External Operations Portfolio Analysis and Segmentation
by
Todd A. Rosenfield
S.B. Mechanical Engineering, Massachusetts Institute of Technology, 2001
Submitted to the MIT Sloan School of Management and the Mechanical Engineering
Department in Partial Fulfillment of the Requirements for the Degrees of
SACHUSETTS
JUN 18 201
In conjunction with the Leaders for Global Operations Program at. the
Massachusetts Institute of Technology
LIBRARIES
June 2014
2014 Todd A. Rosenfield. All right reserved.
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic
copies of this thesis document in whole or in part in any medium now known or hereafter.
Signature redacted
Signature of Author
MIT Sloan chool of Wnagdnt, MIT Debartment of Mechanical Engineering
May
9,2014
() '0
Certified by
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_
Charles Fine, Thesis Supervisor
Chrysler Leaders for Global Operations Professor of Management, MIT Sloan School of
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Certified by
Associate Professor, Department of Ia
Certified by -Signature
Management
Josef Oehmen, Thesis Supervisor
gement Eng/eering, Technical University of Denmark
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,!Warren Seering, Thesis Reader
Weber-Shaughness Prof
Accepted by
~ser f Mechanic
nvering and Engineering Systems
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David F~. lardt,Mair, Committee on Graduate Studies
Department of Mechanical Engineering
Accepted by
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Maura
IBTI
OF TECHNOLOGY
Master of Business Administration
and
Master of Science in Mechanical Engineering
erson Ibirector of MIT SloanNBA Program
MIT Sloan School of Management
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2
External Operations Portfolio Analysis and Segmentation
by
Todd A. Rosenfield
Submitted to the MIT Sloan School of Management and the MIT Department of
Mechanical Engineering on May 9, 2014 in Partial Fulfillment of the Requirements for
the Degrees of Master of Business Administration and Master of Science in Mechanical
Engineering
Abstract
This thesis focusses on a medical device manufacturer, Company X, using a
razor-and-blades business strategy, in which surgical instruments are offered to
customers at little or no cost in order to facilitate the sale of certain implantable products
that require the use of such instruments. It faces considerable challenges in managing the
instrument supply chain. Company X is interested in better understanding its instrument
portfolio through segmentation so that it can efficiently manage its supply chain with an
appropriate supply chain strategy for each segment. This thesis deals with the
development of a segmentation methodology that can be used to objectively rank and
segment medical device products that do not directly generate sales revenues, but
contribute to the revenues generated by a dependent class of products. A methodology
was developed to rank and segment the instruments within Company X's instrument
portfolio using criticality heuristics. The segments were then evaluated for opportunities
for inventory reduction without harming service levels. In addition, a cost minimization
decision tool was developed to determine the optimal amount of excess inventory that
should be discarded when items subject to minimum order quantities. Using the
segmentation methodology and the minimum order quantity decision tool, Company X
can potentially reduce its inventory value by over 60%.
Thesis Supervisor: Charles Fine
Title: Chrysler Leaders for Global Operations Professor of Management, MIT Sloan
School of Management
Thesis Supervisor: Josef Oehmen
Title: Associate Professor, Department of Management Engineering, Technical
University of Denmark
Thesis Reader: Warren Seering
Title: Weber-Shaughness Professor of Mechanical Engineering and Engineering Systems
3
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4
Acknowledgments
I would like to thank everyone at Company X for sponsoring this project and providing
an excellent learning opportunity and a supportive and collaborative environment in
which to work. I would especially like to give thanks to my company supervisors whose
collective experience and insight helped make sure I was talking to the right people,
asking the right questions and working toward the right goals.
I would also like to thank the Leaders for Global Operations program for providing me
with this opportunity to over the past two years to change my career and my life. Special
thanks also go to my MIT advisors, Charles Fine and Josef Oehmen for the guidance and
encouragement over the course of this project.
Finally, and most importantly, I'm incredibly grateful to my wonderful wife Runa and
sweet daughter Natalia. I can't thank Runa enough for her support and encouragement
during these past two years while I lived in two cities to complete the LGO program.
5
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6
Table of Contents
A bstract.........................................................................................................................................3
A cknow ledgm ents.................................................................................................................
5
T able of Contents........................................................................................................................7
L ist of Figures..............................................................................................................................9
L ist of T ables.............................................................................................................................
10
1
11
Introduction......................................................................................................................
1.1
Problem Statem ent..............................................................................................................
