Open Access Open Access  Restricted Access Subscription or Fee Access

IoT's Potential to Measure Performance of MHE in Warehousing

Dr. Ali Kamali

Abstract


KPI is the key tool used by an organization to determine progress towards strategic goals and objectives, and to fortify factors deemed crucial to the success of the organization, and it is used as an indicator of the performance level of employees and department. However, the existing KPI methods being implemented by organizations have many disadvantages and not suitable to all types of operations, and not even able to produce the expected results, along with discouraging employees to consider innovative approaches in their work since the same procedures and metrsics are enforced every year. In some departments, such as the warehouse, the situation is different since the performance is measurable in a number of ways, such as operations, fulfillment and stocking, and each of them depends on the type of equipment used by workers. To this end, this paper focuses on the performance of Material Handling Equipment (MHE) in a warehouse through considering the new technology that enables to increase efficiency of warehousing, and that is Internet of Things (IoT). One of the main characteristics of IoT is the smart sensor, which enables to measure and evaluate a detailed understanding of equipment performance through instrumentation and analytics, and such the tool is very important to improve the warehousing efficiency. In order to understand the importance of IoT in warehousing, a comparative analysis is conducted between the well known and eminent the OEE KPI and the IoT technique to find out how the new technology is able to change the performance of MHE in a warehouse for better, taking into consideration the main metrics and conditions used to measure performance of MHE, and the forklift equipment is selected as an analytical case study. Finally, this paper points out the future directions in the IoT technology in the business environment.


Keywords


Internet of Things IoT, Key Performance Indicator KPI, Overall Equipment Effectivness OEE, Material Handling Equipment MHE, Warehousing, Technology Trends in Supply Chain.

Full Text:

PDF

References


13306, SS-EN (2010). Maintenance–Maintenance terminology. 2nd ed., Stockholm, Swedish Standards Institute.

BALLOU, R. H (1993). Logística empresarial. Sao Paulo, Atlas.

BANKER, Steve (2015). The Key Challenge For The IoT-Enabled Warehouse Will Be Execution. [online]. https://www.forbes.com/sites/stevebanker/2015/07/07/the-key-challenge-for-the-iot-enabled-warehouse-will-be-execution/#6a936ca15442

BARTHOLDI III JJ, Hackman ST (2006). Warehouse and distribution science. [online]. www.warehouse-science.com

BULENT D., Tugwell P., Greatbanks R. (2000). Overall equipment effectiveness as a measure of operational improvements. A Practical Analysis, Internal Journal of Operation & Production Management, 20 (12), 1488-1502.

CASTALDI, Chris (2016). IoT in The Warehouse. [online]. https://www.manufacturing.net/article/2016/07/iot-warehouse

CHIBUYE, M., & Phiri, J. (2017). A Remote Sensor Network using Android Things and Cloud. International Journal of Advanced Computer Science and Applications, 8 (11),.

E, Frazelle (2001). World-class warehouseing and material handling. New York , McGraw-Hill.

EMMENEGGER, Patrick (2010). How good are your counterfactuals? Assessing quantitative macro-comparative welfare state research with qualitative criteria. [online]. http://www.compasss.org/wpseries/Emmenegger2010b.pdf

G, Rihoux B and De Meur (2009). Crisp-set qualitative comparative analysis (csQCA). In: Rihoux B and Ragin CC (eds) Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques. London, Sage.

GEORGE, Alexander L. & Bennett, Andrew (2005). Case studies and theory development in the social sciences. Cambridge: MIT Press..

GONG Y, De Koster MBM (2008). A polling-based dynamic order picking system for online retailers. IIE Transactions, 40, 1070–1082.

GROOVER, M. P. (2001). Production Systems, and Computer-Integrated Manufacturing. 2nd ed ed., New Jersey, Prentice-Hall.

J., Ericsson (1997). Distribution analyis, An important tool in lean production. Lund University, Department of Productions & Materials Engineering.

JAMES M., and Michael C., (2015). By 2025, Internet of things applications could have $11 trillion impact. [online]. https://www.mckinsey.com/mgi/overview/in-the-news/by-2025-internet-of-things-applications-could-have-11-trillion-impact

M., Bengtsson (2004). Condition based maintenance systems: An Investigation of Technical Constituents and Organizational Aspects. Licentiate Theses , Eskilstuna, Mälardalen University.

M.T, Larsson (2010). A model for material handling improvement when using automated storage system: a case study. 26 (2),.

MAESTRINI, V., Luzzini, D., Maccarrone, P., & Caniato, F. (2017). Supply chain performance measurement systems: a systematic review and research agenda. Internation Journal of Production Economics, 183, 299-315.

NAGYOVA, A., & Pacaiova, H. (2009). How to build manual for key performance indicators - KPI. [online]. http://dx.doi.org/10.2507/daaam.scibook.2009

PARMENTER, D (2015). Key Performance Indicators - Developing, Implementing, and Using Winning KPIs. WILEY.

PASUPULETI, Vasu (2017). The Importance of Business and Performance KPIs for IoT Applications. [online]. https://www.appdynamics.com/blog/news/the-importance-of-business-and-performance-kpis-for-iot-applications/

POVEDA, A. C. (2013). Qualitative comparative analysis: an application for industry. Qual Quant, 47 (1), 315–321.

R.M., Williamson (2006). Using Overall Equipement Effectiveness: the Metric and the. [online]. http://www.swspitcrew.com

RIHOUX, B., Ragin, C. (2009). Configurational comparative methods. Qualitative comparative analysis (QCA) and related techniques. CA, Sage.

SCHNEIDER, Carsten Q. & Wagemann, Claudius (2007). Qualitative Comparative Analysis (QCA) und Fuzzy Sets. Opladen Germany, Verlag Barbara Budrich.

SCHOLZ, Nigel Simister and Vera (2017). QUALITATIVE COMPARATIVE ANALYSIS (QCA). [online]. https://www.intrac.org/wpcms/wp-content/uploads/2017/01/Qualitative-comparative-analysis.pdf

SUJONO, S. and LASHKARI, R.S. (2007). A multi-objective model of operation allocation and material handling system selection in FMS design. International Journal of Production Economics, 105, 116–133.

THOMAS, Paul (2010). OEE at Teva: Leveraging the Simplest KPI. [online]. https://www.pharmamanufacturing.com/articles/2010/014/

TRAB, S., Bajic, E., Zouinkhi, A., Thomas, A., Abdelkrim, M. N., Chekir, H., & Ltaief, R. H. (2017). A communicating object’s approach for smart logistics and safety issues in warehouses. Concurrent Engineering, 25 (1), 53-67.

WHITEHOUSE, Stephen (2018). The KPIs within warehouse management systems that you should know about. [online]. http://www.winman.com/blog/key-kpis-within-warehouse-management-systems-that-you-should-know-about


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.