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Quantitative Methods In Supply Chain Management...


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Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested.


The first chapter is an extensive optimization review covering continuous unconstrained and constrained linear and nonlinear optimization algorithms, as well as dynamic programming and discrete optimization exact methods and heuristics. The second chapter presents time-series forecasting methods together with prediction market techniques for demand forecasting of new products and services. The third chapter details models and algorithms for planning and scheduling with an emphasis on production planning and personnel scheduling. The fourth chapter presents deterministic and stochastic models for inventory control with a detailed analysis on periodic review systems and algorithmic development for optimal control of such systems. The fifth chapter discusses models and algorithms for location/allocation problems arising in supply chain management, and transportation problems arising in distribution management in particular, such as the vehicle routing problem and others. The sixth and final chapter presents a short list of new trends in supply chain management with a discussion of the related challenges that each new trend might bring along in the immediate to near future.


Overall, Quantitative Methods in Supply Chain Management may be of particular interest to students and researchers in the fields of supply chain management, operations management, operations research, industrial engineering, and computer science.


Supply chain planning is almost impossible without having an understanding of the future. And since an ecommerce supply chain consists of several moving parts, how does an online brand go about supply chain forecasting, so they can make better predictions and decisions


Supply chain forecasting is essential in ecommerce and a major component of supply chain management. Without forecasting abilities, it is hard to run a smooth ecommerce supply chain without any predictions on future demand, pricing trends, and even supply availability.


The moving average is one of the simplest methods for supply chain forecasting. It examines data points by creating an average series of subsets from complete data. The average is used to make a prediction on the upcoming time period and is then recalculated every month, quarter, or year.


This is a simple supply chain forecasting method used to measure some determinations using existing assumptions, such as seasonality. When compared to other methods, it offers a fast and easy way to make predictions.


Life cycle modeling is a supply chain forecasting method that analyzes the growth and development of a new product. It requires data across different market groups such as creators, early and late adopters, and the early and late majority.


ShipBob is a best-in-class fulfillment provider that offers full visibility into your supply chain. We make it easy for brands to expand their distribution network and track inventory and orders in real time all from one dashboard.


There are several different forecasting methods used in supply chain management. Depending on the amount of data you have to work with, you can choose between qualitative and quantitative forecasting methods, or a combination of both.


Supply chain forecasting improves operations by preparing for unexpected events, delays, and fluctuations in the demand. By using data and other insights to make better decisions, you can optimize costs, meet demand, and keep your supply chain running.


The explosive impact of e-commerce on traditional brick and mortar retailers is just one notable example of the data-driven revolution that is sweeping many industries and business functions today. Few companies, however, have been able to apply to the same degree the "big analytics" techniques that could transform the way they define and manage their supply chains.


In the second part of this article series, we will show how companies can take control of the big data opportunity with a systematic approach. Here, we will look at the nature of that opportunity and at how some companies have managed to embed data driven methodologies into their DNA. Exhibit 1 provides an overview of the landscape of supply chain analytics opportunities.


Big supply chain analytics uses data and quantitative methods to improve decision making for all activities across the supply chain. In particular, it does two new things. First, it expands the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems. Second, it applies powerful statistical methods to both new and existing data sources. This creates new insights that help improve supply chain decision-making, all the way from the improvement of front-line operations, to strategic choices, such as the selection of the right supply chain operating models.


Typically, planning is already the most data-driven process in the supply chain, using a wide range of inputs from Enterprise Resource Planning (ERP) and SCM planning tools. There is now significant potential to truly redefine the planning process, however, using new internal and external data sources to make real-time demand and supply shaping a reality.


We can think about managing inventory in a supply chain similar to the way electricity is managed: Storing electricity is expensive and difficult; power companies bring in additional consumers or start and stop plants to ensure a balanced power grid. Retailers now have the opportunity to use a similar approach. Visibility of point of sale (POS) data, inventory data, and production volumes can be analyzed in real time to identify mismatches between supply and demand. These can then drive actions, like price changes, the timing of promotions or the addition of new lines, to realign things.


In many companies, data on procurement volumes and suppliers are only gathered for few activities in the sourcing process. However, supply data goes beyond the classic spend analysis and annual supplier performance review. On a transactional basis, supply processes can be sensed in real time to identify deviations from normal delivery patterns. Firms are also finding opportunities for predictive risk management. By mapping its supply chains and using "Google trend"-style information and social data about strikes, fires, or bankruptcies, a firm can monitor supply disruptions in transportation, or at 2nd or 3rd tier suppliers, and take decisive actions before its competitors.


As the examples in this article show, big data is already helping leading organizations transform the performance of their supply chains. Today, such approaches are the exception rather than the norm, however. Lack of capabilities and the lack of a structured approach to supply chain big data is holding many companies back. For big data and advanced analytical tools to deliver greater benefits for more companies, those organizations need a more systematic approach to their adoption. Part 2 of this series will address that topic in detail.


About the authors: Knut Alicke is a master expert in the Stuttgart office, Christoph Glatzel is a director in the Cologne office, and Per-Magnus Karlsson is a consultant in the Stockholm office. Kai Hoberg is an associate professor of supply chain and operations strategy at Kühne Logistics University, Germany.


Students who have taken significant coursework in management science and quantitative methods while at Tuck may receive formal recognition for their course of study. This designation often appeals to students planning post-Tuck careers with a quantitative or technical focus. To qualify, students must take at least 15 elective credits from a menu of qualifying courses during the two-year MBA program. The 15 credits used to qualify can include no more than one full-course equivalent grade of LP (one full-course equivalent is one full-term course or two mini courses). This STEM-designated MBA may grant an additional 24 months of Optional Practical Training to students studying on an F-1 visa.


In rare cases, non-Tuck courses that have been approved to be taken for Tuck credit may count toward the management science and quantitative methods option. Such coursework must be consistent with the qualifying sequence of study. Petitions should be sent to tuck.registrar@tuck.dartmouth.edu, and should include a course syllabus, assignments, and exams (if possible).


The SCM program provides students with basic knowledge of supply chain management, such as distribution strategies, planning, and procurement, while also working on their communication, negotiation, and leadership skills.


The SCM program provides opportunities for students to learn about supply chain management in an integrated business framework from distinguished faculty and regional industry experts in the area of supply chain management. The program prepares students to operate and lead major aspects of the supply system in established and start-up firms.


The curriculum focuses on providing knowledge on topics of practical skills and competencies: Supply Chain Principles, Transportation and Logistics Management, Procurement, Warehousing, and Inventory Management. Courses balance theory and practice in supply chain management to bridge the gap between academic and business practices and devise innovative research and teaching methods. 59ce067264






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