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Service Kaizen through Lab-forming Field & Field-forming Lab

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Getting both “results” such as POS data and "processes" including spatio-temporal data on human behavior and environmental stimuli and constraints in an actual service field, it makes the field virtually tangible. Such tangibility must be a key driver not only for understanding what happened there and why it happened more comprehensively, but also for predicting what will happen to facilitate service kaizen.
The virtual tangibility can be realized by technologies and methodologies that support the idea of "Lab-forming Field" and "Field-forming Lab" such as IoT (Internet of Things), WoT (Web of Things), and MR (Mixed Reality) encompassing VR (Virtual Reality), AV (Augmented Virtuality), and AR (Augmented Reality).
This talk will present several case studies on service kaizen assisted by this kind of framework while introducing the technologies and methodologies we have developed and applied to the actual cases.

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Service Kaizen through Lab-forming Field & Field-forming Lab

  1. 1. Service Kaizen through Lab-forming Field & Field-forming Lab Takeshi Kurata1, 2 1 Human Informatics Research Institute, AIST, Japan 2University of Tsukuba, Japan E-mail: t.kurata@aist.go.jp
  2. 2. AR by PDR + Image-based registration Panorama-based Annotation, ISWC2001, ISMAR2003など G Environmental map A B C D E A B C F Input frames Position at which a panorama is taken Position Direction 235 [deg] 5 [deg] From the user’s camera Located Orientated 2
  3. 3. Takeshi Kurata, Ph.D. • Position:  – Research Group Leader, Service Sensing, Assimilation, and Modeling  Research Group, Human Informatics Research Institute, AIST – Professor (Cooperative Graduate School Program), Faculty of  Engineering, Information and Systems, University of Tsukuba • Professional Experience: – 2011‐2014 Doctoral co‐supervisor, Joseph Fourier University, UJF‐ Grenoble 1, France – 2012‐ ISO/IEC JTC 1/SC 24 Member – 2003‐2005 Visiting Scholar, HIT Lab, University of Washington • Education: – 2007 Ph.D. (Eng.) from Doctoral Program in Graduate School of  Systems and Information Engineering, University of Tsukuba – 1996 M.E. from Doctoral Program in Engineering, University of Tsukuba • Research Interests: – Service Research, Assistive technology, Wearable/Pervasive Computing,  Mixed and Augmented Reality, Computer Vision 3
  4. 4. Lab-forming Field & Field-forming Lab • Borrowing from “Terraforming” • Lab-forming Field: Transforming a real field into a lab-like place. (IoT) • Field-forming Lab: Transforming a laboratory into a field-like place. (VR) 4
  5. 5. 5 測って図る Measure Weigh Survey Hakaru Hakaru Plan Design Attempt
  6. 6. Constructing big data structure with spatial/behavioral information 6
  7. 7. ASPR Technologies for Multi-Stakeholders 7
  8. 8. CSQCC (Computer-supported QC Circle) 8 Staying-time rate at each dinning area per personSales at each dinning area per employee Visualization tool combining human-behavioral and accounting history Employee taking order while cleaning up the guest room Icons showing the number of customers at each table POS data log Service Characteristics 1. Intangible 2. Heterogeneous 3. Inseparable 4. Perishable Alleviate the issues due to IHIP
  9. 9. QCC in manufacturing industry Purpose: Productivity improvement Conventional QCC in service industry Purpose: Productivity improvement Subjective QCC in service industry Purpose: Improvement of CS/ES w/ reasonable ways to gather objective data in plants In 1980s, applying QCC for service industry w/o reasonable ways to gather objective data in service fields In 1990s, Service industry lost interest in QCC In 2000 QCC in the Service Industry in Japan 9 Computer-supported QCC (CSQCC) Purpose: Productivity improvement In 2010 CSQCC in the future Productivity improvement Improvement of CS/ES w/ reasonable ways to gather subjective data continuouslyw/ reasonable ways to gather objective data in service fields 1950~ Deming Award
  10. 10. 3rd CSQCC for newly open (Movie) 10 新宿・山野愛子邸 2014.12.23
  11. 11. Case study in Japanese Restaurant “Ganko” • Objectives 1. (for AIST) to test the CSQCC (Computer-Supported QCC) suites in a real service field. 2. (for the restaurant) to observe effects of process improvement planned by CSQCC. • Place – Japanese cuisine restaurant GANKO Ginza 4-chome (Tokyo) • Term – 1st term • January 12 to 18, 2011 – 2nd term • February 3 to 9, 2011 11 Dining area Course dishes 1st term (Jan. 12-18, 2011) for observing ordinary operations QC circle for making improvement plans 2nd term (Feb. 3-9, 2011) for observing improved operations
