Production Management: Material Handling Systems in Manufacturing Firms – Page 2

Automated Mobile Robots (AMRs) in Manufacturing Firms

Some manufacturers do not like the idea of lasers or wires, and guided paths, an aspect that has greatly facilitated the development of AMRs. Unlike AGVS, these systems utilize computer-based systems of vision to move in an industrial environment. In the words of Banerji, Ray, and Datta (2008), “the advantage of this type of robot is that existing manufacturing environment does not have to be altered or modified as in the case of conventional automatic guided vehicles where permanent cable layouts or markers are required for navigation” (Datta, Ray, & Banerji, 2008, p. 537). This robots present with the freedom to move throughout the facility without limitations, and perform the designated tasks.


Case Study

One of the most efficient and reliable material handling systems is the AVG. Egemin Automation is an example of manufactures who manufacture various models of the system including fork lift vehicles, tug vehicles, trailer loading vehicles, and unit load vehicles. In cases that necessitate, the company also creates AVGs that are custom made. Their in-house software and customizability makes the company a successful manufacture of AVGS. Companies pay up to $100,000 when purchasing an AVG unit from Egemin, with an excess of $25,000 in labor for every unit to setup the laser guiding system. However, the Kiva system has proven to be a breakthrough in the manufacturing of AVGS (Overfelt, 2006). The Kiva AVGS are quite distinguished from the traditional systems. Nevertheless, their applications are more directed towards distribution. These systems involve a coordination of numerous small robotics that move about under shelves, lift them and take them towards workers for easy accessibility. In order to coordinate such movement of numerous robotics around an organization, the system inventory is scanned and entered to each of the shelves. A cluster of servers is used to give instructions to each of the robots, and navigation within the manufacturing floor is facilitated by optical sensors within the robots, which recognize the floor (Overfelt, 2006).

Due to the high cost of labor picking, this system promotes cost-efficiency in a manufacturing company in distribution of material about the manufacturing floor (Overfelt, 2006). Various companies including Zappos, Staples, Gap, and Walgreens have installed this system in their centers of distribution and it has been a key factor resulting to efficiency and success. Companies that embrace this system in their manufacturing and distribution processes are bound to experience reduced costs of labor and increased competitive advantage. However, in relation to the AGVS, the AMRs have grown to be of more success in the manufacturing environment. One of the companies that manufacture these types of systems is Seegrid, with the pallet truck model and the tug model being the only available models. An example of the tug model is the GT3 tugger, which has the capability of pulling carts with a load of up to 3,000 pounds. On the other hand, the GT8 pallet truck has the capability of moving a pallet as heavy as 8,000 pounds. An interesting part of the technology included in the robots by Seegrid is one that allows the robots to keep the facility’s 3D map. Their technology of Industrial Mobile Robotics (IMR) also provides the robots with image processing, artificial intelligence, and methods of learning (Overfelt, 2006). As such, the AMRs have the ability to store delivery paths of up to 15 miles within the facility, which are learnt when the robots are walked through them by the operator. These systems prove to be efficient in a manufacturing environment as they can be programmed to facilitate the movement of different materials between different locations in a facility without being physically operated by operators, thus promoting time efficiency and reducing the cost of labor.

The GT3 AMR system has promoted productivity through allowing for easy and frequent change of route and facilitating flexibility at Daimler Trucks (Overfelt, 2006). Some of the exceptional results of the implementation of the system in the company are the reduction of the time taken waiting for parts by 22%, and the reduction of the total time taken in the transportation of parts to line by more than 30 minutes. In addition, inventory deficits were reduced by 98% and the need for inventory that is unnecessary was reduced by two-thirds. The systems significantly reduced the cost of labor through taking on the role of taking different parts onto the lines for processing, and taking of wastes and finished goods from the line. Having the capability to handle heavy loads, the machines also prove to be ideal for the bulky materials in manufacturing environments (Overfelt, 2006).