11
1.2
Research M ethodology and Objectives ......................................................................
12
1.3
Thesis Overview ...................................................................................................................
13
2
Company X and Instrument Supply Chain Background ................
2.1
Com pany X ...........................................................................................................................
2.2
Instrum ent Supply Chain..................................................................................................15
2.3
Forecasting and Inventory M anagem ent...................................................................
17
2.4
SKU Proliferation Drivers............................................................................................
19
3
Literature R eview .......................................................................................................
14
23
3.1
Segm entation and Classification.................................................................................
23
3.2
Excess Inventory..................................................................................................................
26
4
Instrument Portfolio Segmentation Methodology...............................................
28
4.1
Fram ew ork Selection......................................................................................................
28
4.2
Segm entation M etric factors.........................................................................................
30
4.2.1
Demand ............................................................................................................................................
4.2.2
Lead Tim e........................................................................................................................................30
4.2.3
Cost....................................................................................................................................................31
4.2.4
Criticality .........................................................................................................................................
32
4.2.5
Other Possible Segmentation Characteristics.................................................................
33
4.2.6
Segm entation Metric Definition.........................................................................................
34
Instrum ent Revenue Contribution..............................................................................
34
4.3.1
Revenue and Spending data.................................................................................................
35
4.3.2
Criticality Heuristics ....................................................................................................................
36
4.3
4.4
5
14
30
ABC Segm entation.........................................................................................................
47
D ifferential Supply Chain Strategy .......................................................................
48
5.1
Segments A, B and C - Base Stock Policy ...............................
7
48
5.2
6
7
8
Segment D - Minimum Order Quantity Management Model.............................
Results................................................................................................................................
49
53
6.1
Segmentation Analysis .......................................
53
6.2
Idealized Inventory Reduction......................................................................................
55
Conclusions and Recom m endations .......................................................................
58
7.1
Segm entation Strategy ...................................................................................................
58
7.2
Instrum ent Set Definitions and Portfolio...................................................................
58
7.3
Supplier M anagem ent ...................................................................................................
59
References.........................................................................................................................
8
61
List of Figures
Figure 1: Company X Instrument Supply Chain ..........................................................
Figure 2: Revenue Contribution Assignment without Mixed-Platform or Generic
Instruments or Revenues......................................................................................
Figure 3: Revenue Contribution Assignment with Generic Instruments.......................
Figure 4: Revenue Contribution Assignment with Generic Implants...........................
Figure 5: Revenue Contribution Assignment with Mixed-Platform Instruments.........
Figure 6: Revenue Contribution Assignment with Mixed-Platform Implants..............
Figure 7: Revenue Contribution Assignment with Mixed-Platform and Generic
Instrum ents and Im plants......................................................................................
Figure 8: Lifetime Cost of Minimum Order Quantity Management Function..............
Figure 9: Segm ent Characteristics .................................................................................
Figure 10: Normalized Cumulative Contribution and Inventory Value .......................
9
17
39
40
42
43
45
46
52
53
56
List of Tables
Table 1: Fictitious Platform Revenue Data by Territory ...............................................
Table 2: Instrument Spend and Implant Revenue without Mixed-Platform or Generic
Instrum ents or Revenues........................................................................................
Table 3: Instrument Spend and Implant Revenue with Generic Instruments ................
Table 4: Instrument Spend and Implant Revenue with Generic Revenues ...................
Table 5: Instrument Spend and Implant Revenue with Mixed-Platform Instruments......
Table 6: Instrument Spend and Implant Revenue with Mixed-Platform Implants.....
Table 7: Instrument Spend and Implant Revenue with Mixed-Platform and Generic
Instrum ents and Implants.....................................................................................
Table 8: Idealized Inventory Reduction by Segment....................................................
10
36
38
39
41
42
44
45
56
1
Introduction
This chapter defines the problem statement and presents the objectives and research
methodology used in the evaluation of the problem. The thesis overview provides an outline of
the entire thesis.
1.1
Problem Statement
This thesis deals with the problem of managing the supply chain for large portfolio of
products that do not directly generate revenue, including items with very low demand and high
minimum order quantities. Company X is a diversified health care company that develops,
manufactures and markets a variety of health care products, including medical devices. A
subsidiary of Company X primarily generates revenues through the sales of certain implantable
products (hereinafter "Implants") used during surgical procedures. Implants can only be used
with certain custom designed durable surgical instruments. Thus, Company X designs and
deploys sets of these instruments, at no cost to the customer, in order to facilitate the sale of
Implants. Within this thesis, any reference to "instruments" is understood to refer only to
instruments designed for use with Implants. This business model, which is followed by each of
Company X's competitors in this industry, can be analogized to the razor-and-blades or printerand-cartridges model of selling the razor or printer at low cost to facilitate the sale of highly
profitable proprietary blades or ink cartridges.