  12. 12. 12 B2 B1 Dinning Area Kitchen Office room Pantry
  13. 13. During Discussion in CSQCC 13 Trajectory of a wait staff in lunch time: 12:00-14:00 Fact: Going in and out of the kitchen/office to no small extent. Possible result: Difficulty in concentrating on guest service. Cause: Cell phone everywhere, but reservation book only in the office room. Possible improvement: e-reservation book Dinning Area Kitchen Office room
  14. 14. Summary of 1st CSQCC for Wait Staff 14 Grasp of actual condition Shorter stay in dinning area than the manager assumed Kaizen plan development (1) Re-composition of service processes (SP) (2) Thoroughly obeying each division’s roll, (3) Guts Direct effect Stay ratio in dinning area at dinner time: UP ↑ Spillover effect Number of additional orders at dinner time: UP ↑ Side effect (Trade-off) (1) Work load (walking distance): No difference → (2) Number of additional orders at 3pm: No difference → Stay ratio in dinning areas 30% 35% 40% 45% 50% 55% 11 12 13 14 15 16 17 18 19 20 21 22 Walking Distance [m] 1,000 1,500 2,000 2,500 11 12 13 14 15 16 17 18 19 20 21 22 Num. of additional orders per customer 0.0 0.4 0.8 1.2 11 12 13 14 15 16 17 18 19 20 21 22Hour Hour Hour Before After Down: Due to SP re- comp. for preparation of dinner/party UP: Much more than time decreased in Tea hour No diff.: Due to no SP re-comp. No diff.: Despite SP re-comp. for preparation of dinner/party UP: due to reduction of opportunity loss No diff. on workload Lunch Tea Dinner Lunch Tea Dinner Lunch Tea Dinner
  15. 15. 2nd CSQCC: Keep your zone! 15 Jan-Feb in 2012 Actions Description 1 Stay longer in the dining area Waiting staff should stay longer in the dining area to serve their customers. 2 Reduce the movement Waiting staff should reduce their movement. 3 Keep your positions Waiting staff should keep their positions (Zones). They should not undertake jobs of other zones and should do their jobs in their zones.
  16. 16. Walk distance of waiting staff per customer (meters / hour / person) 16 *** * p < .05, ** p < .01, *** p < .001 ****** They were able to reduce walking distance while not reducing staying time in the dining area!
  17. 17. Indicators for position keeping 17 B2 B1 Zone Dedication Rate=Orange/Red Zone Order Defense Rate =Orange/Blue All of orders in the staffʼs zone # of accepted orders by a staff in the staffʼs zone The total # of accepted orders by the staff
  18. 18. Relation between skill level and Zone Defense/Dedication 18 IV. Expert They take all of orders in their zone while taking orders in other zone for helping others. II. Fully occupied They take orders in his/her own zone but it is not enough for covering the zone. Support by other staffs is needed. III. Well organized They take all of orders in his/her zone, but they don’t help other zones. I. Purposeless They fail to take orders in his/her zone and take orders in other zones. Training is required. Zoneorderdefenseratio(ZOD): Theratioof#ofacceptedordersbyastaffin his/herownzoneoutofallofordersinthezone Zone dedication ratio (ZD): The ratio of # of accepted orders by a staff in his/her own zone out of the total # of accepted orders by the staff Precision individual skill Teamwork performance
  19. 19. Before 19 Precision
  20. 20. After 20 Improved coverage of each zone by each staff Less need for helping other staffs (zones) Precision
  21. 21. So many kinds of positioning methods 21
  22. 22. In the year of 2010 • iPhone 4: the first popular consumer mobile device equipped with 9-axis sensors including accelerometers, magnetic sensors, and gyro sensors 22 G-spatial EXPO 2010: Handheld PDR (Pedestrian Dead Reckoning) on iPhone 4 (Maybe world’s first-ever live demo)
  23. 23. PDR(Pedestrian Dead-Reckoning) Estimates velocity vector, relative altitude, and actions by measurements from waist-mounted sensor module.  Wearing sensor module on waist  Easy to wear and maintain  Easy to measure data for action recognition  Relatively easily apply for handheld setting compared to shoe-mounted PDR based on Zero Velocity Updates (ZUPTs) 23 Handheld PDR From PDR to PDRplus 10-axis sensors • Accelerometers • Magnetic sensors • Gyro sensors • Barometer
  24. 24. Frontier of PDR: Walking direction estimation 24 • Tutorial: Personal Navigation with Handheld Devices by Valerie Renaudin, IPIN 2015.
