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Re-engineering proves effective
for reducing courier costs
School of Business and Public Administration,
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.
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
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
Vol. 10 No. 4, 2004
BPR project. Utilization of proven scientiﬁc methods assured optimization and
feasibility, while readily available software provided timely results without technical
q Emerald Group Publishing Limited
complexities. This study’s ﬁndings are supported by LeBlanc et al. (2000) who
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
existing software eliminates the resource-intensive task of creating complex
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)
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
(1) energizing the organization through management involvement and project
(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).
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
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
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.
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
system, effective May
An overview of research
between the BPR team and the IT team. Critical inputs from system management,
laboratory management and patient care management were obtained throughout the
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
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
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
Route Day Facility
9 and 10 Monday-Friday Memorial Hermann Woodlands, Burbank Clinic and Home
11 and 13 Monday-Friday Memorial Hermann Fort Bend, Memorial Hermann Katy, 1st
Colony Mall Clinic, and Sport Center
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
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
including only Memorial Hermann. Routes 11 and 12 are identical. Route 11 is a midday route while
route 13 is an evening route including only Memorial Hermann Fort Bend and Memorial Hermann
laboratory courier routes
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
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,
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
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.
Model A: shortest direct route H-SE-W-NW-MC-K-FB-SW-H 182 miles/259 min
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
Notes: FB = Memorial Hermann Fort Bend; H = Memorial Hermann Hermann; K = Memorial
Hermann Katy; MC = Memorial Hermann Memorial City; NW = Memorial Hermann Northwest; SE =
Memorial Hermann Southeast; SW = Memorial Hermann Southwest; W = Memorial Hermann
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
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.
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
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
Memorial City. HHN: Home Health North; NW: Memorial Herman Northwest; JL: Jane Long Clinic; SE:
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
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
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
10 10 10
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
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.
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
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
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
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.
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
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
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.
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
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
Abara, J. (1989), “Applying integer linear programming to the ﬂeet assignment problem”,
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.
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,
Thompson, G. (1997), “Assigning telephone operators to shifts at New Brunswick Telephone
414 Company”, Inferfaces, Vol. 27 No. 1, pp. 1-11.
Bellmore, M. and Nemhauser, G.L. (1968), “The traveling salesman problem: a survey”,
Operations Research, Vol. 17 No. 3, pp. 538-58.