves the ‘resolution’ through multiple criteria and validations before satisfactory ‘completion’.

Records:

It records and analyses each exception right from discovery to resolution. The system processes these records to throw-up insights or best-practices for future applications.

Escalation:

This is an important aspect of dynamic exception management. The system constantly tracks each issue within the system.

• If at the analysis or resolution stage, the supervisor (or system) deems the issue – critical or complicated, then it’s escalated through special ‘analysis’ and resolution. It mostly includes people with different skill-sets or authority.
• If the system detects that an issue hasn’t been resolved in its time-frame, it’s again escalated.

Through these 6-stages, the system constantly weeds-out inefficiencies from within itself. It helps propagate a more transparent, accountable, agile, and responsive culture. Furthermore, it helps reduce errors and delays, which, in turn, improves profit margins. A few new-age TMS start-ups, like Fretron, are trying to capture market share using this 6-stage exception management.

Real-world applications of escalation management in logistics

Let’s consider a real-life use-case for an exception management system (EMS) – a fast-growing retailer in India focusing on Tier-2 and Tier-3 cities.

Their biggest challenge was an unorganized logistics (vendor/freight forwarder) network and weak city infrastructure. Even though the retailer had opted-in for total logistics automation, they still weren’t able to implement it to the full extent. The client was looking for a tech-enabled process and culture change.

Let’s take vendor performance management as an example.

• The EMS helped cut down discrepancies in billing and settlements. A single synchronized TMS was able to track each order (at the SKU level) as it moved through crates, pallets, trucks, cross-dockings, and final delivery. The out-scan could automatically highlight all the missing items.
• The EMS would process the information and mark the exact point of deviation where the item went missing. This helped with issue resolution and also to plug these operational gaps. It cut down invoice-level disputes and hastened the settlements.
• The EMS enabled fast and error-free invoicing which incentivized the carriers and freight forwarders to work in a more organized fashion. Through an iterative learning process, the system improved upon itself. It brought a higher degree of transparency and accountability within the logistics ranks (in the company).
• On the back of machine learning-enabled EMS, the company was able to deliver on-time value (better shelf choices) for its end consumers.

Conclusion: Exception management, in logistics, is a game-changer

EMS successfully bridges the gap between tech-induced efficiency and on-ground employee efficiencies. It’s especially effective in unorganized or traditional markets that are riddled with such ‘exceptions.’

If machine-learning backed EMS is used in the right manner, many mid-level companies can scale fast and improve their outlook within the next five years. At this time of COVID-19, scaling faster may be the only option to save your company.

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If we look back on the past five years, we would find many breath-taking tech advancements. Smart cities, micro-drones, Internet of Things, connected logistics, artificial intelligence, etc. have put us on a platform where pride comes naturally. We can talk about the coronavirus pandemic and lockdowns all we want. However, we shouldn’t forget one thing. Technology has empowered us with numerous advantages to fight this crisis.

We are in this together. It’s not just a statement, it’s a fact. Let’s pause for a minute and evaluate the ‘tomorrow’ beyond the current pandemic. When we do get through this tunnel, we should endeavor to build a better and more connected world together.

In this article, we would talk about: why this is important; what we can improve; and how we should go about doing it.

How global cities measure up during this pandemic.

We kept speaking about disruption and innovation for years. The starting point for both was ‘need identification’. Where are the gaps, and why aren’t they being filled? Let’s look at some of these points.

1. Information overload with minimal applications.

The world has been consuming information and data at an extravagant rate. Hence, the current slowdown (in all news other than the pandemic) has pushed people into consuming coronavirus updates like candy. This, inevitably, pushes us towards information overload.

This has led to misinterpretations, misinformation, and misrepresentations. In short, there’s too much information that doesn’t lead anywhere. It doesn’t have any application or doesn’t solve anything. It’s just noise.

2. Multiple labor and logistics bottlenecks.

Lockdown and travel restrictions have prioritized essential services (medical, food supply, utilities, etc.) over all other goods or people’s movement. This sounds good on paper. However, there have been problems with getting the right people and the right supplies to where it’s most needed.