  25. 25. Frontier of PDR: Walking direction estimation 25 • Tutorial: Personal Navigation with Handheld Devices by Valerie Renaudin, IPIN 2015. • Long Paper: Christophe Combettes, Valerie Renaudin, Comparison of Misalignment Estimation Techniques Between Handheld Device and Walking Directions, IPIN 2015. • FIS was proposed by Kourogi and Kurata in PLANS 2014. “Globally, the FIS method provides better results than the other two methods.” Frequency analysis of Inertial Signals Forward and Lateral Acc. Modeling Principal Component Analysis
  26. 26. Power-Aware PDR + Bluetooth LE • Sensor module with PDRLE (power-aware PDR chip) + BLE • Towards total management system for attendance record, work/collaboration support, work analysis, and human-resource developments based on name-card-like devices 27
  27. 27. Indoor Pedestrian Positioning Using SDF (Sensor Data Fusion) Pedestrian Dead-Reckoning (PDR) ID reader ID RSSI Acceleration / angular velocity Building Structure/Layout Magnetic vector Magnetometer Output of position/orientation Positioning based on stationary and mobile nodes Atmospheric pressure Barometer Trajectory Sensor/Data Fusion (SDF) (Particle filter) Accelerometers / gyro-sensors Walking velocity Position / Orientation Trajectory matching/ Velocity estimation Absolute position 3D environment model Velocity vector / Relative altitude / Action type Sensor module Active RFID tagID Surveillance camera/ RGB-D sensor ID-LED ID Video/ Depth
  28. 28. Behavior Measurement of workers at Nursing facility (Supercourt Hirano) 33
  29. 29. •Helper •Night shift Time flow: RYGSB 0~1hr 1~2hr 4~5hr 5~6hr 2~3hr 6~7hr 3~4hr 34
  30. 30. Time flow: RYGSB •Helper Leader •Night shift 0~1hr 1~2hr 4~5hr 5~6hr 2~3hr 6~7hr 3~4hr 35
  31. 31. 40 60 80 100 40 60 80 100 40 60 80 100 • Nurse R: Role as a leader. Mainly desk work and sometimes vital check of residents. • Nurse S: Taking care of each resident while relatively flexibly circulating. Care worker E, I, K Care worker D, H, MCare worker A, G • Flexibly changing the role? • Or low skill? • High skill? • Or assigned at specific floor? • Mainly desk work? # of steps # of utterance (VAD) # of floor change Time spent in residents’ rooms Nurse R Nurse S # of steps # of utterance # of floor change Time spent in residents’ rooms # of steps # of utterance # of floor change Time spent in residents’ rooms Voice Activity Detection (VAD) FrequencyLow High RestroomBath/Dressing roomResidents’ rooms Corridor Nurse Station Stairs/EV Dining room Work Analysis in Nursing Home Validation of the hypotheses on what is related to high skills: e.g. ‘Workers who are skillful at comprehensive awareness is to talk to residents frequently everywhere, but each conversation is basically short.’ 36
  32. 32. Interview with FPV Passage of Time + Over 50% cost reduction on labor cost and preparation time compared with existing time studies + Consideration of customer privacy by not using cameras + FPV with less motion sickness + Effective in episodic memory retrieval for retrospective interviews considering bounded rationality Worker’s trajectory 3D model built from a set of photos First-person view (FPV) CCE (Cognitive Chrono-Ethnography) Lite Japanese-style hotel at Kinosaki Onsen (hot spring) 37
  33. 33. Pre-evaluation of Kaizen Plan Considering Efficiency and Employee Satisfaction by Simulation Using Data Assimilation -Toward Constructing Kaizen Support Framework - 40
  34. 34. Results of comparison between the actual plan and Kaizen plans by simulation 41 We can find Kaizen plans which achieve both Efficiency (Ef) and Employee Satisfaction (ES) by behavior measurement, modeling, and simulation.