During the past several years, managing the instrument supply chain has become more
challenging for Company X, due to the proliferation of stock keeping units (SKU's).1 Total
inventory levels have risen, while stockouts have become more common. 2 Company X's
strategy has historically been to attempt to keep nearly all SKU's on the shelf ready for
1 The instrument portfolio currently stands at over 10,000 SKU's.
10% of instrument SKUs have become backordered at least once within the past 2 years.
2 Over
11
deployment nearly 100% of the time. Because this strategy has proved difficult and expensive to
pursue, Company X is assessing the risks and benefits of a SKU level analysis and segmentation
of its instrument portfolio.
1.2
Research Methodology and Objectives
This research project was developed to investigate the challenges and benefits of a SKU
level segmentation of Company X's instrument portfolio. The primary sources of information
for this thesis include interviews with multiple stakeholders at various levels within and outside
the organization, plant and warehouse visits, company data, and academic literature. Most
importantly, the author spent over six months from June to December 2013 working on this
project at Company X to gain a better understanding of the business and its challenges.
The overall approach was to examine processes and systems in place supporting the
current state of Company X's inventory management and control, identify the key drivers in
Company X's supply that impact inventory holding costs and service levels, and propose an
idealized approach to supply chain strategy based upon the analysis.
The primary goals for the project were to:
" Develop a framework for segmenting the instruments within the portfolio
" Identify opportunities for inventory and capital cost reductions while maintaining
service levels
" Generate an idealized supply chain strategy based upon a SKU level segmentation
framework
" Demonstrate how a decision analysis tool can be used to minimize total costs
associated with excess inventory of slow moving SKUs with minimum order
quantities.
12
1.3
Thesis Overview
The remainder of the thesis is structured as follows: Chapter 2 outlines a description of
Company X and the challenges it faces in managing its Implants instruments supply chain.
Chapter 3 describes the body of literature relating to ABC segmentation and disposal of excess
inventory. Chapter 4 details the proposed methodology for the segmentation framework,
including the heuristics for measuring the criticality of instruments. Chapter 5 describes the
inventory control policies proposed for each segment, including a method for managing excess
inventory caused by minimum order quantities. Chapter 6 details the cost improvements that
Company X can achieve through the use of segmentation and optimized inventory management
described herein. Chapter 7 outlines conclusions and recommendations for Company X going
forward.
13
2
2.1
Company X and Instrument Supply Chain Background
Company X
Company X is a multinational, multibillion dollar healthcare company. A subsidiary of
Company X primarily generates revenues through the sale of Implants used during surgical
procedures. About half of Company X's revenues are generated in the United States, with most
of the remainder concentrated in Europe, but with growing sales in many Asian, African, Middle
Eastern, and Latin American countries.
Company X sells Implants within three major segments, defined by area of the body in
which the Implant is used. Within each of those segments, it offers Implants within many
different product lines, also referred to as platforms. Each consumable platform represents a
single product line. A surgical procedure utilizing a consumable requires the use of a specialized
set of surgical instruments specifically designed for an individual consumable product platform.
Previously, Company X's subsidiaries were organized as independently functioning
businesses, responsible for their own manufacturing and supply chain operations. However, in
2010, following industry pressure to improve operational efficiency and reduce costs, Company
X changed its organizational structure such that primary subsidiary manufacturing and supply
chain operations were removed from their respective organizations and re-organized into a
separate division within Company X. This organization works with the different subsidiaries to
ensure product supply, develop strategies to reduce operating costs, and mitigate supply risks.
Executive leaders anticipated that the shift would significantly help in aligning all of the
manufacturing and supply chain operations to similar quality standards throughout the network
and improve end-customer relationships overall. Following this reorganization, it became
14
apparent that the subsidiary studied for this thesis was underperforming both in terms of
inventory costs and service.
2.2
Instrument Supply Chain
Instruments are designed in standard sets, sometimes with optional modules.