Some cities and states have shut down their borders. Shutting down borders further hinders the smooth logistics movement. There are (understandably) multiple checks and balances. Checks and balances, also delay the delivery and availability of essential goods and services.

3. Longer reaction times due to inertia.

The above points (and many intermediate ones) culminate into longer reaction times. The authorities get stuck with firefighting and decision inertia. These decisions, in turn, lead to food and supply insecurities among the masses, especially the poor. Another point of worry with decision inertia is the inadequate or delayed testing, reporting, and isolation of possible infections.

The Smart City Approach: What it means and how it can help

Let us now develop a scientific approach towards the crisis management. What are the facts and how do we proceed? The underlying, and inherent benefits of smart cities, here, turn into essential tools.

The ‘Smart City Approach’ is when authorities within the city leverage real-time insights and updates. With this, they streamline their crisis response, plan for process improvements, and ensure seamless logistics.

1. Detection and reporting: Fast and decisive action.

We need better ways to detect and report possible Covid-19 cases. Moreover, we also need better reporting for other emergencies relating to the operations and governance of major cities. Cities like New York, London, Singapore, and Mumbai have been global travel hubs for decades. These globally-connected cities need faster case (or emergency) detection and reporting.

2. A collaborative approach from cities, states, and nations.

Cities and countries need to be on the same page with the fight against this unprecedented crisis. We have discovered common threads of empathy and compassion that have connected us all, across demographics. We want this to continue beyond the pandemic. The connectivity and collaboration will help us recover, as companies and cities, faster.

3. Stakeholder mapping and planning.

The crisis has thrown many curveballs. It seems like when cities solve one issue, another rears its head. There are migrant workers and homeless people who stand to be worst affected by the lockdowns. The people on the frontline of the crisis: the doctors, nurses, police, utility, and sanitation personnel, etc. need more attention. They need to be protected with the timely deliveries of necessary equipment and supplies.

4. Real-time tracking and information.

Overall, the cities need to function as one. Each development, each shortage, each victory needs to be tracked, recorded, and utilized. Smart cities cover all these aspects with aplomb. These cities have the power to handle live information and turn them into key insights.

The Application: How smart cities can be better equipped.

We have talked about improving the connectivity and conditions of our cities by turning them into truly ‘smart cities’. The current crisis is unprecedented in every way. We are at a point where we need to minimize the damage, protect the assets (people and economies), and revitalize our operations (in cities).

We must learn and adapt using the intrinsic smart city concepts to better equip all cities.

1. Drones as a catalyst for better visibility.

Many cities in China, the United States, India, etc. are either planning to use small or micro-drones or are already using them. These drones primarily help:

• gather intel with regular lockdown surveillance (spotting suspicious activities or simply supervising local operations);
• spray disinfectant in areas with active Covid-19 cases (reported);
• detect elevated body temperatures of random people on the streets (used in Hunan, China);
• deliver essential medical supplies (especially masks or sanitizers), etc.

Drones can help local city authorities build situational awareness. The mayor or emergency service officials can view live video updates of sensitive (quarantined) zones without putting on-ground forces in harm’s way.

All information is encrypted and transmitted back to a central repository for current or future use. The drones also ease the pressure on the overworked essential forces within the city.

2. Connected logistics and contactless deliveries.

People under different levels of lockdowns depend on their local stores for their groceries and essentials. Hopefully, we are past the initial frenzy and panic buying. Now, the essential goods and equipment must move seamlessly within and outside the cities.

Many retailers and e-commerce players within major cities have, previously, reaped the benefits of a real-time package and shipment tracking. They used route-planning software to identify short and ideal travel routes. This gave them the cumulative benefits of speed, cost, and end-customer satisfaction.

We need to extend this AI-enabled route planning and live shipment tracking to ensure that all essentials are evenly distributed across districts. Retailers and foodservice providers, along with the local authorities, should engage with this technology as equal collaborators. We will cover more on this in the next point.

