The Emerald Research Register for this journal is available at         The current issue and full text archive of this jou...
Re-engineering
demonstrated the ease of using Microsoft Excel for efficiently assigning construction
project managers with ...
BPMJ   admits over 113,000 patients annually. Eight of the System’s acute care hospitals and
       12 of the health clini...
Re-engineering
                                                                                            for reducing
  ...
BPMJ                  management was focused on providing a higher level of service while reducing
                      c...
Re-engineering
Route          Day                  Facility
                                                              ...
BPMJ   delivery and/or distribution problems (Driscoll and Sherali, 2002). In this effort, the
       traveling salesman p...
Re-engineering
Model A: shortest direct route            H-SE-W-NW-MC-K-FB-SW-H                 182 miles/259 min
        ...
BPMJ   salesman model is rerun. The other two routes (K-MC-NW-H and SF-SW-H) had ample
       slack time, and, for that re...
Re-engineering
                                                               Total hours
                                ...
BPMJ                                                                           Runs                              Total num...
Re-engineering
repairs, tolls, and cellular phones. The existing service included two runs devoted
solely to errands (bank...
BPMJ   hospital runs occurred each weekday, and three others occurred each weekday
       evening. A separate weekday run ...
Re-engineering
management science techniques and software with BPR. Together, the power of
management science and BPR is s...
BPMJ   ProSci (2002), “Methodology selection guidelines”, in, BPR online learning center, available at:
             www.p...
Upcoming SlideShare
Loading in …5
×

Http Www.Emeraldinsight.Com Insight View Content Servlet Filename= Published Emeraldfulltextarticle Pdf 1570100403

3,495 views
3,444 views

Published on

Published in: Business, Technology
1 Comment
0 Likes
Statistics
Notes
  • five articles about social economic back ground affects an individuals performance
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Be the first to like this

No Downloads
Views
Total views
3,495
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
11
Comments
1
Likes
0
Embeds 0
No embeds

No notes for slide

Http Www.Emeraldinsight.Com Insight View Content Servlet Filename= Published Emeraldfulltextarticle Pdf 1570100403

  1. 1. The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at www.emeraldinsight.com/researchregister www.emeraldinsight.com/1463-7154.htm BPMJ Re-engineering proves effective 10,4 for reducing courier costs Lee Revere School of Business and Public Administration, 400 University of Houston-Clear Lake, Houston, Texas, USA Keywords Process management, Optimization techniques, Information systems, Business process re-engineering Abstract The success of business process re-engineering (BPR) is dependent on the use of data-driven methods that provide cost-effective and optimal solutions. Today’s business managers are inundated with methodologies and tools that claim to provide sustaining process improvement results. Determining the appropriate BPR method(s) to employ is a daunting task for many businesses. Understanding the technical complexities of these methods is even more overwhelming. However, with the increased availability of management science software, business managers can easily identify and employ proven management science techniques. Readily available software that provides timely results, is easily adaptable to resource changes, and does not require extensive technical competencies. This paper demonstrates how scientific management techniques, coupled with management science software (Management Scientist, Project Management and Excel), provided a feasible and achievable solution to a laboratory courier service BPR project. The solution yields a 19.5 percent reduction in annual laboratory courier specimen costs while improving service levels. Introduction Business process re-engineering (BPR) is the examination and redesign of business processes to improve cost efficiency and service effectiveness. It fundamentally requires rethinking and redesigning business processes to obtain dramatic and sustainable improvements (Hammer and Champy, 1993). BPR’s premise of radical organizational change requires businesses to think both creatively and realistically in an effort to obtain a solution that is feasible and achievable. This requires adopting a data-driven systematic approach, something that is often difficult for many business managers. Business managers who understand and employ simple data-driven methodologies achieve lasting BPR success. At present, the over-abundance of BPR methodologies and the lack of consensus regarding their usefulness have left many managers confused (Shin and Jemella, 2002). Al-Mashari et al. (2001) showed that successful BPR projects employ fairly simple methods that can be used and understood by non-technical employees. This study suggested that easily implemented management science techniques such as Gantt charts were more often employed in BPR efforts than complex techniques that require a certain level of familiarity. This paper demonstrates how mainstream data-driven management techniques, coupled with readily available software (Management Scientist, Project Management Business Process Management and Excel), provide a feasible and achievable solution to a laboratory courier service Journal Vol. 10 No. 4, 2004 BPR project. Utilization of proven scientific methods assured optimization and pp. 400-414 feasibility, while readily available software provided timely results without technical q Emerald Group Publishing Limited 1463-7154 complexities. This study’s findings are supported by LeBlanc et al. (2000) who DOI 10.1108/14637150410548074
