Data democratization and self-service analytics are a gateway to companies’ data-driven decision-making in this post-digital transformation world. Rapid digitalization has altered the scope and scale of data usage across industries and throughout supply chains. Database structures and data management processes require accessible, accurate, and timely data, integrated from many sources to progress alongside this evolving landscape.
Analytics and BI-related technologies, practices, examples
Supply Chain Networks are evolving to become increasingly autonomous, letting intelligent software agents make simple decisions.
A ‘self scorecard’ can help shippers improve their own turn-around times, detention metrics, dock door scheduling, load tender timing, and fulfillment of volume commitments. We also examine how trade data can be used for supply chain risk management, supplier discovery, price discovery, total landed cost optimization, and competitive intelligence.
Analytics can be used to improve carrier performance, enabling data-driven negotiations, improving delivery performance, reliability, responsiveness, and information sharing.
The mandatory adoption of ELDs provides all kinds of data that can be used to improve driver performance in many ways. Analytics can also be used to improve route and service planning, optimizing the cost of service tradeoffs.
How shippers and carriers can leverage analytics to improve the performance of their private fleets using near-real-time shipment location data, combined with customer orders, routing plans, electronic proof-of-delivery data, service/work order schedules, vehicle inspection/maintenance data, and other sources of data.
Implementing advanced analytics requires data science expertise, analytic technology, data integration, data-wrangling capabilities, and ideally the ability to rapidly implement advanced analytics. Some transportation and logistics solution providers have these capabilities, so their customers don’t have to acquire them. Here we discuss how to assess whether or not a logistics software solution provider has the right capabilities.
A discussion of requirements for Agile Demand-Supply Alignment (ADSA) solutions for demand management (demand-side visibility, time-phased views, POS and channel data visibility, order pegging, demand sensing, retail planning capabilities), and analytic capabilities (e.g. supplier performance, carrier performance, lead-time analytics, total landed cost optimization.)
We explore the kinds of data being produced by transportation and logistics systems, how that data can be used to create a data-driven enterprise, and the substantial obstacles to achieving an analytic advantage.
A framework for understanding Agile Demand-Supply Alignment solutions. We present questions to ask solution providers about how they detect, contextualize, prioritize, predict, and prescribe solutions for demand-supply imbalances.
The job of Data Scientist is supposed to apply to those who program with AI and Machine Learning technology. Instead, they spend most of their day on the data, which begs the question, don’t we already have legions of data people?