People have been confined to their homes and have become more and more dependent on hyperlocal deliveries of food and groceries. Delivery service companies have stepped up their response with contactless deliveries where prepaid orders can be left at the doorstep.

The entire ‘proof of delivery’ is conducted in-app. The delivery person sends the receiver a photo of the package (at the receiver’s doorstep). The receiver, then, confirms the delivery. The network interconnectivity within smart cities is the linchpin for such sharp adaptability within delivery companies.

3. Centralized city info-system for essential services.

Local and central governments are keeping a close eye on all relevant developments within cities (and the nation as a whole). Smart cities have been historically more adept at collating and pushing actionable insights. These insights have been critical in effective decision making.

The authorities, now, must scale and interconnect all related services in the city into one central info-system. It will:

• give the exact cluster map of active and suspect Covid-19 cases (with live health and status updates)
• show live drone-surveillance through key-areas giving real-time intel about select or sensitive areas
• enable retailers and suppliers to update their stock-levels as they depreciate in real-time
  • This will trigger automated delivery requests.
  • The authorities can track delivery trucks in real-time, through in-vehicle trackers, and direct them to the shops with lower supplies.
• help plan and execute structured disinfecting of different areas in the city
• ensure proper law and sanitation upkeep through automated and regular drone-surveillance
• ensure on-time food packet deliveries to the elderly, homeless, and needy (this can be enabled through interconnected community-based reporting)

A connected and centralized info-system ensures effortless smart city management. This leaves the authorities with enough insights (as they study cluster patterns) to tackle and overcome the pandemic.

Beyond the crisis: Recovery and rebuilding.

According to sources, we are in the midst of a recession. We will understand the full extent of the economic backlash once we emerge from the current crisis. However, we have our work cut out for us.

Technology and connectivity have helped us fight this pandemic. Smart cities with emerging and innovative tech applications have witnessed faster and effective testing and reporting. These cities have set the benchmarks for adaptability and recovery. We must embolden more cities with smart and connected technology. This will strengthen us and hasten the rebuilding phase for our communities.

Future generations would read about this pandemic in their history books. Let’s turn our response to it, as a global community, into something that would motivate them for years.

Image Credit: Gerd Altmann; Pixabay

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We have been talking a lot, lately, about the Internet of Things (IoT) and Artificial Intelligence (AI). So much so that it’s now difficult to differentiate the real from the not-so-real or purely ‘marketing’ IoT and AI. Data mining isn’t AI. Marketers have been doing it for a good three decades, and others likewise. It’s using intelligent correlations and cohorts to find patterns and latent needs. That’s not much that is artificial about the issue nor situation.

There should be a new marketing codebook with these lines: “Thou shalt not cite IoT and AI in vain.” I don’t know how, but the salesperson calls my latest watch “AI enabled,” whether they have AI or not. The clock is not even smart; at best, it’s just digital. When you wipe off the not-so-real jargon and look at the actual applications of AI and IoT, they are aplenty. But how do we find what is actually true — in a world so taken with these terms? It’s simple.

Just know the story behind the pitch. Does the product or solution improve over time? In a customer-facing scenario, does it customize itself to your language (maybe like the Amazon Echo).

In a more enterprise setting, does it offer better/faster delivery routes for your logistics movement each time you use it? Does it incrementally better itself with a singular goal of improving the results, learning and adjusting? If yes (to any), then it’s AI.

A system which learns on itself and tells right from wrong;

A recent use-case comes to mind. The company I am associated with, LogiNext, used Kalman filters (algorithm). NASA made the Kalman filter famous when they used the algorithm in their effort to better direct satellites in near and outer space. According to a paper, right back from 1985,

“The Kalman filter in its various forms has become a fundamental tool for analyzing solving a broad class of estimation problems.”

The company in question used an updated iteration of the Kalman filter to fix vital tracking information of hundreds of trucks moving across the country. Hence, each tracking point was, then, accurate up to 3×3 yards. What’s the impact?

• Precise knowledge of where each truck is located.
  
