Dr. Robert E. Overstreet Dr. Matthew A. Douglas Dr. Benjamin T. Hazen Supply Chain Leader/Follower Interaction: Managing Change while Focusing on Enterprise Level Objectives Dr. Robert E. Overstreet Dr. Matthew A. Douglas Dr. Benjamin T. Hazen

Overview Background Methodology Phase 1: Case Study Phase 2: Instrument Development Phase 3: Collect/Analyze Quantitative Data Possible Implications Limitations and Future Research Conclusion

Background We are studying a $16B/Year enterprise that maintains, repairs, and overhauls military aircraft The organization employs nearly 40K people who are almost entirely civilian unionized labor Three Air Logistics Complexes (ALCs) Ogden, UT Oklahoma City, OK Warner-Robins, GA Each ALC has multiple Aircraft Product Lines

Background $16B Enterprise; 819 Aircraft; 383 Engines; 223,160 Commodity Exchangeables; 2.7M Supply Managed Items; 417 Delivered Software Products

Background Strategy - Maximum aircraft fleet readiness through the competitive priorities of cost, speed, quality, and safety USAF has reduced manning and budgets Two choices: “Accept the costs and reduce capability or change the way we do business” Electing the latter, the leadership instituted and is leading a large-scale supply chain administrative innovation now called “Art of the Possible”

Background What is Art of the Possible ? Phased approach to instituting Art of the Possible Phase 1: Set the basic foundation Phase 2: Ensured that the workers had the training, skills, and tools Phase 3: Disseminated the program “deep and wide” within the organization Phase 4: Buy-in through all levels

Background Previous study looked at the post-adoption constructs of acceptance, assimilation, and routinization as it contributed to eventual incorporation We conducted 77 in-depth interviews with all levels of employees at all three ALC with 7 APLs 31 hours of transcripts 571 pages Published an article in Business Horizons Recurring themes regarding leadership, empowerment, and performance lead to this study

Methodology Exploratory Sequential Mixed Method (Creswell, 2014) Phase 1: Qualitative data collection and analysis Phase 2: Instrument development Phase 3: Quantitative data collection and analysis

Phase 1 Pulled a representative sample from the 77 interviews based upon position (evenly matched each APL) Input into Atlas.ti for qualitative analysis Having conducted the interviews we had a good idea of the constructs that we would measure Reanalyzed the data to find qualitative support for the concepts included in Phase 2

Phase 1 Empowerment “…when you empower people that are out there doing the job, they feel like they can make a change, and they look for areas to say this might be better, and a lot of times, it is.” “… they empower their employees to try to resolve and come up with solutions to their problems or issues or whatever they have.”

Phase 2 Phase 2: Instrument Development Themes from Phase 1 along with literature were used to develop a survey instrument and theoretical model To the extent possible validated scales will be adapted to make them germane to the current study We will pre-test and pilot test before data collection Multi-theoretical lens Leader-Member Exchange Theory Organizational Support Theory Technology-Organization-Environment Theory

Phase 2

Phase 3 Phase 3: Collect/Analysis Quantitative Data Plan to visit each of the three APLs to administer hard copy surveys and explain the dyadic component First line supervisors will answer one survey with the referent being his or her team Multiple mechanics will answer a survey with the referent being his or her teammates The research team will code the data with each row representing one dyad Additional objective data will be added to represent APL and ALC outcomes Test the theoretical model

Possible Implications Better understanding the importance of leader-member exchange in operations management and the implication on overall performance is important Dyadic research with an ability to look across APLs and ALCs May help explain differing levels of maturity and performance

Limitations and Future Research Will only look at one enterprise (aircraft maintenance) Cross sectional data collection Future Research Test in other settings Test longitudinal effects of the relationships

QUESTIONS/COMMENTS