  2. 2. Re-engineering demonstrated the ease of using Microsoft Excel for efficiently assigning construction project managers with one of the 114 construction projects. The ability to use the for reducing existing software eliminates the resource-intensive task of creating complex courier costs mathematical models that are highly process specific and have limited functionality. Portougal and Robb (1996) demonstrated the difficulty in creating a mathematical model to assign managers with a simple building project, while Dell’Amico et al. (1993) 401 and Lobel (1998) showed the necessary requirements of both information systems support and mathematical modeling for vehicle scheduling. Other projects that have required complex optimization models include assigning shifts to telephone operators (Thompson, 1997), assigning airline crews (Jarrah and Diamond, 1997), and assigning flight schedules to aircraft (Abara, 1989). The goal of the laboratory courier service BPR initiative was to reduce the laboratory courier cost (become more cost-efficient) while increasing the level of service provided to the hospitals and health clinics (improve service effectiveness). It was imperative that the BPR project would be completed within four months, due to a concurrent facility redesign. The redesigned facility required the re-routing of many laboratory specimens to a new facility. A process similar to Shin and Jemella’s (2002) four-stage methodology was followed to achieve a cost-effective and optimal solution, in a timely manner. Their methodology consists of: (1) energizing the organization through management involvement and project organization; (2) focusing the project by assessing the current environment; (3) inventing the future process design; and (4) launching the roadmap to implementation. In this project, key laboratory management executives provided energy and organization for the BPR efforts. Assessment of the existing courier service, the laboratory resource constraints, and the information system capabilities (assessing the current environment) was necessary to determine the needs, requirements and limitations for the new process. A process map was used to depict the redesigned laboratory system and necessary flow of information for the new courier service (inventing the future process and design). Management science models were used to create optimal BPR solutions. Evaluation of the implementation plan (launching the road map) projected success. Thus, a cost-efficient and service-effective laboratory courier service was created through the use of data-driven BPR methodologies and the existing optimization software. The results were attained readily and without technical complexity; unlike many re-engineering efforts that have required extensive amounts of time and resources (ProSci, 2002). Case study In 1997, Memorial Healthcare System and Hermann Healthcare System merged into one organization. The new corporation, Memorial Hermann Healthcare System (System), is the largest not-for-profit, community-owned, health care system in southeast Texas. The System has annual total gross revenues of $3.25 billion. There are 13,300 employees and 3,100 inpatient hospital beds within the System’s 11 acute care hospitals, two long-term care facilities, and numerous health clinics. The System
  3. 3. BPMJ admits over 113,000 patients annually. Eight of the System’s acute care hospitals and 12 of the health clinics are located in the greater Houston area. This project focused on 10,4 the greater Houston facilities. The System suffers the same escalating costs prevalent throughout the USA. In 1980, annual health care expenditures averaged $1,067 per person; by 1998 that number had increased by $3,760 per person (McConnell, 2001). Facing these rapidly 402 escalating costs, the purchasers of healthcare are demanding lower costs, and they are creating intense competition among health insurers. In turn, healthcare insurers are renegotiating and reducing hospital service payments because hospital services are estimated to be 35 percent of the total healthcare expenditures (McConnell, 2001). Hospital laboratories are not immune to these costs and competitive pressures. They are no longer able to defray readily the rise in costs with price markups passed along to patients and their insurance companies (Ellis and Moser, 1998). System centralized much of their laboratory testing because compressed and restructured laboratories have proven to save hospital’s millions of dollars (Ellis and Moser, 1998). In the summer of 2002, the System began requiring all of the greater Houston hospitals and health clinics to send their cytology and complex general laboratory tests to Memorial Hermann Hospital (Hermann), while their cytology tests were to be sent to Memorial Southwest Hospital (Southwest). Figure 1 shows the process map of the summer 2002 redesigned laboratory system and the information flow of the re-engineered laboratory courier service. Problem description Transporting of specimens is pervasive in the healthcare industry; centralized laboratories servicing widely dispersed collection locations make this a necessity. Transportation is challenging because specimens must be handled carefully and according to government regulations (Home Health-care Nurse, 2001). Additionally, transportation is challenging due to routing and staffing needs and their attendant costs. Specimen and staffing challenges consist of covering a set number of locations while minimizing travel times and achieving realistic staffing patterns (Desrosiers et al., 2000). This type of integer programming problem is not confined to vehicle routing (Erkut et al., 2000), but is also found in aircraft routing (Desrosiers et al., 2000), and nurse staffing (Bordoloi and Weatherby, 1999), among other problems. The System was seeking a more efficient laboratory courier service to meet the needs of the redesigned laboratory system. The System’s objectives to reduce the total laboratory courier cost was to maintain or improve the existing level of courier service, and to determine the most effective use of both employees and vehicles. It was very important to the System that the level of service be maintained for specimen pickup times, redelivery, and reporting. Overview of research methodology Several process management techniques were employed to achieve a cost-efficient and service-effective laboratory courier service. The research heuristic model included four stages: assessment, mathematical modeling, system optimization, and staffing solutions (Figure 2). During each stage the information-system inputs flowed
  4. 4. Re-engineering for reducing courier costs 403 Figure 1. Memorial Hermann Healthcare System laboratory specimen system, effective May 2002 Figure 2. An overview of research heuristic between the BPR team and the IT team. Critical inputs from system management, laboratory management and patient care management were obtained throughout the project. During the first stage, senior management defined the project objectives, laboratory managers determined their process capabilities, patient care managers verbalized their needs, and information systems specialists assessed the IT environment. Senior
  5. 5. BPMJ management was focused on providing a higher level of service while reducing costs and assuring implementation within four months. The laboratory and patient 10,4 management needs included specific limitations and requirements for reporting specimen results. The information systems assessment showed that the existing system allowed to input the specimen results by the reporting laboratory and for outlying facilities to have immediate access to the results. In the second stage a 404 mathematical model, the traveling salesman, was used to identify the most time-efficient courier routes. An information-systems database was used to determine model inputs, while laboratory and patient-care management needs determined constraints. The Management Scientist software was used to solve the algorithm. Gantt charts, created by Microsoft Project, were used in the third stage to determine optimal start and stop times for the courier runs. Information regarding vehicle and staff availability, as well as other cost data was obtained from both information systems and senior management. Project cost-saving estimates flowed back to the information systems and modifications were made as necessary. Laboratory results-reporting limitations, obtained from laboratory management, were also considered. In the final stage, an integer program was used to generate a flexible 7-day weekly staffing schedule. The Management Scientist software was used to generate the algorithm while Excel was used to create a staffing solution. Resource restrictions were obtained from the information systems and senior management. Potential solutions were included in a database format for ease of alteration. Together, the research methodologies provided an efficient (least cost), effective (high service) and feasible (appropriate staffing) solution to the System’s laboratory courier service. Determining courier service levels The prior laboratory courier service was analyzed to estimate the required service levels. The prior service used nine full-time-equivalent employees and an outsourced courier company. In total, 20 courier runs existed, of which 18 were used for specimen delivery and two for errands. (This study did not include the random errand routes.) The routes were uniform on weekdays, but were sporadic on weekends. Full-time employees were responsible for four specific routes that included three hospitals and eight health clinics (Table I). These routes began and ended at Hermann Hospital. The outsourced courier service was responsible for five specific routes including all weekend runs (Table II). Route Day Facility 1 Monday-Friday Memorial Hermann Northwest and Jane Long Clinic 2 and 3 Monday-Friday Memorial Hermann Southeast, Wave Clinic, and Power Center 4 and 5 Monday-Friday Memorial Hermann Memorial City and Wellness Center 6 Monday-Friday LaConcha, Home Health Central, Texas Medical Center, Fort Bend Health Center Table I. Existing laboratory Note: Routes 2 and 3 are identical. Route 2 is an early morning route including only Memorial Herman courier routes for Southeast, while route 3 is midday. Routes 4 and 5 are identical. Route 4 is a midday route while route 5 full-time employees is an evening route including only Memorial Hermann Memorial City
  6. 6. Re-engineering Route Day Facility for reducing courier costs 9 and 10 Monday-Friday Memorial Hermann Woodlands, Burbank Clinic and Home Health North 11 and 13 Monday-Friday Memorial Hermann Fort Bend, Memorial Hermann Katy, 1st Colony Mall Clinic, and Sport Center 405 12 and 14 Saturday/Sunday Memorial Hermann Fort Bend and Memorial Hermann Katy 15 Saturday/Sunday Memorial Hermann Woodlands, Memorial Hermann Memorial City, Memorial Hermann Southeast, and Memorial Hermann Northwest 16-20 Monday-Sunday Memorial Hermann Southwest Note: Routes 9 and 10 are identical. Route 9 is a midday route, while route 10 is an evening route Table II. including only Memorial Hermann. Routes 11 and 12 are identical. Route 11 is a midday route while Existing outsourced route 13 is an evening route including only Memorial Hermann Fort Bend and Memorial Hermann laboratory courier routes Katy Service demands Each hospital required at least five laboratory specimen pickups/deliveries per day during the week. Ideally, they should be scheduled evenly throughout the day, including