• Where the truck will be in the future.
• And when this vehicle will reach the destination; down to the minute.

The updated algorithm, with the layer of Kalman filter, learns from the tracking errors. It is essential as the tracking is hardware and network coverage dependent. It identifies patterns in the tracking data to understand what is ‘credible’ monitoring and what’s an error. The system would itself know which tracking data to use and which to ignore, growing the accuracy with continued functioning.

In turn, this would ensure that the information going into the system for processing and route planning is accurate. More importantly, avoiding another case of ‘garbage in, garbage out.’ It would be more consistent with incrementally better plans each time it’s used.

Here’s the IoT you can use, with complete logistics streamlining.

Logistics is primarily a game of Service Level Agreements, SLAs. A company/carrier needs to adhere to these basic unit agreements, SLAs, or minimum viable service levels. It may be when a shipment leaves, the quality of the truck or environment for the cargo, the time when it needs to reach, etc. These SLAs are the code of conduct for carriers, drivers, and companies. They are specific to each shipment. SLA breaches are a serious affair and may result in delays and eventual penalties.

So, with SLAs at the center stage, when you must track a package from perhaps LA to NY, you would expect a continuous flow of information regarding the location and state of your package, along with tracking the adherence to the all-important SLA, the ‘promised delivery time.’ How is your estimated time of arrival (ETA) looking as the package is exchanged between carriers, hubs, delivery centers, and the final mile couriers?

It’s a dynamic logistical world where even local traffic and weather may become disruptors. If you simplify the entire end-to-end movement of your package – there’s the pickup, the hub-to-hub movement, and the delivery. It’s possible that all this would be dealt with different drivers, trucks, etc., changing multiple hands. How would you know if any of these drivers are more prone to speeding or delays? How would you know if the truck loaded with your package is well-equipped to handle it? All of the maneuverability allows logistic leaders to use AI right now.

Here’s how IoT and AI help.

It’s the system, an intricate-interwoven-intelligent ecosystem of software and devices where right from the moment the package leaves your hand; it’s tracking capture the unique id and driver details, aligning-in all possibilities, down to the climate in New Jersey a day from the end-delivery time.

This system picks the best-suited driver and trucks for the package as per the promised timelines, nature of the package (perishable, fragile, sensitive, burdensome, etc.), route requirements and delays expected/predicted, hours of service for each driver (ELD/DoT compliances), etc.

All the information is beamed-up into a single screen where a manager can view all his/her trucks across state lines, and the possibilities of any delays whatsoever. This monitoring empowers the manager (and the brand involved) to take on corrective measures and avoid final delays for the end-customer.

Furthermore, this kind of detailed analysis and pin-point accuracy of multiple systems seamlessly talking to each other adds on a layer of predictability. Here the manager can efficiently predict, how many, trucks would continue to accommodate the possible load coming in, correctly. This is without having the need to dip into the spot markets.

Conclusion? Only the beginning for IoT, AI, and yes — Machine learning, too.

All this brings us to the summation of the main ‘gains’ of IoT and AI with real-world applications in logistics.

1. 1.  Risk estimation – Cutting down on possible delays, SLA breaches, and service disruptions.
2. 2.   Cost savings – Companies that can predict their carrying capacities (of trucks) precisely as per load variations (seasonal, regional, random aberrations), can plan better with their owned and market-sourced vehicles and boost their margins with favorable freight rates.
3. 3.   Customer satisfaction – The ‘holy grail’ comes within grasp, as companies can reverse engineer the perfect delivery experience using AI (exhaustive delivery route permutations to get the quickest one, consistently), and deliver on time, every time.

Perhaps it’s time we speak of AI and IoT as “tools,” which they are. They aren’t ‘magic’ solutions to each of our problems. Just last week my investment advisors told me that they could double my savings. When I asked them how they planned to do it, they quickly came back with ‘We’ll use AI.’ The funny part was that I wasn’t supposed to ask anything else. Well, I did, and now I am looking for better investment advisors.