Additionally, customized substitutes are sometimes designed for certain types of instruments in
response to customer requests. The manufacture of these types of medical devices are subject to
strict regulations, including a long validation period before an instrument can be approved for
use after an engineering change order.
Instruments are primarily machined metal and polymer parts, tools and assemblies. In
the decades prior to this project, Company X had determined that its core competency lay in the
design and manufacture of Implants. For this reason, and due to the economies of scale
associated with manufacture of instruments, Company X elected to outsource the manufacture of
the majority of instruments to third party suppliers.
Company X maintains a single main warehouse in the US from which all instruments for
use worldwide are stored. Instruments are ordered directly from suppliers, and stored in the
warehouse following an inspection, and sent to customers in response to "sales." 3
In the US, sales are driven in each territory by independent distributors. In sales
territories outside the US, sales are driven by internal business units. In both cases, each sales
territory is assigned a yearly budget with which to purchase instruments. That budget is in part
determined by Implant and instrument forecasts. Additionally, an internal marketing department
has a separate budget with which to distribute instruments, primarily used for new product
3In
the context of this thesis, although instruments are generally not sold to customers, and are instead
treated as depreciable assets, their demand behavior emulates items that are sold to customers, and as
such, their deployment is referred to herein as "sales".
15
rollouts. In all cases, the instruments are charged to their respective budgets, and customers are
not charged for the deployment of instruments.
Instrument sales are primarily driven through two channels, new instrument set
deployments or breakage and replenishment.4 First, new instrument sets are deployed either to
existing customers to expand their capacity for Implant surgeries or to add additional platforms
to their offerings. Second, existing instruments in the field occasionally wear out or break, and
need to be replaced.
Generally, when an order is placed, either for an entire instrument set, or for individual
instruments, they are picked from stock at the warehouse, and shipped in small packages directly
to the customer. Certain critical and higher volume instruments may be held at the distributor or
field warehouses for rapid deployment, especially in non-US markets. However, the vast
majority of inventory is held in the main warehouse.
In the event that an ordered instrument is out of stock, in the case of a US territory, the
remainder of the order is shipped to the customer, the reason being that not all Implant surgeries
require the use of all instruments in the set, and the set may still be used for some surgeries
without the missing instrument.5 However, in non-US territories, instrument orders do not ship
until all instruments in the order are available.
An illustration of the instrument supply chain is shown below in figure 1.
4 There are some alternative channels driving instrument sales, include some sales for revenues.
However, these represent a comparatively tiny percentage of total instrument sales.
s Some instruments may only be used in a small percentage of surgeries, depending on the
characteristics of the patient and the preferences of the surgeon.
16
L
Distributor
A
Distributor
B
Distributor
...
-I
L
Supplier 1
ie.
Supplier 2
Company X
Instrument
Warehouse
Customers
Supplier...
Satellite
Warehouse
Figure 1: Company X Instrument Supply Chain
2.3
Forecasting and Inventory Management
Instrument inventory policy is largely based around sales forecasts. Sales forecasts, in
turn, are based on two primary sources of data: historical sales data and territory budgets plans. 6
Each territory's plans for how it intends to allocate its budgets are combined with historical sales
data to generate monthly forecasts. Because these forecasts are largely based on internal
budgeting decisions, it is believed by many stakeholders that the forecasts should be relatively
accurate. However, a number of factors have led to very poor historic forecasting.
First, although each territory distributor gathers and submits data on its plan to allocate its
yearly budget, they are not required to adhere to their plans. Instead, they are permitted to spend
6 In
addition, new product launch plans contribute to instrument forecasts for new products.
However, this thesis is concerned primarily with the inventory management for legacy instruments
rather than for new product launches.
17
their budgets on a completely different product mix. In practice, distributors tend to change their
orders and plans quite drastically. Second, although standard instrument sets are defined for
each platform, customers are not required to adhere to them. In the US, custom instrument sets
are routinely assembled from the catalog to the preferences of the surgeons, making individual
instrument forecasting more difficult. Outside the US, each territory had its own set definitions,
which varied significantly from territory to territory, also making individual instrument
forecasting more difficult. Third, a major source of sales for various instrument SKUs,
especially the lowest volume SKUs, is largely the replenishment of broken or worn instruments
in the field. It cannot be easily forecasted when such replenishment will be required. Finally,
the proliferation of SKUs within the portfolio has generally made forecasting more difficult for
each individual SKU, as Company X is unable to significantly pool inventory risk. Because of
Company X's poor ability to forecast, and because instruments tend to have long lead times
(frequently 3 months, sometimes longer) a culture of conservative inventory management arose.
