Welcome to our 2nd part of Logistics Week Series. Having understood the impact of digitalization on logistics from our previous article, today we see how logistics work in a demand driven omnichannel commerce ecosystem. Technology in Logistics grew throughout 2016. Over the last year, huge strides were made in machine-to-machine connectivity as a stronger resolve for more omnichannel logistics solutions. It is important to know how they relate to improving supply chains and why they are essential to omnichannel logistics solutions.

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Next Level Of Supply Chain With
Omnichannel Logistics
Welcome to our 2nd part of Logistics Week Series. Having understood the impact of
digitalization on logistics from our previous article, today we see how logistics work in a demand
driven omnichannel commerce ecosystem.
Technology in Logistics grew throughout 2016. Over the last year, huge strides were made in
machine-to-machine connectivity as a stronger resolve for more omnichannel logistics solutions.
It is important to know how they relate to improving supply chains and why they are essential to
omnichannel logistics solutions.
Connected Logistics Solutions
By some accounts, overall spending on connected logistics solutions globally exceeded $7
billion, and more than $13 billion will be pumped into this supply chain powerhouse by 2020.
The real scope of investment into connected devices can be seen in the following graph.

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This increasing level of connectivity was driven by three critical types of sensors and data
processing, which include the following:
 RFID sensors found a place among large shipments. Radio frequency identification
(RFID) tags were commonplace among individual shipments in 2016. Additionally, these
sensors were used to help monitor temperature changes and ensure the best conditions
possible for shipment.
 Part of the problem with the large-scale implementation of machine-to-machine
connected devices was data capture. In other words, someone had to scan and enter
information physically.AIDC communications reduced workload, making tracking items
and using analytics easier than ever. More importantly, they have eliminated the chance
for error from manual entry systems.
 IoT-based tech spending saw a huge boost, becoming synonymous with improvements. It
is eliminating bottlenecks in the supply chain, ensuring maintenance is kept up-to-date
and verifying the operability of all equipment.

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Customers Pushed For More OmniChannel Logistics Solutions
We have been witnessing a steady push for more omnichannel logistics solutions, such as online
or in-store returns, purchasing or shipping. Powered by cloud-based technologies and advanced
automated protocol integration (API), consumers’ demand for omnichannel logistics solutions
was stronger in 2016 than ever before. More interestingly, companies saw this demand and
answered the call.
Walmart launched a pick-up service, allowing customers to shop online and simply drive up to
get their orders. Considering Amazon’s promise of next-day delivery, the opportunities through
omnichannel logistics solutions are growing stronger. Now, consumers can return products to
Amazon stores, and recently, Amazon launched a no-employee grocery store, Amazon Go, in
Seattle.
Inventory Management InOmnichannel Success
A huge factor in omnichannel success or failure is inventory management and a big component
to maintaining accurate inventory counts is visibility. Gaining real-time inventory visibility, with
real-time carrier updates, into where your merchandise is across your entire distribution is
essential with the changing nature of omnichannel operations.
E-commerce is driving the new omnichannel wave.The following show how retailers and
manufacturers are applying omnichannel logistics strategies.
 Omnichannel Warehouses. Consumers may reach these warehouses that serve both
businesses and consumers from online and offline channels. Various automation
solutions fulfill the different supply chain requirements.
 Fulfillment Capabilities and Real-Time Customer Interaction. Companies have started
integrating their systems and providing real-time inventory visibility combined with
accurate and timely information sharing. This is necessary in optimizing supply chain
management.
 Efficient Last-Mile Delivery Processes. In an attempt to enhance speed, flexibility and
convenience, brick-and-mortar stores have now become pick-up locations for online
orders. Some retailers have partnered with these stores leveraging their products to
provide their consumers a faster and more cost-effective delivery option.
 Value-Added Services. Many companies offer customization of products according to
customer’s preferences. In the future it will be possible to actually ship the products
before the customer orders, or make predictive purchases. This will help gain an omni
customer service experience.
Companies are expected to take advantage of the benefits that an omnichannel logistics
platform brings in resulting in many more satisfied consumers across the globe.
Dear friends, here we move to the 3rd part of Logistics Week Series. The logistic industry is
going through an unprecedented transformation and today, we are going to study the role of Big
Data in optimizing Supply Chain Operations.

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Big Data is still a relatively untapped asset that logistics companies can exploit once they adopt a
shift of mindset and apply the right drilling techniques. Sophisticated data analytics can
consolidate this traditionally fragmented sector, and these new capabilities put logistics providers
in pole position.
Big Data in Logistics Business planning
For years, analysts have depended upon macro indicators like good’s category, demand volume
and supply levels etc. to forecast oncoming impact. Big Data disrupts the logistics space with its
capability to extract micro indicators that have significant impact on the business.
 Demand and Supply Chain Forecasting
Every transaction in Supply Chain is producing data and every actor involved in it is releasing
information. Big Data is compounding the structured and unstructured data into actionable
insights, giving a more accurate forecast.
 Sales and Marketing Intelligence
Big Data in Logistics can give a customized market research and venture into demographics to
target potential customers, scaling business.
 Hyper Local Business Models
New data sets available from GPS, embedded sensors and mobile apps are enabling the logistics
players to become hyper-specialised, developing launch pad to augment new value-added
services. For example, C2C Logistics (consumer-to-consumer) ecosystem that would allow
shipments to flow from one consumer to another.
Big Data in Logistics Customer Service
Here is how Big Data makes an impact to create a superior customer service- meeting the
requirements of Logistics ecosystem.
 Customer Retention
Big data combined information of customer’s complaints, requests, appreciations and other
interactions from conventional CRM and combine it with publicly available sentiments on news,
annual reports and social media to identify patterns that can point out potential attrition in the
customer. The information can be further applied into programmed application to automatically
trigger loyalty programs.
 Service Performance Continuity
To measure and tune supply chain performance, customer feedback provides valuable insights.
Apart from the traditional customer surveys, the customers now are discussing the brand,

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services and products on social networks and discussion forums. Big Data techniques now are
analysing unbiased customer sentiments from the huge text, audio, video data etc. can identify
correlations between various parameters and can be broken down by location, action and time.
Logistics as a Data-driven Business
Big Data analytics can provide competitive advantage because of five distinct properties. These
five properties highlight where Big Data can be most effectively applied in the logistics industry.
Let us see them in the below picture.
(Source: DHL)

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These properties make using Big Data invaluable in Supply Chain Management. Instead of
having to depend fully on what you can see, you can predict, use data from past operations, and
so much more to increase operational efficiency.