  35. 35. Open Data Contest in Logistics & PDR Challenge in Warehouse 42
  36. 36. Service Field Simulator •Supporting service design using VR technology – Evaluating service environment and its process in advance by sensing and analyzing human behavior in virtual environment Risk reduction by evaluation of the new service in advance comparison between • current layout and new layout plan • current process and new process Acquiring more detail and reliable data • Various sensors are available because of limited sensing area • Easy to control the condition As is New plan With EEG With Eye-Tracker 43
  37. 37. Simulators for layout and service process evaluation in advance • Retail store simulator for marketing  evaluation of package design in-store situation  some benefit on cost and flexibility  prevent to leak new package designs VR Drugstore for marketing, Kimberly-Clark Inc. × Insufficient scientific basis for reproducibility compared with real environment 44
  38. 38. Simulators for layout and service process evaluation in advance •ServLab: – Simulator as service theatre where professional actors play some roles of customer and employee to review possible situation 45
  39. 39. Design concept of SFS •Keep sense of direction as well as the real  small and easy to provide immersiveness HMD Full solid angle display  Ideal display condition × very complex and need big space △ Keep sense of horizontal direction  Simple structure (easy to construct)  wide field of view  natural to see holding real objects Fully omni-directional display × narrow field of view, low resolution × eye fatigue × unnatural to see holding real objects × latency from head motion to CG rendering 46
  40. 40. Design concept of SFS •imitate the way to move in real fields: – control virtual viewpoint by walking motion •hands free: test service process with real tools •evaluation of physical load to move around Omni-directional treadmill  very similar to real motion × required to get used to control × initial cost  Easy and Intuitive action for users  Lower initial cost △ have to develop robust detection method Walking-in-place motion detection 47
  41. 41. Continued improvement SFS Ver. 1.0 • low resolving power: 0.2 • short of vertical FOV SFS Ver. 2.0 24 Full-HD(1920x1080) 27-inch LCD : Resolving power is improved to 0.7 SFS Ver. 2.1 40 Full-HD 24-inch LCD : Vertical FOV is improved (Upper 35°, Lower 58.5°) 48
  42. 42. Case studies for verifying efficiency •Gaze point analysis using combination of eye- tracking device and SFS – Hypothesis •we can do the same investigation using an eye-tracker and the SFS as real in-store marketing in-store marketing experienced person(subjective opinion): "the motion of the gazed point in the virtual environment is similar to that in the real store especially from the entrance to in front of the shelf where target products are layout" 49
  43. 43. Case studies for verifying efficiency •Investigation for a method for measuring human interest using EEG and the SFS 50
  44. 44. Example of Analysis and Future Work 51 To compare the shopping behavior in detail, we made heat-map visualization of the stay time for each 50 cm grid in the real and virtual store. The read area indicates subjects spent longer time than other area. Because position data of the real store situation is recorded by hand, we only have the discrete position and timestamp data. Therefore, we could not compare both of them strictly, but we found out we could get the similar results (Figure 9). comparison of heat-map visualization of stay time between in the real store (left) and in the virtual store (right)
  45. 45. Abstract • Getting both “results” such as POS data and "processes" including spatio-temporal data on human behavior and environmental stimuli and constraints in an actual service field, it makes the field virtually tangible. Such tangibility must be a key driver not only for understanding what happened there and why it happened more comprehensively, but also for predicting what will happen to facilitate service kaizen. • The virtual tangibility can be realized by technologies and methodologies that support the idea of "Lab-forming Field" and "Field-forming Lab" such as IoT (Internet of Things), WoT (Web of Things), and MR (Mixed Reality) encompassing VR (Virtual Reality), AV (Augmented Virtuality), and AR (Augmented Reality). • This talk will present several case studies on service kaizen assisted by this kind of framework while introducing the technologies and methodologies we have developed and applied to the actual cases.52

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