an early morning (6:00-7:00 a.m.) and a late night (9:00-10:00 p.m.) pickup/delivery. The early morning test results were required to assess patient status and/or revised care plans. It was critical that these specimens were delivered to Hermann not later than 8:00 a.m. so that tests could be performed and results put into the System computer immediately. The late night pickup/delivery was predominantly for emergency-room specimens. Specimens obtained after the last run were sent to Hermann at the earliest, the next day. Hospital pickup/delivery demands varied greatly during the weekends. But there was some consistency with each hospital requesting an early morning and a late evening pickup/delivery. Although this was requested, an early morning route was not there earlier for many of the hospitals. Thus, meeting this request would provide an increased level of service for the System. Additionally, a request was made that specimens arrive at Hermann within 2 h of being obtained from the originating hospital. Each of the health clinics required only one scheduled pickup per weekday with no pickup required on the weekend. The majority of the health clinic specimens were procured in the morning hours. Thus, while a late morning or early afternoon pick-up schedule was preferred, it was not required. In fact, the prior courier service had a very sporadic schedule for the health clinics. All of the health clinics received their results via the system-wide computer and usually reported the results to patients the next day. Owing to the next-day reporting, there were no expectations that specimens had to reach Hermann within 2 h of pickup. Determining the optimal routes with the traveling salesman model The set-partitioning traveling salesman model was used to determine the most efficient courier routes that met hospital and health clinic service demands. The traveling salesman model was used because it could readily be made to fit the situation. The traveling salesman problem is famous among operation researchers who often encounter it as an embedded part of a larger problem, specifically routing, scheduling,
  7. 7. BPMJ delivery and/or distribution problems (Driscoll and Sherali, 2002). In this effort, the traveling salesman problem was encountered, but minor manual manipulations were 10,4 made to fit situational reality. In a traveling salesman problem the “salesman” (courier) travels from home to multiple destinations (hospitals) then returns home. In the model, Hermann was considered the home and each hospital was considered a destination. The health clinics were considered in a separate model because there were no pickup 406 times required on the weekends. A database containing both time and mileage between nodes was created using MapQueste. MapQueste provides up-to-the-minute travel information that even considers route closures. Both time and mileage data were analyzed, with preference being given to the shortest-time model. Minimizing the time to transport the laboratory specimens would minimize in-route travel time for specimens and also minimize the number of employee hours. Employee hours were the largest cost of transportation, so minimizing on transport time would be effective (specimen travel time) and efficient (employee hours). In a typical traveling salesman model, the solution seeks to prohibit sub-tours. A sub-tour occurs if the traveling salesman returns to the home city before visiting all the other cities. For example, if there are five cities (numbered one to five), the salesman might go from one to three to four and then back to one in the first sub-tour, while he would go from two to five and then back to two in a second sub-tour. This would satisfy the constraints that he must enter and each city exactly once. This can be prevented through mathematical modeling, but the model becomes extremely large. For this reason, decision science software programs, such as Management Scientist, are used to handle the model. However, not allowing the salesman to return home until he/she visits all destinations created an infeasible solution for the courier service because all the specimens had to be delivered to Hermann within 2 h of being obtained by the courier. Thus, for this project, sub-tours were necessary. To meet this limitation, three different models were created and their results compared. In the first model (Model A), the best direct route was identified, with no consideration of the time limitation just mentioned. In other words, the shortest route that went to each hospital and then returned to Hermann was identified. The second model (Model B) forced a stop at Hermann while visiting each of the other hospitals. This model found the shortest route that originated at Hermann, went to at least one hospital, returned to Hermann, and then proceeded to the remaining hospitals before returning to Hermann. The third model (Model C) forced two stops at Hermann while visiting each other hospital, then returned to Herman. The solutions for these three models are shown in Table III. Each of the three solutions was analyzed to determine if the hospitals’ time demand was achieved – delivery to Hermann within 2 h – as well as the sensitivity of the solution to deviations in the inputs time. None of the models met the 2 h time demand as constructed; therefore, the routes for each model were parceled into multiple routes. Model A was parceled into three separate routes to meet the time demand. The three routes had a total round-trip time of 310 min and a distance of 219 miles. Model B revealed no parceled routes that met the time requirement, and Model C was parceled into four routes to meet the time requirement. These four routes had a total round-trip travel time of 326 min and a distance of 226 miles. Table IV shows the results of parceling the routes in each model.
