Category: Technology Hardware, Storage & Peripherals

The Choice of Choice: The Perceived Importance of Selection in a Modern Supply Chain

By Ryan Carl Dincher, supervised by Robert A. Novack📧 (Thesis Supervisor) and John C. Spychalski📧 (Honors Advisor) (2020)

In the field of supply chain management, internal structure and processes are often dictated by the product offering given to the customer. More choice, subsequently leading to more complex supply chain systems, have become the new industry norm. Statements such as, “The customer is always right,” imply that consumer choice is directly correlated with consumer satisfaction and purchasing behavior. Were this not true, and businesses were able to limit stockkeeping-unit (SKU) availability and work towards full automation without impeding upon their top line, the net financial impact could be profound. This thesis will examine two key concepts that relate human psychology to a company’s supply chain: the importance of choice for consumers and the cost associated with removing this established norm of perceived customization. The impact of this on customer behavior will be used to detail the ways in which supply chain systems can subsequently be optimized and automized. Using Dell Technologies and the automotive industry as a real-world examples, this thesis will serve to prove that offering customers less choice can lead to increased profitability for retailers by reducing their expenses and, potentially, increasing their revenue

Access the paper at Electronic Theses for Schreyer Honors College (ETDA) website here.

Artificial Intelligence & Machine Learning: Supply Chain Risk Management

By Tina Pan, supervised by Robert A. Novack📧 (Thesis Supervisor) and John C. Spychalski📧 (Honors Advisor) (2020)

The resurgence of Artificial Intelligence (AI) and Machine Learning (ML) research has increased technological innovation. The field of Supply Chain Risk Management (SCRM) benefits from the increase in AI and ML tools and solutions, which provides a means to better identify, assess, mitigate, and manage supply chain risks. As organizations broaden and globalize their supply chains, they are introduced to new supply chain risks. Different forms of risks occur depending on the structure of an organization’s supply chain. As the types of risks vary, the solutions implemented to identify and manage risk will also vary. Currently, on the market, there are tools specialized in providing solutions to address risks in different segments of the supply chain. AI and ML can help organizations reshape their SCRM processes when used properly. The use of AI and ML may be a solution that works for one organization but may fail at another organization. This thesis analyzes the impact of AI and ML on SCRM by first defining AI, ML, and SCRM. The thesis will identify findings from interviews and research on these technologies and provide current tools and solutions available on the market.

Access the paper at Electronic Theses for Schreyer Honors College (ETDA) website here.

Managing High-End Ex-Demonstration Product Returns

By L. Muyldermans, L. Van Wassenhove, and V. Daniel Guide📧

In European Journal of Operational Research, 2019, 277:195–214. https://doi.org/10.1016/j.ejor.2019.02.031

Some manufacturers demonstrate their products so that customers can gain experience before making a purchase. We present a novel application of a closed-loop supply chain where product returns from demonstrations of high-end IT equipment are substantial and the major delay in the system is due to the long demonstration time at the client sites. In addition, the product lifecycle is short and the value erodes rapidly over time, with steep drops in the resale revenue when new product generations are introduced. We present a finite lifecycle model that captures the key trade-offs in this environment, that is, either to reuse a collected ex-demo product for a next demonstration or to salvage its residual value in the secondary market and use a new product to satisfy the next demo request. We derive two cost/revenue signals that enable us to distinguish between fast and slow value erosion. We show that the fast/slow erosion decision is dynamic and depends on the rate of value erosion and the length of the demonstration time. We analyze the optimal demo pool strategies and show that in the case of fast erosion it may be better to postpone reuse activities until later in the lifecycle. We illustrate our model using empirical data from a large IT manufacturer and formulate several guidelines so as to better manage high value ex-demonstration product returns.