Surgeries cannot be performed, and thus Implants cannot be sold, without deployed
instrument sets. As a result, Company X largely maintains a make-to-stock inventory strategy
for its Implant instruments, in order to maximize potential Implant revenue.
As mentioned above, the manufacture of the vast majority of instruments is outsourced.
Because of the strict regulatory environment requiring expensive and time consuming process
validation, nearly all instruments are sourced from a single supplier. Depending on the
preferences and capabilities of the supplier, an individual SKU is managed either through
purchase orders, or managed by the supplier.
In the case of purchase order SKUs, an analyst will conduct a monthly review and submit
a purchase order to the supplier if necessary, depending on current stock levels, forecasts, and
18
desired safety stock levels. The size of the order is generally at the discretion of the analyst, and
both volume discounts and minimum order quantities frequently contribute to order size
decisions.
In the case of supplier managed SKUs, minimum and maximum inventory levels are set
with the directive to suppliers that inventory remain within these limits. Inventory and forecast
updates are set to the supplier weekly, enabling them to plan their manufacturing activities.
Minimum and maximum inventory levels are often set according to rules of thumb, and tend to
allow for a significant buffer of safety stock and a large spread between the minimum and
maximum permitted stocking level in order to permit suppliers to supply on a less than monthly
basis. These inventory policies and the culture of conservative inventory management,
combined with a management plug permitting the storage of additional inventory to guard
against unquantified supplier risk have together resulted in ever increasing inventory levels and
holding costs. 7
2.4
SKU Proliferation Drivers
A number of factors have contributed to the instrument SKU proliferation at Company X,
which in turn has contributed to the difficulty in forecasting instrument demand, ability to
service that demand, and inventory holding costs. These factors include platform and instrument
lifecycles, the culture of accommodating surgeon preferences, and business strategy surrounding
instrument set design.
ProductLifecycles
Company X has been selling Implants for many decades, and has many different Implant
platforms within each of its three market segments. When a new Implant platform (or a new set
? Company X was required to rent additional space in a warehouse across the street to
accommodate its warehousing needs.
19
of instruments for an existing platform) is launched, especially if it is intended to be a
blockbuster rather than to fill a niche need, it is generally first launched in the US, where
margins are highest, and in certain high sales territories outside the US.
When a new platform is launched, even if it is intended to replace an existing platform,
the older platform is usually not immediately discontinued. Instead, its instrument sets remain in
the field and need to be serviced for years thereafter. This is the case for several reasons. First,
it would be impossible for suppliers to manufacture the instruments needed to deploy the sets
necessary at all intended customers in a short period of time. Instead, as soon as regulators
approve the sale and use of a new Implant platform, instruments are deployed as they are
produced according to the marketing department's launch plan. Second, surgeons need to be
trained on the technique for new surgical instrument sets. It would not be possible to support the
simultaneous training of all customers. Thus, even if a newly introduced platform is intended to
supplant an older platform, it would take several years to convert existing customers to the new
platform. Third, some surgeons may prefer to continue using older Implant platforms or
instrument sets. While these sets remain in the field, they need to be supported.
Additionally, newer Implant platforms and instrument sets tend to be more expensive
than older platforms. It does not make financial sense to deploy new expensive instruments in
certain international markets where they will not be able to achieve revenues to justify the
expenditure. Instead, instrument sets from older platforms, including some that may have been
phased out in Company X's primary markets, may be deployed and marketed in these lower tier
markets.
20
As a result, instruments that are part of sets designed for older platforms remain in the
instrument portfolio for many years, and it is difficult to predict when older instruments will be
retired.
Surgeon Preferences
Historically, surgeons have been the decision makers regarding which of Company X and
its competitors to rely on for its Implants. Each competitor essentially relies on the same
business model of distributing instrument sets at no cost to facilitate the sale of Implants. As a
result, switching costs for hospitals are very low. Because surgeons have been the decision
makers regarding the purchase of Implants, they can demand that Company X design specialized
instruments (such as with a custom handle) as substitutes for various instruments in the standard
sets. If Company X refuses to provide such custom instruments, surgeons can credibly threaten
to switch to a competitor because of the low switching costs and their decision making power.