  8. 8. Re-engineering Model A: shortest direct route H-SE-W-NW-MC-K-FB-SW-H 182 miles/259 min for reducing (in minutes) courier costs Model B: shortest one-return route H-W-NW-MC-K-FB-SW-H 188 miles/275 min (in minutes) H-SE-H Model C: shortest two-return route H-NW-MC-K-FB-SW-H 205 miles/297 min (in minutes) H-SE-H 407 H-W-H Table III. Notes: FB = Memorial Hermann Fort Bend; H = Memorial Hermann Hermann; K = Memorial Memorial Hermann Hermann Katy; MC = Memorial Hermann Memorial City; NW = Memorial Hermann Northwest; SE = traveling salesman Memorial Hermann Southeast; SW = Memorial Hermann Southwest; W = Memorial Hermann models Woodlands Memorial Hermann Parceled routes from traveling salesman model traveling salesman solutions Round-trip time/miles Model A W-SE-H 130 min/101 miles K-MC-NW-H 98 min/72 miles FB-SW-H 82 min/46 miles Total 310 min/219 miles Model B No parceled routes satisfied the time demands Model C W-H 86 min/70 miles SE-H 60 min/38 miles K-MC-NW-H 98 min/72 miles FB-SW-H 82 min/46 miles Total: 326 min/226 miles Notes: FB = Memorial Hermann Fort Bend; H = Memorial Hermann Hospital; K = Memorial Table IV. Hermann Katy; MC = Memorial Hermann Memorial City; NW = Memorial Hermann Northwest; SE = Parceled routes from Memorial Hermann Southeast; SW = Memorial Hermann Southwest; W = Memorial Hermann Models A, B, and C that Woodlands meet the time demand The total time and mileage for the parceled Model A was slightly less (16 min and 7 miles) than the total time and mileage for Model C. In addition, Model C required one courier to service only the Memorial Hermann Woodlands Hospital (Woodlands) with a round trip of 86 min and one courier to service only Memorial Hermann Southeast Hospital (Southeast) with a round trip of 60 min. This created a requirement of four employees and vehicles for Model C vs three employees and vehicles for Model A; thus, Model A was deemed more efficient. The three routes determined by Model A were very robust and unaffected by deviations in the travel-time assumptions. Although the round-trip travel time for route one (W-SE-H) was 130 min, the time for a sample from W to arrive at H was less than 80 min. Thus, an extreme deviation in the travel time between either W and SE or SE and H would be required for the optimal solution to change. Although this is highly unlikely, the dynamic travel time database provides updated information as needed. That is, if a major artery between W and SE becomes blocked, new travel-time inputs are obtained from the database and the traveling
  9. 9. BPMJ salesman model is rerun. The other two routes (K-MC-NW-H and SF-SW-H) had ample slack time, and, for that reason, were robust as well. 10,4 The traveling salesman solution for the health clinics was straight forward because the specimens did not have to be delivered within 2 h of pickup. In this model the courier leaves Hermann, travels to the health clinics, and returns to Hermann. Most of the health clinics were centrally located; however, four health clinics were in outlying 408 areas. These four clinics were close to the outlying hospitals, so they were manually included in the courier runs to the outlying hospitals. Including these four clinics in the previously determined routes did not change Model A optimization because the clinics were a few miles away from the outlying hospitals. However, it did slightly reduce the amount of slack available in the time constraint (reaching H within 2 h), but not to the point of jeopardizing the optimal solution. Identifying employee shifts with Gantt charts Gantt charts, which included employee breaks, were used to show the System’s management a detailed schedule of the runs and to ensure that the selected routes (1A, 2A, and 3A) met hospital pickup/delivery demands. Specifically, each hospital required a minimum of five pickups/deliveries – one in early morning and one in the late evening. Not more than 2 h could elapse prior to another scheduled visit, with the exception being visits after lunch and dinner. The employee requirements were that each stop required 15 min and each shift required a 30 min lunch or dinner break. The Gantt charts demonstrated that these demands were met while displaying a feasible staffing arrangement. They were also used to determine the number of employee shifts required and their duration. Separate Gantt charts were used for day and evening services because the routes differed slightly due to the timings of lunch and dinner breaks and/or the inclusion of a health clinic visit during the day route. In total, 13 Gantt charts (courier shifts) were necessary. Two of the hospital runs (2 and 4) included pickup/delivery from four of the health clinics. One run (7) is completely focused on the pickup/delivery for the remaining seven health clinics. Table V summarizes the 13 runs necessary for the service demands. Developing a staffing schedule with integer programming Employee scheduling is critical for service operations, such as courier services. It is a resource-management issue which requires managers to develop equitable schedules for employees while providing enough flexibility so that efficiency can be maintained even with demands that may vary daily (Browne, 2000; Oldenkamp, 1996). Therefore, creating employee shift assignments specified by length, start time, and breaks can be a difficult task. Furthermore, for processes requiring seven-days-a-week coverage, developing a rotating schedule that makes fair use of days-off progression is imperative (Browne, 2000). This employee assignment process is amenable to integer programming for which Dantzig et al. (1954) presented a set-covering formulation upon which many integer programming approaches are now based. Staffing the System’s 13 courier runs to minimize the number of employees during a two-week shift was accomplished using an integer-programming model. The staff resource constraints are given below: (1) Each employee must work # 80 h in a two-week period. This prevented overtime because employees are paid on a two-week period.
