Supply Chain: Learn how newer developments are being applied in practice in various industries, to make sure that the right things get to manufacturers, wholesalers, retailers, and consumers when they are needed.
Supply Chain Analytics: Emerging concepts and innovative technologies are disrupting business and driving new policies, products, services, and channels for increased revenue. Supply chain leaders are evolving their businesses to keep pace and, during this track, our accomplished speakers will share practical applications of new concepts and technologies being implemented within their supply chain analytics programs to maximize their efficiencies while minimizing business disruptions.
Dynamic Pricing for Varying Assortments
Most demand learning and price optimization approaches in academia and practice rely on learning the demand of each product in an assortment over time via price experimentation. Although these approaches may work well when the retailer offers a static assortment, the approaches fail to learn demand and optimally price when retailers change their assortment frequently. With the growth of e-commerce as well as fast fashion business models, retailers are changing their assortments more frequently. In this research, we develop a demand learning and price optimization approach for retailers whose assortments change frequently. Our approach can be described as a “learning-then-earning” approach that uses conjoint analysis and optimal experimental design to learn attribute-based demand, and subsequently uses this information to price optimally. We test our algorithm in a field experiment at an e-commerce company, which demonstrates that our algorithm quickly learns demand and sets prices that significantly increase revenue.
Analytics in Compliance Risk Management – An Adaptive and Recursive Approach Using Machine-Learning Methods
Compliance risk in supply chain management is one of the most critical risks that businesses are exposed to. While leading the industry in commercial credit and operational risk analytics, Dun & Bradstreet (D&B) has worked with multiple globally diversified enterprises and formulated an adaptive and recursive approach for proactive compliance risk management of global suppliers. This talk will explain what this analytics approach entails and how this approach has successfully helped businesses in today’s challenging compliance landscape. In this talk, we will start with data and its various aspects, which is the base of analytics, and then move on to methods of analytics as well as method comparisons. Finally, we will explain in depth how analytical results can be applied to and incorporated as an input in an adaptive and recursive process over time.
Closing the Gap Between Forecasting and Inventory Management
Although methods for forecasting and inventory optimization are well established, the intersection of the two is less developed. Nevertheless, minimizing forecast error and inventory cost separately may lead to sub-optimal overall performance. When forecasts are employed for inventory decisions, it is advantageous to consider the resulting inventory performance instead of more commonly used forecast error metrics. Forecasting methods are typically evaluated based on statistical properties without taking into account lead times, inventory cost, or service levels. Similarly, inventory methods are optimized independently of the forecasting process and treat the forecast as simply an input. In this talk, we discuss methods developed to improve the integration between forecasting and inventory management. The goal of this work is to move beyond sequential optimization of forecast and inventory models to a framework in which forecasting and inventory management are treated as an integrated cycle where each one influences the other.
Variance-Damping or Variance-Amplifying? A Look at Analytics in the Supply Chain
Analytics has increased firms’ ability to adapt their decision policies in response to incoming information. The benefits of such an ability are evident. However, we focus on an important but overlooked tradeoff, which comes from the fact that adaptive decision policies may introduce more variation into a business process than do their non-adaptive counterparts. We differentiate between “variance-damping” and “variance-amplifying” analytics, giving common examples of each. Our analysis includes foundational operational decisions such as inventory ordering and pricing, both of which have been the focus of much analytics research recently. Based on our theory, we give managerial insights for a firm’s analytics and supply chain strategies.
Mitigating Supply Chain Risk by Combining Internal, External, and Open Source Data into a Single User Experience
Supply chain risk can be mitigated by fusing a myriad (internal, external, and open source) of data sources in a single user experience. Big data analytics solutions can detect and monitor various supply chain risks including counterfeiting and fraud in known vendor networks and beyond.
This session will focus on:
- Counterfeit and fraud in supply chain network — The insertion of counterfeit goods into licit supply chain networks is a growing multi-billion dollar threat to all commercial and governmental sectors. Counterfeiters have been exploiting the internet, especially social media and the dark web, to introduce their products into the market. This threat amplifies when goods are produced in developing countries. Big data analytic solutions leverage natural language processing, machine learning, and data science to detect potential instances of fraud and / or counterfeiting and create decision-oriented, actionable insights for leaders at all levels and across all business functions.
- Counterfeiting and its associated role with Threat Convergence — Changing geopolitical dynamics and the interconnected supply chains present a significant risk to all organizations. Understanding how counterfeiting pays a crucial role in the alliance of: espionage, criminal, opportunists, terrorists, state sponsored entities and cartel syndicates; is paramount in the endeavor of using NLP, machine learning, and other data science techniques to actively assist in identifying these risks.
