White Papers

Forward Pick Area Design and Operations
In a typical warehouse operation, the order-picking process consists of retrieving products from storage locations to fulfill customer orders. The process has received a great deal of attention from warehousing practitioners and researchers because it is generally the most labor-consuming operation in a warehouse, accounting for approximately 35% of a warehouse operating cost [19]. From a general point of view, there are two basic order picking systems, namely parts-to-picker, and picker-to-parts. In the former, some sort of automated system is used to bring the products to the picker (i.e., the picking operator), whereas in the latter, the picker travels along the aisles of the warehouse retrieving the required products from the storage locations. Among these two types of systems, it has been well documented that the largest proportion of order picking systems found worldwide belong to the picker-to-parts type [8, 17], probably because of the lower capital investment required and the flexibility and adaptability of human workers.
Cycle Time for Print-and-Apply Labeling Automation

When considering print-and-apply label automation systems for a top-applied label in shipping or e-Commerce operations, speed is obviously a critical component. Manufacturers of these systems are often asked to qualify their “cycle time” – the process by which the system prints a label, stages it for application, applies it to an item, and then returns to repeat the process, ad infinitum.
Adding Value to Manufacturing, Retail, Supply Chain, and Logistics Operations with Big Data Analytics
In this article, we first elaborate on the big data concept and present the storage and processing technologies that have been developed to deal with big data. We then briefly discuss the evolution of traditional analytical processing to today’s big data analytics. Through several applications and use cases, we illustrate how big data analytics is adding value to manufacturing, retail, supply chain, and logistics operations. Finally, we conclude by discussing key challenges that businesses have to face as the use of big data analytics becomes more widespread.
Cyber Security and Its Implication on Material Handling and Logistics
The frequency and financial impact of cyberattacks on businesses doubled in the last five years and expected to triple in the next five years. Cybersecurity breach poses a dynamic challenge to businesses and threatens their smooth operations and competitive advantage. This paper mainly focuses on challenges faced by cybersecurity and how businesses, especially the material handling and logistics should do to address those challenges.

Cybersecurity Questions for Major Stakeholders
During a recent MHI Solutions Community meeting, the cyber security topic was discussed with a panel of industry experts. The results of that discussion were a series of questions that all companies should consider to further protect from cyber security attacks.
Blockchain and Supply Chain Management
Many authors have explored the potential impact of blockchain on supply chain management, and indeed, many articles in the popular press extol the potential of blockchain to impact the supply chain. In this white paper, we argue that while blockchain does have some potential to impact supply chains in the short term, many of the potential blockchain-enabled supply chain impacts will require significant research advances.
Digital Twin for Intralogistics
As companies strive to meet customer demands, they are beginning to embrace the concept of Industry 4.0. With a goal of achieving smart factories, Industry 4.0 centers on increasing automation through the application of technologies including digital twin (DT) simulations, the Internet of Things (IoT), sensors, and advanced communication systems. A key component of this is the development of smart intralogistics.