  10. 10. Re-engineering Total hours for reducing (including pick-up Number and delivery Corresponding courier costs of runs Days (am/pm) Facilities included in run time) route 1 M –F (am) FB, SW 9 1A 2 M –F (pm) FB, SW, FBHC, JL, WC 8.5 1A 409 3 M –F (am) K, MC, NW 9 2A 4 M –F (pm) K, MC, NW, SC, 1stCM 7.5 2A 5 M –F (am) W, SE 10 3A 6 M –F (pm) W, SE 8 3A 7 M –F (am) SE, HHN, BB, LC, MC, 8 Clinics only WV, PC, HHC 8 Sa – Su (am) FB, SW 8 1A 9 Sa – Su (pm) FB, SW 9 1A 10 Sa – Su (am) K, MC, NW 9 2A 11 Sa – Su (pm) K, MC, NW 5 2A 12 Sa – Su (am) W, SE 6 3A 13 Sa – Su (pm) W, SE 6 3A Notes: FB: Memorial Herman Fort Bend; BB: Burbank Clinic; H: Memorial Herman Hermann. FBHC: Fort Bend Health Center; K: Memorial Herman Katy. HHC: Home Health Central; MC: Memorial Herman Table V. Memorial City. HHN: Home Health North; NW: Memorial Herman Northwest; JL: Jane Long Clinic; SE: Memorial Hermann Memorial Herman Southeast; LC: LaConcha (LTAC); SE: Memorial Herman Southwest; PC: Power laboratory runs and Center; W: Memorial Herman Woodlands; SC: Sport Center; MC: Texas Medical Center; WV: Wave related time Clinic; WC: Wellness Center; 1stCM: 1st Colony Mall Clinic (2) Employees cannot work . one shift per day. No employee was required to work greater than one 10-h shift per day. (3) Employees cannot have . four weekend shifts during the two-week period. Each employee could not be scheduled to work more weekend days than exist in a two-week period. (4) Employees must work # ten shifts in a two-week period. Employees have at least four days off every two weeks. (5) Each weekday shift must be performed ten times in a two-week period. Weekday routes must be performed Monday to Friday or five times per week. (6) Each weekend shift must be performed four times in a two-week period. Weekend routes must be performed Saturday and Sunday, which is twice per week. Three integer-programming models were developed, one for each route determined earlier (1A, 2A, and 3A). The expectation was that employees should always be responsible for the same route regardless of their shift. This expectation limited confusion about learning multiple routes. Results of the integer-programming model indicated that ten employees (not all were full-time) were required. Also, 94 shifts for each two-week period were required. Table VI displays the staffing model results. For example, Employee 1 is assigned Run 1 seven times and Run 2 twice, for a total of nine shifts in a two-week period. Similarly, Employee 2 is assigned Run 1 thrice, Run 2 twice, and Run 8 four times, for a total of nine shifts in two weeks. Together, Employees 1 and 2 work Run 1 ten times, every
  11. 11. BPMJ Runs Total number 10,4 Employee 1 2 3 4 5 6 78 9 10 11 12 13 of shifts 1 7 2 9 2 3 2 4 9 3 6 4 10 410 4 5 3 8 5 4 2 4 10 6 1 8 1 10 7 7 1 8 8 3 3 1 3 10 9 7 3 10 Table VI. 10 10 10 Memorial Hermann Total number of runs 10 10 10 10 10 10 10 4 4 4 4 4 4 integer program staffing Note: Run 7 was a unique route. Integer programming was not needed to staff this route model solutions weekday (M-F), and Run 8 four times, every two weeks (Sa-Su). Because the solution focused on assigning employees to runs, it was very sensitive to deviations in the number of employees available. A reduction in workforce resources would create a demand for overtime hours and/or an infeasible solution. Thus, vacation scheduling and sick-time assumptions are critical to successful staffing. A realistic staffing schedule was created using the information in Table VI. In addition, the following were considered: avoiding employees working a day shift directly following an evening shift; . maximizing the total hours per employee so that a number of employees would . be full-time according to the staffing requirement; and attempting to give employees the same days off each week. . The staffing schedule required ten employees (9.55 FTEs), plus a dispatcher and a manager, for a total of 12 employees (11.55 FTEs). Six of the employees were full-time; that is, they were scheduled to work at least 79 h every two weeks. Of the remaining four employees, one employee was scheduled to work 76 h, another to work for 73.5 h, and two employees were scheduled to work 68 h each. These part-time employees gave scheduling flexibility. Four vehicles were required, one for each route (1A, 2A, 3A) which comprised 12 runs in total and one for the clinic run. It is possible that the four proposed part-time employees would be made full-time and their additional hours used to perform errands or special runs that are not included in the recommended courier schedule; however, this may require an additional vehicle. The integer programming model solution can be modified over time so that the staffing schedule is dynamic; it can be altered over time to meet the changing needs of the system and its employees. Cost savings The existing laboratory courier service for the System had a total cost of $829,253 annually, as determined by the previous year’s experience. This figure included total salaries (including dispatcher and manager), benefits, capital depreciation, fuel, vehicle