Company X's culture and strategy has been to accede to surgeon's demands for custom
instruments, regardless of their individual costs or effects on the instrument supply chain, in
order to avoid any lost sales. This strategy has contributed to the overall proliferation of
instruments, but also specifically to the proliferation of extremely low demand instruments that
are difficult to efficiently stock.
Instrument Set Design
There is a standard instrument set for each platform, sometimes with optional modules
for uncommon patient characteristics or alternative surgical techniques. However, as detailed
above, surgeons are not required to use the standard set. In the US, hospitals are free to request,
21
through the distributors, a set of instruments assembled from the entire catalog. Internationally,
each territory has its own set definition that hospitals within that territory use.8
Due to the freedom with which hospitals and surgeons are provided with which to design
their own instrument sets, and Company X's desire to provide maximum choice to their
customers, there is a reluctance to retire individual instruments.
8 Set
definitions for each territory are set by the distributors based on the preferences of hospitals
within the territory, and concerns for minimizing the cost of deployed instruments. Outside the US,
Consumables margins are generally lower, and instrument costs are charged to distributors P&L, so
concerns about instrument costs are reflected in set design.
22
3
Literature Review
The literature surrounding inventory classification policies and the handling of excess
inventory is extensive, and this review is not intended to be exhaustive. Rather, it is meant to
provide a summary of various approaches to classification and excess inventory, as well as how
these approaches can be used to better understand inventory segmentation/classification and
excess inventory at a medical device company such as Company X.
3.1
Segmentation and Classification
For organizations managing large portfolios of SKUs, it may not be practical to
individually control each product. One way to manage a large number of SKUs is to aggregate
them into different groups and set common inventory control policies for each group. For
example, Millstein et al. (2014) notes that management may set different service levels for
different groups, reflecting a company's order fulfillment strategy and customer relationship
policies. Grouping provides management a means for specifying, monitoring and controlling
inventory performance. Guidance for how to treat the different groups ranges from vague
suggestions such as stricter managerial oversight or attention to more specific suggestions such
as varied service rates or distinct control policies (make-to-order versus make-to stock, for
example).
A well-known method of inventory grouping is the ABC classification system, widely
used throughout the world. ABC classification generally is well described by Silver et al.
(1998). In a typical ABC implementation, items are grouped according to their transaction
volume or sales value, and it is often found that small number of items account for a high
percentage of volume or sales. The categories are typically labeled A, B, and C, respectively.
There is little agreement on the breakdown of percentages for each classification, however. A
23
common breakdown is to follow the Pareto "80/20" rule, giving the top 20% of items the A
classification, the next 30% B, and the bottom 50% C. Flores and Whybark, (1986). Silver, et
al. (1998) suggests a 10/50/40 breakdown. Juran (1954), a pioneer in manufacturing quality
management and in Pareto analysis as applied to inventory management, describes a 5/20/75
breakdown.
Nor is there universal agreement on the optimal number of classes. Fewer classes
provides for more simplicity in setting inventory control policies, while more classes increases
complexity while allowing for finer control. The number of classes is usually limited to six.
Silver, et al. (1998).
Millstein et al. (2014) suggests an optimization model to determine the
optimal number of inventory groups and their corresponding service levels, as constrained by a
limited inventory spending budget.
ABC classification is typically performed based on a single criterion, most commonly
demand value, followed by demand volume. Teunter, et al. (2010). However, it is recognized
that using only revenue, or any single criteria, to classify items can fail to capture all the factors
that may be important for classification and optimization of inventory policy to various
stakeholders. Other criteria recognized as relevant for various organizations include inventory
holding cost, part criticality, lead time, commonality, obsolescence, substitutability, number of
requests for the item in a year, scarcity, durability, reparability, order size requirement,
stockability, demand distribution, and stock-out penalty cost. See Eslaminasab and Dokoohaki
(2012).
There are several approaches to using multiple parameters in inventory classification.
Flores and Whybark, (1986) describe a two factor classification (dollar value and criticality, as
determined by management) to classify items into 9 categories using a 3x3 table, which were
24
then mechanically bunched into three criticality categories. Eslaminasab and Dokoohaki (2012)
describe using a non-linear optimization model to maximize total portfolio performance across
multiple criteria. Ernst and Cohen (1990) suggest the use of statistical clustering methods.
