Slaying the Inventory Dragon with the Assistance of Analytics

Dean S. Williams, Manager, Measuring & Test Equipment, Duke Energy, Huntersville, NC, USA

Keywords: Inventory, Analytics, Optimization

Industry: Energy

Level: Intermediate


This presentation is a case study documenting an inventory optimization pilot project that used analytics as a key to identifying and managing inventory levels for a measuring and test equipment program.

The vast majority of inventory optimization projects have been in the manufacturing arena, where the inventory being addressed involves the product line consumables. This project case study differs in that it looks at another category of inventory; the inventory of facilities, equipment, and tools used to produce the product or service we offer. This second category of inventory, due to not being seen as a process consumable, is often overlooked when dealing with the inventory issue. Ironically however, this second category of inventory may have as big if not a bigger impact on operational effectiveness.

The reason is two fold: First, if not available in sufficient quantities it can directly impact the ability to produce a product or service. Second, this type inventory represents not only the “inventory” waste as defined by LEAN it can also involve the “Over Production” waste. There is not only the challenge of managing the acquisition cost and stock level of this type of inventory, but this second type of inventory involves ongoing operating and maintenance costs. If there is excess inventory it means you are expending production resources (over production) and incurring the associated cost to maintain this unnecessary inventory. A simple illustration is machines on a production line. If you do not have enough of those machines you may not be able to meet your production goals. However, if you have an excess of those machines, you are not only incurring the acquisition cost but also the on-going maintenance cost and other carrying cost for a piece of equipment that is not being used.

This particular case study about this second type of inventory involves measuring and test equipment (M&TE) that is used to support the production of electricity via nuclear power plants. The nuclear industry requires the control of M&TE as specifically mandated by Title10, Part 50, Appendix B of the Code of Federal Regulation. Other industries may have similar regulatory requirements, or just a recognized commercial need to have accurate M&TE to ensure quality standards. Whatever the basis for the M&TE program, it involves inventory costs.

This case study follows the process of developing an M&TE inventory optimization pilot project using analytics as a key component of the effort. This case study explores the steps that were involved with the process and results obtained consistent with the DMAIC process as follows:

       Defining the issue in terms of potential waste associated with supporting excess inventory as well as defining the stakeholders and processes that would be affected. Currently available data provided a starting point as to total inventory in circulation, the average per unit cost to acquire and maintain, and who the affected stakeholders were. The inventory within scope involved over 16,000 unique items involving over 2,000 types and models, used at 6 different operating facilities supporting a half dozen different groups at each facility.

·        Defining the requirements of the analytics software to support the effort was also a key first step in the process. The software had to have certain capabilities and had to work in conjunction with an existing software database being used to manage the current process.

       Measuring the impact in terms of current and potentially averted costs and impact on stakeholders. This step required the first use of analytics to determine detailed usage patterns during both on-line and off-line periods and out of service times for the equipment when it was at the calibration lab being serviced.

       Analyzing the variations in the usage and out of service times was a key to estimating optimum inventory levels. The analysis looked at both these factors for the most potential gain if variations could be minimized. In addition, it also looked at the risk associated with current and forecasted inventory levels. Again, having the right analytics was a key to this step in the process.

       Improve the process by optimizing the inventory. Reducing where excesses existed and increasing where vulnerabilities were identified. Improving also involved the practical aspect of implementation that includes selling the concept and having an effective change management plan that addressed potential concerns on the part of stakeholders.

  •        Control the process by establishing and monitoring meaningful metrics, including inventory level control limits. The analytics provided the ability to determine the relative risk associated with different stocking levels for each type and model of M&TE in the inventory across different operating conditions (on-line versus maintenance periods) based on the control limits that were set. Controls were also established for how to address potential “stock outs” (i.e. unavailability of a particular item).

The case study concludes by summarizing initial estimates of cost savings, which were obtained during the pilot project, lessons learned from the implementation and control phase of the pilot project, and plans for full implementation with the expected benefits and challenges.

Participants will come away from the presentation with:

       A sense of the potential impact tool and equipment inventory has on overall operating costs and the benefits of properly addressing that inventory.

       A road map for developing and implementing a process improvement initiative focused on optimizing inventory associated with tools and equipment.

       An understanding of the roll analytics software can play in the effort and considerations for the selection of software to support that effort.