Improving Logistics Performance With Big Data

Unsurprisingly, the topic of choice for most lobby conversations is Big Data. An easy definition is: a set of data that is too large and complex for conventional programing to compute and analyze. And if computer department is nothing but conventional, then, there might have a problem. An organization’s ability to capture massive data sets and discern ever changing consumption behaviour, disclaim unfeasible aberrations, form accurate value management segments for analytics and develop fine tuned strategies that account for breaking viabilities; thus, is crucial in this competitive day and age where the competitors, namely flexible startups, can leapfrog ahead of everyone in a blink of an eye. Ironically, it’s the very business owner imposing on the executive team “I want to know exactly how many customers will be added by investing in marketing and IT” that holding back progress. As the former doesn’t work without accurately defined segments while the latter, not without defining and streamlining the entire organizational structure in the first place, the conversation becomes a catch 22 chasing around a phantom tail. A streamlined organizational IT infrastructure, nonetheless, can progress data structures proven crucial to logistics optimization, which in turn distinguishes the company from competitors with superior delivery and therefore, customer satisfaction. It’s a fundamental platform that can make or break a business, that comes with a reasonable price, esp. nothing the smart local computer gigs can’t reinvent the wheel, themselves. It’s a pretty cheap new normal.

Reliable demand forecasts and transportation planning, or Itinerary Optimization, will improve the efficiency and effectiveness (2Es) of logistics operations and in turn maximize overall profitability. As logistics and supply chain management are increasingly confronted with extensive complex network requirements, though, a Big Data capacity then becomes imminent, either as an opportunity or a barrier.

Key sources for Big Data today consists of:
>>General data input and maintained by the organization
>>Traffic-weather data through sensors, detectors and forecast systems
>>Vehicle analysis, driving patterns and geological location data
>>Business financial forecast data 
>>Commercial advertisement response data
>>Website access and browsing data
>>Data from social media

Given today’s rate of data occurrence that takes place more frequent than we can attend to, obviously, numerous sources of information needs to be properly handled for subsequent computation. As data needs to be imported into the system and analyzed in an automated fashion for business analytics purposes, Big Data solutions can help logistics and transport companies 360° big picture perspective with vital information and automated actionable capacity on the entire operation in real-time and in a user friendly manner, never mind instantaneous interaction with customers, without dedicating an entire department to unnecessary functions.

Therefore, companies that opt to prioritize logistics and supply chain management to assess performance metrics, consumer value management, and optimize overall standard operating procedure and workflow as well as develop advanced solutions that give the organization an edge over competitors with superior agility, will need to reconsider present capacity to initiate Big Data management systems.

Customer development along with business partners and service performance.

As transportation is today’s key success factor for businesses and supply chain, while logistics providers differentiate themselves through service reliability, the challenge becomes accuracy in delivery turnaround time, as well as safety driving behavior and transparent, inspectable, operating procedures. Transportation business, for that matter, are required to manage with a tight grip on weather forecast, vehicle selection, staff capacity and other amenities. Thus, Predictive Analytics plays a crucial role in assuring planned operations are carried out as thoroughly, logically, analyzed as intended.

Operational Optimization.

Predictive analytics is a critical component to the Big Data mechanism, typically used for warehouse management. However, nowadays, applications are extended to other areas, as well, such as route optimization, as transportation itinerary determines associated overhead. Shipping costs can be minimized, thereby maximizing profit, through thorough selection of vehicle size and route according to cargo size and weight as well as terrain and transportation feasibility. All the same, other challenges cannot be ignored, particularly, safety concerns. Thus, Predictive Analysis can complement Big Data in ascertaining the most practical solutions to maximize safety, reliability, and customer confidence, all the way to kicking business cost down the scale.

Business Cases

With diverse data structures in sizable volumes, transportation companies are able to analyze information and determine best practices in four crucial areas as follow:

1.Plan Objectives, Accurately: resource analyst  must be able to forecast future demands accurately to assure the company maintains appropriate staff and resources in the right place and at the right time in order to deliver products and services to customers in accordance to promised time schedules. Additionally, they are responsible of inspecting warehouses and distribution centers for accurate stocks levels.

2.Route Optimization: also termed itinerary optimization, the most important factor that determines satisfactory delivery turnabout time with the highest level of operational efficiency possible, is the appropriate number of staff and itinerary planning with proper tools. Transportation companies, including commercial airlines, must understand the impact of altering weather conditions, connectivity failure, staff limited working hours, maintenance schedule as well as other factors. Thus, sensory technology and sensor devices have become an important source of data. In conjunction with telematics programs, financial opportunities can be identified. For instance, insurance premium can be reduced based on risk analysis that references real-time statistical data and authenticated information. In addition, best itineraries can be planned for optimized service efficiency and delivery turnaround time by referencing customer location, and etc.

3. Support the growth in the customer’s business: businesses must be able to satisfy obligations with delivery of products or services according to the Service Level Agreement mutually created with the customer, or in other words, with the supply chain partner. Whichever direction the customer wishes to grow their business, logistics must be able to accommodate.

4. Risk Analysis: logistics providers must possess a comprehensive insight of different risk factors that relate and impact service performance, such as the damage that may occur as a result of delayed delivery, the impact of change in weather temperature that may cause mishandling of cargo, or even risk assessment related to the driver or operating vehicle according to local geographical information and data generated from on-site sensors.

The aforementioned are only a fraction of the business challenges in various industries to logistics that demonstrate the opportunity in enhancing operational efficiency based on effective data analytics in different areas. As analytics become accurate and reliable, effectiveness in operational planning, assessment and adjustments according to new factors that arise should increase as well.

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Compiled by BLOG.SCGLogistics

References & Pictures by:  linkedin.com, forbes.com, cw.iabc.com

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