Zhang et al. (2001) proposes classifying SKU's through the use of a ranking criteria defined by
the ratio (D/c 2 ) where D is the expected yearly demand, 1 is the lead time, and c is the cost, using
that classification to set a desired service level, and using heuristics to determine order quantities
and reorder points. Teunter et al. (2010) proposes a ranking criteria defined by the ratio (bD/hQ)
where b is the backorder cost (as a proxy for criticality), D is the expected demand per unit time,
h is the inventory holding cost per item per unit time, and Q is the average order quantity, setting
an overall desired fill rate, and using an optimization model to minimize costs by setting the
service level for each classification.
Spare parts inventory classification is an active area of classification literature. Flowers
and O'Neill (1978) applied traditional ABC analysis on manufacturing equipment spare parts
inventory, ranking items by the cost of items used annually. It is recognized that traditional
ABC classification using a single criteria, such as demand or cost, is not sufficient for
heterogeneous spare parts that may have vastly different levels of criticality. Molenars et al.
(2012). Spare parts inventory management is of particular concern because if a spare part is not
available, the shortage cost may be far greater than any potential individual lost sale if
manufacturing lines are shut down, or if a customer switches suppliers. Rego and Mesquita
(2011) reviewed a significant amount of literature concerned with classifying spare parts
according to their demand forecasting models (among other spare parts issues). Gajpal et al.
(1994) propose spare part classification based on a single criticality metric as determined by an
analytic hierarchy process, a multi-criteria decision making tool that takes into account the
25
relative importance of various criteria values using weights as determined by normalized
eigenvectors. Braglia et al (2004) applied also applied the analytic hierarchy process using the
same method, but additionally used decision tree analysis to segment the items into one of four
different inventory management policies. Molenars et al. (2012) proposes the use of the analytic
hierarchy process and a decision tree to classify spare part criticality based on equipment
criticality, logistics characteristics, and probability of failure. Each of the above spare parts
criticality classification schemes rely on qualitative assessments of the characteristics of each
spare part.
3.2
Excess Inventory
A significant amount of literature explores the concept of the disposal of excess
inventory. Virtually all of this literature, however, looks at the situation from a revenue
maximization perspective for items that are directly sold for revenue, rather than for a cost
minimization perspective. Furthermore, none of the literature analyzes a situation subject to
future minimum order quantities.
Tersine and Toelle (1984) and Tersine et al. (1986) present tools for making disposal
decisions regarding excess inventory, assuming deterministic demand and multi-period review in
order to maximize profits. However, in neither of these articles do they consider the impact of
minimum order quantities on their ability to replenish in the future. Each of Rosenfield (1989),
Lovejoy (1992) and Qetinkaya Parlar (2010) examine the decision to dispose of excess inventory
assuming stochastic demand processes under various circumstances. Again, however, none of
them consider the situation where the future replenishment of an item is subject to minimum
order quantities.
26
Alternatively, there is significant literature concerning inventory control policies for
items subject to minimum order quantities. For example, Zhou et al. (2006) examined the
effectiveness of a heuristic policy for a single-item periodic review inventory system with
stochastic demand and minimum order quantities. However, none of this literature considers the
possibility of a disposal decision by an entity with excess inventory in the face of such minimum
order quantities.
27
4
Instrument Portfolio Segmentation Methodology
The question this chapter attempts to answer is what framework to use for the portfolio
segmentation, and what factors to include in the analysis. To answer this question, we looked at
the frameworks presented above and selected one appropriate for Company X.
4.1
Framework Selection
A basic ABC classification based on either of the classic criteria of demand or revenue
cannot be applied, as instruments generally do not directly generate any revenues. As detailed
above, the instruments are deployed to hospitals at no cost to facilitate the sale of Implants, and
are treated as depreciable assets once they are deployed. Alternatively, a simple ABC
classification based only on demand fails to account for the fact that these parts are parts of
instrument sets, and may have varied degrees of criticality with respect to Company X's ability
to continue to generate revenues through the sale of Implants. A multi-factor ranking metric,
such as those proposed by Teunter, et al. (2010) or Zhang et al. (2001) seem more appropriate.
Treatment of instruments as spare parts may seem appropriate, as the instruments in some
ways function as spare parts. Just as a manufacturing line can be shut down if a piece of
equipment malfunctions and needs a spare part, an instrument set can no longer be used to
perform surgeries if an individual instrument needs replacement. However, there are significant
differences that lead away from this treatment.
First, the loss of use of a single instrument set may not be comparable to an entire
manufacturing line in terms of dollar value. Second, the use of analytic hierarchy process to
measure spare part criticality requires the use of several subjective judgments and manual data
coding regarding both the divisions of the modes and the relative weights of each criteria and
28
mode. Company X wishes to avoid this cumbersome and subjective process in favor of an
analysis that can be performed using objective data already in existence.