Keywords: Supply Chain Management; Reuse closed-loop supply chains; Ex-demonstration product returns; Time-sensitivity; Continuous time transportation problem application

Selling Assortments of Used Products to Third-Party Remanufacturers

By A. Mutha, S. Bansal📧, and V. Daniel Guide📧

In Production and Operations Management, 2019, 28 (7): 1792–1817. https://doi.org/10.1111/poms.13004

This paper analyzes the business‐to‐business transactions in which a supplier sells assortments of used products to third‐party remanufacturers. The supplier offers used products in different quality conditions, called grades. We model this buyer–supplier transaction as a Stackelberg game in which the buyer chooses his optimal purchase quantity of various grades, and the supplier chooses the optimal assortment and the prices of the grades in the assortment anticipating buyer’s behavior. We first develop an analytically tractable solution to the buyer’s and supplier’s problems. Subsequently, we show several structural properties of the optimal assortment offered by the supplier, including (i) the optimal prices set by the supplier are such that high quality grades have a higher profit margin for the buyer; and (ii) the grades in the optimal assortment constitute a convex hull of the remanufacturing and acquisition costs. We also extend the results to the case when the supplier’s acquisition costs are marginally increasing in the quantity acquired.

Keywords: Used products; Quality levels; Inter‐firm transactions; Pricing; Assortment planning

Product Life Cycle Data-Set: Raw and Cleaned Data of Weekly Orders for Personal Computers

By J. Acimovic📧, F. Erize, K. Hu, D. J. Thomas, and J. Van Mieghem

In Manufacturing & Service Operations Management, 2019, 21 (1): 171–176. https://doi.org/10.1287/msom.2017.0692

We provide and describe a data set of N=8935 weekly, normalized customer orders over the entire product life cycle for 170 Dell computer products sold in North America over a three and a half year period, from 2013-2016. Total orders for these products exceeded 4 million units and well over a billion dollars in revenue. While Dell is historically known for fulfilling customer demand with a build to-order approach, the products in this data set were designated as build-to-stock products. There are three elements in the data that, depending on the research application, researchers may want to identify or mitigate. First, some products have seemingly anomalous orders representing one-time purchases from large customers. Second, there are negative values for some products representing order cancellations. Third, end-of-life sales may be significantly influenced by management action. We present approaches for cleaning the data to address these issues.

Keywords: Empirical research; Personal computers; Product life cycles

Forecasting Product Life Cycle Curves: Practical Approach and Empirical Analysis

By K. Hu, J. Acimovic📧, F. Erize, D. J. Thomas, and J. Van Mieghem

In Manufacturing & Service Operations Management, 2018, 21 (1): 66–85. https://doi.org/10.1287/msom.2017.0691

We present an approach to forecast customer orders of ready-to-launch new products that are similar to past products. The approach fits product life cycle (PLC) curves to historical customer order data, clusters the curves of similar products, and uses the representative curve of the new product’s cluster to generate its forecast. We propose three families of curves to fit the PLC: Bass diffusion curves, polynomial curves and simple piecewise-linear curves (triangles and trapezoids). Using a large data set of customer orders for 4,037,826 units of 170 Dell computer products sold over three and a half years, we compare goodness-of-fit and complexity for these families of curves. Fourth-order polynomial curves provide the best in-sample fit with piecewise-linear curves a close second. Using a trapezoidal fit, we find that the PLCs in our data have very short maturity stages; more than 20% have no maturity stage and are best fit by a triangle. The fitted PLC curves of similar products are clustered either by known product characteristics or by data-driven clustering. Our key empirical finding is that, for our large data set, data-driven clustering of simple triangles and trapezoids, which are simple-to-estimate and explain, performs best for forecasting. Our conservative out-of-sample forecast evaluation, using data-driven clustering of triangles and trapezoids, results in mean absolute errors approximately 2-3% below Dell’s forecasts. We also apply our method to a second data set of a smaller company and find consistent results.