  12. 12. Re-engineering repairs, tolls, and cellular phones. The existing service included two runs devoted solely to errands (bank, post office, and inner-System mail stops) and three additional for reducing runs that included errands; these costs were included in the System’s total cost courier costs mentioned above. The errand costs were estimated by the System to be $108,483 annually and were considered unnecessary; thus, the System did not want errands to be included in the new courier service. Thus, the true (existing) laboratory courier 411 service cost (excluding errands) was $720,770 annually. The re-engineered laboratory courier service had an estimated cost to the System of $612,908 annually. This figure includes the same cost categories mentioned above with all cost categories held constant for the old and new courier service. Proposed costs also include 1.5 h/day for errands (which was added to the proposed health clinic run by the System’s management) at a cost of $9,287 annually. So, the actual courier service cost is projected to be $603,081 annually, a reduction of $117,689. This is a 19.5 percent reduction from the existing courier service. Of course, as the solution is implemented there may be some real-world dynamic adjustments for variables not considered or underestimated in the models. These adjustments may prevent achieving some of these projected cost savings. On the other hand, much of the cost data can be expected to increase at a predictable rate (salaries, benefits, and capital depreciation), so the model’s savings may be more than currently expected. Further, some adjustments and changes (perhaps even additional modeling), after modeling implementation and experience, may also increase the projected savings. Additional advantages of the re-engineered courier service seem apparent. The System has control of the courier service and is not dependent on an outsourced company. The travel time for each courier route is minimized with no idle time, and continually updated according to deviations in travel time. Minimized courier time provides a higher level of service to each facility because pickups/deliveries are more frequent and predictable. The vehicle cost is minimized by efficient route scheduling which allows one vehicle to be used for two non-overlapping runs. Staff scheduling is optimized, minimizing the number of employees involved. Employee scheduling provides full workweeks, avoids overtime, and allows for appropriate days off. Additionally, as the new courier plan becomes well understood and implemented, there is a possibility that the manager and dispatcher positions can be unified. This will effect greater cost savings for the new plan. Project summary Prior to re-engineering, the Memorial Hermann laboratory courier service was developed haphazardly on an as-needed basis. Detailed evaluation indicated that the prior system was neither efficient (cost) nor effective (service). Process management methods, fine-tuned for reality, provided a lower-cost and higher-service laboratory courier. Three optimal courier routes were identified using the traveling salesman model. None of the three met the time requirement for specimen delivery. These routes were selected based on the shortest travel time. Each model was manually parceled for multiple routes that were again optimized for shortest travel time. A model (A) was found which met the delivery time requirement. Gantt charts were used to determine the appropriate number of runs to meet specified service levels. Thirteen runs were needed to provide each hospital and clinic with the level of service demanded. Three
  13. 13. BPMJ hospital runs occurred each weekday, and three others occurred each weekday evening. A separate weekday run serviced the health clinics while six runs covered all 10,4 of the facilities on the weekends. Integer programming was used to minimize the number of employees needed to staff the 13 runs. The integer program solution indicated that 11.55 FTEs, including the dispatcher and the manager, were needed to staff the new courier system. These 412 11.55 FTEs included eight full-time employees with the remaining FTEs being part-time employees. A two-week staffing schedule was developed based on the integer programming results. It may be manually modified to meet the newly arising needs without altering the optimal routing and scheduling plan. Maintaining the optimal plan will continue its effectiveness and efficiency. This modeling of courier service was particularly interesting because it required both a combination of precise model optimization as well as slight manual modification to meet all situational requirements. This combination produced a new courier system (routing, scheduling, and staffing) which will save the System $117,689 annually. This is a reduction of 19.5 percent in total costs and these savings are attained even with increases in courier service. This cost improvement and service enhancement is very significant and, in fact, impressive to the System administrators. Conclusions The success of BPR depends on the use of sound analytical methods that provide cost-effective and optimal solutions. Determining the appropriate BPR methods and tools can be a daunting task for many businesses. Management science techniques that have been proven successful eliminate the confusion and lack of understanding surrounding BPR methods, thus paving the way for a successful BPR effort. Readily available management science software that is adaptable to the organization’s objectives and resource constraints, can provide optimal