- Counterfeit detection using natural language processing — If a supply chain stretches to the developing world, particularly to factories that produce small, unsophisticated components, it is potentially more susceptible to risk of counterfeit and/or fraudulent activities. With multi-lingual NLP and translation capabilities big data analytics solutions support risk tagging in original language and English.
- Introduction to advanced supply chain analytics — Four fundamental attributes of counterfeit detection and monitoring technology are contributing to a robust and efficient solution today: fuses multiple data sources; automates activities; improves through machine learning; presents information for effective decision making.
- Client cases and examples
- Other use cases
Reverse Logistics and Machine Learning: The Key to Predictive Repair
Tom Maher will discuss how you can improve initial diagnostics and product serviceability through the use of analytics and machine learning. The discussion will revolve around leveraging data provided from the Reverse Logistics Supply Chain, and how you can make determinations to enhance current diagnostic processes. In addition it will explore how you can leverage the same data to predict repair outcomes prior to product arriving at repair centers. Benefits include: a higher first-time fix rate, more efficient repair operations and lower service incidents.
Using Data Analytics to Challenge Conventional Thinking
Mark Ramirez and Daniel Windle
Trinity Industries, a leader in railcar manufacturing, leasing, management, and maintenance services has transformed in an industry where institutional knowledge, intuition, and rules of thumb largely drive decision making to an organization where data-driven analysis has become far more normalized.
This journey started with transforming our supply chain to create scenario analysis and optimize based on a range of scenarios. It continued with demand forecasting and analysis as a more complete understanding of the commodities transported by rail and the railcars that carry them. The tools, processes, and vendors used to enable this change will be discussed. This session will focus on our story of data-enabled change.
ROMEO: A Fast and Transparent Framework for Multi-Echelon Inventory Analytics in Chemical Industries
Defining the right level of inventory in multi-echelon supply chains is a key issues for commodity as well as specialty chemical companies. In the past 15 years, the Guaranteed Service Model (GSM) has gained wide adoption in planning software. While the GSM-based approaches bring valuable insights in retail or discrete manufacturing supply chains, these fall short in chemical supply chains where production wheels, tight manufacturing and warehousing capacity constraints as well as variable recipes exist. We present a simulation/optimization approach called ROMEO (Rolling Optimizer for Multi-Echelon Operations) that replicates daily supply chain operations (Order Promising/ATP, Supply Planning) and hence provides analysts with more tractable inventory recommendations that users can relate to. After a quick overview of literature and problem statement, we’ll describe ROMEO’s logic and show how it is currently applied at Eastman Chemical Company to drive inventories down.
Using Classical Optimization and Advanced Analytics to solve Supply Chain Problems
LLamasoft will discuss how its Applied Research group combines cutting edge applications of classical operations research techniques with newer advanced analytical techniques to solve a broad spectrum of supply chain business problems. The talk with explore the classical applications within network optimization, vehicle route optimization, inventory optimization, and supply chain simulation to solve detailed supply chain problems at scale. Additionally, it will discuss the application of machine learning and artificial intelligence to improve demand forecasting. Finally, it will touch on the cross section between the classical and analytical techniques and how they can work together to solve more problems.
Anticipating a World of Automated Transport: Cost, Energy, and Urban System Implications
Connected and (fully-) automated vehicles (CAVs) are set to disrupt the ways in which we travel. CAVs will affect road safety, congestion levels, vehicle ownership and destination choices, long-distance trip-making frequencies, mode choices, and home and business locations. Benefits in the form of crash savings, driving burden reductions, fuel economy, and parking cost reductions are on the order of $2,000 per year per CAV, rising to nearly $5,000 when comprehensive crash costs are reflected. However, vehicle-miles traveled (VMT) are likely to rise, due to AVs traveling empty, longer-distance trip-making, and access for those currently unable to drive, such as those with disabilities. New policies and practices are needed, to avoid CAV pitfalls while exploiting their benefits.
Shared AVs (SAVs) will offer many people access to such technologies at relatively low cost (e.g., $1 per mile), with empty-vehicle travel on the order of 10 to 15 percent of fleet VMT. If SAVs are smaller and/or electric, and dynamic ride-sharing is enabled and regularly used, emissions and energy demand may fall. If road tolls are thoughtfully applied, using GPS across all congested segments and times of day, total VMT may not rise: instead, travel times – and their unreliability – may fall. If credit-based congestion pricing is used, traveler welfare may rise and transportation systems may ultimately operate near-optimally. This presentation will present research relating to all these topics, to help professionals and the public think about policies, technologies, and other tools to improve quality of life for all travelers.