For a better understanding of the benefits of implementing AMRs or AGVS, it is important to consider areas that have shown improvement in environments that such material handling equipment has been implemented. Among others, the most evident outcome of implementing these systems in the companies including Daimler trucks is the reduction of the throughput time through ensuring that the period for manufacturing a single item is reduces to a great deal (Overfelt, 2006). In addition, these systems promote the reduction of manufacturing lead-time and the work in process. Another eminent outcome of the implementation of AGVS and AMRs is the reduction of job related injuries. Most injuries are due to movement within the facility and manual operation of equipment. However, with the automation of material handling equipment, most of the injuries that result from the processes handled by this equipment were mitigated. He cost of operation has also been noticed to be reduced in cases where the automated material handling equipment is implemented as compared to the manual operated machines. As such, as much as the cost for the AGVS and AMRs appears to be high, it was evident that the operation cost of the manual systems was high in the long-term due to the increased cost of labor across shifts (Siegwart, 2004, p. 119). Lastly, it was also evident that Daimler Trucks easily implemented their Just-In-Time (JIT) manufacturing business strategy with the help of the automated system through the reduction of the inventory by promotion of the use of only the needed items when they are needed.


It is important to note that these two types of automatic systems greatly increase the efficiency in transportation of materials from one station to the next within a manufacturing facility (Groover, 2010, p. 922). As such, the throughput time is reduced by enhancing the speed by which materials move through the process of manufacturing. Unlike human beings, these machines dispatch correct quantities of materials at programmed times to the workstations and thus are less likely to slow down the manufacturing process due to fatigue or other inconveniences. This increases the productivity of the facility as the output is increased due to an increase in the input. Through reduction of the manufacturing time for a single product and replacement of the manual workers who could be handling the same materials, the cost of labor and total cost of production is reduced thus, leading to an increase in the profit margin (Groover, 2010, p. 922).

Another aspect of importance is the reduction of injuries due to replacement of human labor with automatic material handling equipment (Sople, 2009, p. 12). The few employed individuals are mostly stationed at the control system and the workstations and their work involves minimal manual handling of the materials as most of the equipment has been designed to load and offload themselves at the workstations. As such, the likelihood of these individuals to develop work related injuries or illnesses are low. On the other hand, reduced injuries lead to reduced lost time, as most of the employees are not forced off work by injuries (Groover, 2010, p. 923). This further increases the productivity of such facilities.

Conclusion and Recommendation

Material handling is an activity that is both expensive and non-value adding as it accounts for up to 70% of the total cost of manufacturing of a product. In most cases, 55% of factory space, 25% of employees, and 87% of production time is dedicated to material handling in a typical manufacturing company. Manufacturing companies can employ AGVS in different areas within the facilities to promote productivity. These systems utilize guided paths in moving materials from station to station and thus increase efficiency. On the other hand, the AMRs can be employed as unlike the AGVCS, they do not depend on given paths for movement, but depend on computer-based visions. As much as these systems are expensive, they provide long-term advantages as they reduce on the cost of labor and idle time. Through embracing AGVS and AMRs, other measures including the lead-time, throughput time, and work in progress are bound to improve. It is evident that AMRs have not been well utilized in the handling of materials as most manufacturers opt for outsourcing of their operations for lower costs and deem the AMRs expensive. Nevertheless, it is important to note that the flexibility of these systems allows them to fit in any manufacturing environment and thus they provide for a perfect solution for material handling in the future.


  • Datta, S., Ray, R., & Banerji, D. (2008). Development of autonomous mobile robot with manipulator for manufacturing environment. The International Journal of Advanced Manufacturing Technology, 38(5), 536-542.
  • Groover, M. P. (2010). Fundamentals of Modern Manufacturing: Materials, Processes, and Systems. Hoboken, NJ: John Wiley & Sons.
  • Jeppsson, J. (2008). Emerging technologies. Industrial Engineer, 40(5), 58.
  • Overfelt, M. (2006, June 1). Slash your shipping costs. Retrieved from Fortune Small Business:
  • Siegwart, R. (2004). Introduction to Autonomous Mobile Robots. Cambridge, Massachusetts: The MIT Press.
  • Sople. (2009). Logistics Management. Delhi: Pearson Education India.
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