Moral: Don’t let the terms bog you down. Look beyond them to the real-world applications, and they may amaze you.

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Let’s get down to the basics before we delve in more advanced tech. What is a business? An entity that offers a solution (or a set of solutions/products) to more than one customer. It caters to a defined or latent need. It offers this ‘value’ and accepts value back as compensation (money, time, or now, even access to personal info with permission). What are the elements in play here? There’s a business, a customer, and a market and customers are making traditional transport movement obsolete, fast.

A market is essentially a place or platform where the business and customer interact and exchange value.

Businesses look to create awareness for the products among the right-customers (target audience), make it available to the customer to try out or buy, put the final product into the hands of the customer, take feedback and offer after-sales service.

Internal threat:

Some businesses invest heavily in the current ‘way of doing things’ and discard the probabilities of the same ‘way of doing things’ evolving over time. We have seen this in businesses getting ‘cocky’ saying they know what the customers want and how they want it, just because it has worked for them before. It won’t necessarily work for them in the future. That’s why some like ‘Toy’s R Us’ fell by the side. Who’s to blame? No one. That’s the nature of evolution.

As customers and their needs improve, so does the market. How a business and customer interact and deal with each other, keeps evolving. A customer has multiple quality products that they can choose from. Customer needs are not “needs,” they are “demands.”

Businesses have become very good at making the best product, backed by great promotional campaigns. Also, now the customer has the tools required to make an intelligent choice about what they want to buy. There are peer-reviews in blogs and videos for them. In a heavily social word, word-of-mouth, or what we now call ‘viral’ coverage has made it very easy for quality products to be recognized. This covers the awareness.

Customers know what they want, do you?

Customers now know exactly what they want, and they know what everyone in the market is offering. They are well-prepared to make a wise choice. Some businesses have reached a saturation point to how much they can improve their products (as per customer’s needs). No matter how much they improve, there would be someone else who would do it better and market themselves more aggressively. Once customers agree that the competitor’s products are more suited to them, they will simply switch.

External threat:

What businesses considered as their core-differentiator, is becoming less and less unique. Competitors are closing on market leaders with their own identical or better products. As for the customer, they see more and more companies offering similar products and expect price-wars. Market leaders tend to reach their deep pockets and offer discounts to push the new-entrant competitors out of the business. This, however, restricts them to do what they were actually good at, building a product which evolved faster than how the customer’s need evolved.

Since the new entrants have moved the battle, they can attract external investment, use it to lay down a long runway for their growth, and keep eating into the market. Sooner rather than later, more new entrants join in. They are all attacking the big fish. Then, eventually, the market opens up with no clear leader. The previous leader would now share the stage with any one of the new-entrants (that hasn’t burn through their funds already) themselves. The product differentiators won’t be the same for long. Businesses must build more than one differentiator to sustain.

Make it available and convenient for the customer

Beyond the awareness and product quality, there is a very important part of the market that can create that additional value which makes the customer stay with a company. The ‘availability’ part. They can build a lasting differentiator here which would be extremely difficult to emulate.

Getting it into the hands of the customer at the right time, in the right manner. The movement of your product has to be perfect. Imagine going to a fine restaurant. Even though the food might be nice, if it is just slapped on to a plate and dumped in front of you, next time you would go to the restaurant which gives you more respect. That’s exactly the role good transportation practices play in end-customer satisfaction.

It starts with a delivery promise

Let’s run through a simulation. A customer goes to a portal and selects a product they like. They place the order and get an option. Do they want the product the next day or after two-days? They select the next-day. What was the point of giving the customer this option? They have already selected the product and probably would pay for it in the next instance. Why take the extra effort to put the product in their hands the next day itself?

Here, you are increasing the probability of getting-in on their next purchase. When they buy again, they would be more likely to come to you. There’s your differentiation. You just gave the customer the control over their delivery time window. There are even options where the customer can pick how they want to receive the product, to be left with a neighbor, to be gift-wrapped, etc. There’s a lot to say about these promises and the delight it creates for the customer.