Keywords: Forecasting; Product Life Cycle; Dell; Clustering

A Typology of Remanufacturing in Closed-Loop Supply Chains

By V. Daniel Guide📧 and J. Abbey

In International Journal of Production Research, 2018, 56 (1): 374–384. https://doi.org/10.1080/00207543.2017.1384078

This manuscript defines a typology of remanufacturing based on multiple decades of direct observations across various remanufacturing industries. The manuscript also details how managers adapt their remanufacturing operations and strategies to the idiosyncrasies of the varied remanufacturing industries. The typology identifies four distinct typological groupings based on the dimensions of a firm’s strategic focus and product design philosophy. Before delving into typology and implications on strategic and design issues, the manuscript provides recent information on the current state of the remanufacturing industry based on governmental and industry reports. To assist readers who may be less familiar with the remanufacturing industry and closed-loop supply chains, the discussion also provides a brief overview of remanufacturing processes and the overall remanufacturing industry.

Keywords: Closed-loop supply chains; Sustainability; Remanufacturing; Green manufacturing; Green supply chain; Typology

Visual Recognition in the Supply Chain: A New Age of Logistics

By Matthew Garrity, supervised by Lauren Bechtel (2018)

Visual Recognition is the ability for software, through deep machine learning, to identify objects, people, text, actions, and places in real time (gumgum).  Visual recognition, considered a subset of Artificial Intelligence, is already in use in a wide array of fields from the mundane to the highly complex. Augmented Reality (AR) takes Visual Recognition one step further. AR allows each object to be identified by image recognition software and to be supplemented with additional useful information such as text, graphics, sounds, GPS, etc. The progress made on AR software solutions over the past ten years has also been significant. This paper explores what technology currently exists and how logistics companies have leveraged the technology for business advantages.

View the document here.

Suggested citation

Garrity, Matthew. 2018. “Visual Recognition in the Supply Chain: A New Age of Logistics.” Student project paper, supervised by Lauren Bechtel, Center for Supply Chain Research® (CSCR®), The Pennsylvania State University.

Impact of Compound and Reduced Risk Specification on Valuation of Projects with Multiple Risks

By S. Bansal📧 and Y. Rosokha

In Decision Analysis, 2018, 15 (1): 27–46. https://doi.org/10.1287/deca.2017.0358

Several firms make business decisions based on risk specifications or estimates provided by domain experts. But research on whether the format of risk specifications systematically affects decision making in multidimensional environments is scarce. Using laboratory experiments, we show that subjective valuations of projects with multiple risks are highly sensitive to the format of risk specification. In the experiments, participants considered a project that had two risks and would be considered a success when favorable outcomes occurred on both risks. Participants were provided with the probabilities of success for each risk in two different specifications. In the reduced specification, each probability was directly specified. In the compound specification, each probability was specified as a two-point distribution. The data showed that under the reduced specification, decision-makers’ perceived value of the project was higher than its true value, because of conjunctive probability bias in which decision makers overestimated the conjunctive probabilities. Under compound specification, however, judgmental valuations were subject to two biases that acted in opposite directions and, as a net outcome, managerial valuations were closer to risk-neutral valuations.

Keywords: Managerial valuation; Judgmental biases; Reduced and compound risk specifications; Multiple risks; Experiments

The Challenges of Applying Lean Principles to Smart Manufacturing

By James Nathaniel Rusack, supervised by John Jordan📧 (Thesis Supervisor) and John C. Spychalski📧 (Honors Advisor) (2018)

This paper seeks to define and evaluate a set of important challenges that companies must solve to effectively implement smart manufacturing with a lean perspective. It is a qualitative analysis utilizing past research papers, business principles, and logic to decide which challenges are the most difficult to solve, as well as providing potential solutions to these problems. These challenges are managing big data and machine learning, managing the data security of the smart factory, implementing new technologies within ERP programs, and managing the data quality of the operation. There is a particular focus on managing data quality and data security as pressing concerns for a lean implementation of smart manufacturing. Additionally, the paper features cultural change as a major strategic element for defeating the technical challenges. Ideally, manufacturing companies will be able to use this paper to evaluate the current risks and benefits of implementing smart manufacturing in their operations while considering a lean perspective and will have a general guide of the areas for additional quantitative research to test the validity of these challenges and solutions.

Access the paper at Electronic Theses for Schreyer Honors College (ETDA) website here.