solutions for many business processes without the need for extensive technical competencies. This paper demonstrates how software packages that contain common process management techniques can facilitate and, in fact, make possible successful BPR efforts such as this courier service redesign. The linear programming traveling salesman, with manually created sub-tours, provided more efficient courier routes without the excessive time and information systems burden of creating a new complex mathematical model for analysis. The model allowed new solutions to be quickly determined to account for extreme changes in resource constraints. This approach to manually create traveling salesman sub-tours has further application for other problems where the number of cities are relatively few and the desired routes are constant. Furthermore, integer-programming software effectively assigned employees to laboratory courier runs without requiring a new complex model. Workforce changes can be easily entered into the software by a non-technical manager, resulting in a new, quickly available, optimal solution. Excel was used to create a staffing schedule that is readily adaptable to changes in employee schedule requests. In total, the route, staffing, and scheduling models easily allow for changing model parameters. The models can be adjusted and re-optimized by staff employees who have limited technical capacity. Case research using dynamic optimization models enhances the field of BPR. System’s laboratory courier BPR project demonstrates the value of integrating
  14. 14. Re-engineering management science techniques and software with BPR. Together, the power of management science and BPR is shown to be a very valuable managerial asset. for reducing courier costs References Abara, J. (1989), “Applying integer linear programming to the fleet assignment problem”, 413 Interfaces, Vol. 28 No. 2, pp. 58-71. Al-Mashari, M., Irani, Z. and Zairi, M. (2001), “Business process re-engineering: a survey of international experience”, Business Process Management, Vol. 7 No. 5, pp. 437-55. Bordoloi, S.K. and Weatherby, E.J. (1999), “Managerial implications of calculating optimum nurse staffing in Medicare units”, Interfaces, Vol. 24 No. 4, pp. 35-44. Browne, J. (2000), “Scheduling employees for around-the-clock operations”, IIE Solutions, Vol. 32 No. 2, pp. 30-3. Dantzig, G.B., Fulkerson, D.R. and Johnson, S.M. (1954), “Solution of a large-scale traveling-salesman problem”, Journal of the Operations Research Society of America, Vol. 12, pp. 303-410. Dell’Amico, M., Fischetti, M. and Toth, P. (1993), “Heuristic algorithms for the multiple depot vehicle-scheduling problem”, Management Science, Vol. 39 No. 1, pp. 115-25. Desrosiers, J., Lasry, A., McInnis, D., Solomon, M.M. and Soumis, F. (2000), “Air Transat uses ALTITUDE to manage its aircraft routing, crew pairing, and work assignment”, Interfaces, Vol. 30 No. 2, pp. 41-54. Driscoll, P.J. and Sherali, H.D. (2002), “On tightening the relaxations of Miller-Tucker-Zemlin formulations for asymmetric traveling salesman problems”, Operations Research, Vol. 50 No. 4, pp. 656-70. Ellis, J.E. and Moser, L.H. (1998), “Reorganization, cross-training, and automation have allowed the laboratory department to reduce hospital-paid personnel by more than 70 full-time equivalent (FTE) employees”, Health-care Financial Management, Vol. 52 No. 8, pp. 52-5. Erkut, E., Myroon, T. and Strangway, K. (2000), “Trans Alta redesigns its service-delivery network”, Interfaces, Vol. 30 No. 2, pp. 54-70. Hammer, M. and Champy, C. (1993), Re-engineering the Corporation: A Manifesto for Business Revolution, HarperBusiness, New York, NY. Home Health-care Nurse (2001), “Transport and storage of infusion supplies and blood products”, Vol. 19 No. 9, pp. 537-8. Jarrah, A. and Diamond, J. (1997), “The problem of generating crew bidlines”, Interfaces, Vol. 27 No. 4, pp. 49-64. LeBlanc, L.J., Randels, D. Jr, Swann, T.K. and Emory, E.F. (2000), “Heery International’s spreadsheet optimization model for assigning managers to construction projects”, Interfaces, Vol. 30 No. 6, pp. 95-106. Lobel, A. (1998), “Vehicle scheduling in public transit and Lagrangean pricing”, Management Science, Vol. 44 No. 12, pp. 1637-9. McConnell, C.R. (2001), “Health-care cost containment: a contradiction in terms?”, The Health-care Manager, Vol. 20 No. 2, pp. 68-80. Oldenkamp, J.H. (1996), Quality in Fives [Online Resource]: On the Analysis, Operationalization and Application of Nursing Schedule Quality, Labyrinth Publications, Groningen, pp. 21-36. Portougal, V. and Robb, D. (1996), “Production scheduling theory: just where is it applicable?”, Interfaces, Vol. 30 No. 6, pp. 64-77.
  15. 15. BPMJ ProSci (2002), “Methodology selection guidelines”, in, BPR online learning center, available at: www.prosci.com/project ¼ planning.htm (accessed 28 January 2003). 10,4 Shin, N. and Jemella, D. (2002), “Business process re-engineering and performance improvement: the case of Chase Manhattan Bank”, Business Process Management Journal, Vol. 8 No. 4, pp. 351-63. Thompson, G. (1997), “Assigning telephone operators to shifts at New Brunswick Telephone 414 Company”, Inferfaces, Vol. 27 No. 1, pp. 1-11. Further reading Bellmore, M. and Nemhauser, G.L. (1968), “The traveling salesman problem: a survey”, Operations Research, Vol. 17 No. 3, pp. 538-58.

×