Remember retail ambiance? Malls spent millions creating ‘that’ ideal ambiance with air conditioning and music to offer that extra delight which would make the customer come back. Ambiance has been replaced by the ideal ‘delivery experience’.

Create the highest value for customer, company, and economy

What’s in it for the customer? Faster delivery, more control over the timelines, and hence, higher value. For them they just got a discount, and on top of that, they are getting this amazing deal of faster delivery and other perks.

What’s in it for the company? Higher customer satisfaction and retention; and creating lasting business differentiation. Another factor from stationary retail that has evolved is the idea of an impulse buy. When customers are happier at the time of check-out, with the promise of getting hold of the product very soon, they are more inclined to buy large amounts and more frequently. Companies can, not just lengthen customer lifetime value, but also increase it multifold.

What’s in it for the market or economy? Customers have a higher buying power than before. When they are delighted by the perceived benefits of such purchases, they would buy more and support the economy (and connected businesses) more. There’s also the factor of technology evolution which goes hand in hand with these interactions. Almost, all elements of this market are backed by technology. And technology development directed by businesses (in need of market growth) always brings in high-levels of innovation.

Delivery promises turn into expectations and then experiences

It’s all well and good, as far as promising a great delivery experience to the customer goes. Now, the business must fulfill these promises. How would they ship a product halfway across the country in a day? Those that win this transportation challenge, win the market.

The secret lies in proper technology intervention and support.

The latest in cloud-based optimization solutions give businesses the ideal plan on how to best pick and load the product in-time from the warehouse, dispatch it on-time, track it as it moves through a route optimized to pass through minimal traffic – avoiding unnecessary delays and detention, reach the local distribution center, pass on the right product to the smaller vans quickly, and track the passage through connected scanning devices.

Now comes the tricky part, there are hundreds of such customers that requested next day delivery. This part of the distribution leg is called the last mile. Tons of literature is written about the complexities of such movement. There are multiple versions of the traveling salesman problem out there about this same scenario. Manually planning these complex legs ends with delays, which in turn lead to late or missed deliveries.

The irony of customer expectations is that now you potentially lost a customer because of an expectation the customer didn’t set themselves, but was prompted to do so by you. If you just hadn’t given them an option and said that the delivery would take two days, then all would be fine. But then, someone might have offered them a similar product in faster time and the customer might have gone for it. It’s a dilemma either way.

How the right technology makes it all fit into place

The only way out is to deliver on the promise every time, without fail. You can do it with the use of the right tech.

Since the last mile problem with multiple constraints, permutations, and real-time adjustments is too tough for a person to do daily, it can be put through an algorithm working on artificial intelligence and machine learning logic combined. This algorithm would suggest the perfect route, devoid of traffic to go along a delivery schedule which helps reach all destinations on-time. It would work on a continuously growing set of location data points that update live. All customer addresses would be verified in the system and the routes connecting them to the deliveries nearby would be made as short as possible. This would reduce overall turnaround time for the local distribution center, saving resource and time costs. The delivery schedule plan would give them ample idea about how to balance the incoming load and whether they need more vehicles to fulfill all deliveries.

Even along the different transportation legs, all moving elements are connected to a central monitoring system. Vehicle movement, in-transit product quality, driver behavior, package tracking from warehouse to distribution center – scanned at all entry-exit points. All this comes in the domain of what we have come to know as the Internet of Things. Through intricate and continuous connectivity, businesses can control all their movements from a single platform.

Such connected transportation, backed with instant notifications and alerts, gives a high degree of agility to the movement. Businesses can react to on-ground situations faster. Suppose, there is a vehicle breakdown, even before the driver calls up the manager, there would a repair or replacement vehicle on the way. It’s about covering all bases to ensure that there are no surprises when it comes to putting the desired product in the hands of the intended customer, on-time.

Technology is more acceptable than ever before. If all this seems futuristic, it’s not. It’s what’s happening in the world right now. To sustain in this environment businesses must accept that logistics, especially last mile optimization, is now a part of their value proposition to the end-customer.

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