Thus, we are led to propose a multiple criteria segmentation metric that can be used to
rank and segment the instruments. There are a number of criteria that can be selected for this
metric. We first look to the literature for guidance on the metric.
)
As noted above, Zhang et al. (2001) suggest a ranking metric defined by the ratio (D/c2
where D is the expected yearly demand, 1 is the lead time, and c is the cost. This ratio fails to
take into account item criticality, and this seems insufficient for Company X's purposes.
Furthermore, it puts undue weight on item cost, as noted by Teunter, et al. (2010).
Alternatively, Teunter et al. (2010) proposes a ranking metric defined by the ratio
(bD/hQ) where b is the backorder cost (as a proxy for criticality), D is the expected demand per
unit time, h is the inventory holding cost per item per unit time, and
Q is the average order
quantity. This ratio includes a measure of criticality, which is important for Company X to
conclude. However, this metric was defined with the purpose of using an optimization to
minimize total inventory costs, including holding and shortage costs, while holding a desired
total fill rate constant. Although it includes a measure of criticality, it does so with the specific
function of minimizing total inventory and shortage cost, under the assumption that shortage cost
can be accurately measured. Further, although the metric is aimed at reducing costs, a stated
goal of this project, the cost minimization function fails to allow for the application of alternative
inventory control policies to the different classifications (such as make to stock versus make to
order), rather than simply setting different service levels. Company X is not concerned with
setting a desired overall fill rate. Rather, it wishes to classify its instrument inventory based on
29
objectively measurable criteria and have the flexibility to determine the inventory control policy
for each segment.
4.2
Segmentation Metric factors
Having arrived at the conclusion that a multiple criteria ranking metric is most
appropriate for the classification of Company X's we explore the relevant criteria to include the
segmentation metric. The criteria for whether or not to include a given item characteristic in the
segmentation metric include whether the characteristic can be objectively measured with
available data, whether the characteristic reflects the cost of maintaining high service levels, and
whether the characteristic reflects the importance of the item to Company X's business.
4.2.1
Demand
Both Zhang et al. (2001) and Teunter, et al. (2010) include demand in their segmentation
metric, recognizing its importance in classification schemes. The fact that the traditional single
criterion of ABC classification is either demand volume or demand value supports this view.
That in many cases it is insufficient on its own to measure the strategic importance of an item
does not lead us to exclude it. It should be noted that higher the demand not only leads to higher
cycle stock levels (assuming the same cycle length), but also leads to higher levels of safety
stock (and in turn, higher holding costs) because higher variability is typically associated with
higher demand.
4.2.2
Lead Time
Items with short lead times have lower safety stock levels (and in turn, lower
inventory holding costs) because Company X does not have to account for as much
variation of demand during lead time. This characteristic seems especially significant for
Company X both because most lead times are fairly long due to the make to order policies
30
of its suppliers and because there is significant variation in lead time from supplier to
supplier. For this reason, Zhang et al. (2001) included this characteristic in their
segmentation metric, as items with shorter lead times are cheaper to stock due to the lower
demand variability during lead time, and hence lower safety stock levels. Teunter et al.
(2010) did not include lead time in their segmentation metric because it was derived from
an expression of the fill rate in terms of the service level. However, lead time does factor
into the performance of their segmentation metric in terms of the level of safety stock
required following the optimization. The importance of lead time to maintaining low
inventory costs leads us to include it in our segmentation metric.
4.2.3
Cost
Both Zhang et al. (2001) and Teunter, et al. (2010) include cost in their segmentation
metrics. Teunter includes inventory holding cost per item per unit time. Zhang instead includes
unit cost. Because we are interested in including in our segmentation metric characteristics that
reflect that cost to stock an item, we definitely want to include a measure of the actual cost of
holding that item. Ideally, the metric should include both a measure of the actual inventory
holding costs and capital costs of holding inventory. However, we have no reliable objective
measure of any activity, volume, weight or other basis for allocating inventory holding costs,
other than the cost of the item itself. The cost of the item does reflect the capital holding costs,
however, and is believed to be correlated with actual holding cost. Because item cost is the only
objective measure we have of inventory holding cost, we will assume that total holding cost,
including both actual and capital costs, is proportional to item cost. It should be noted that
Zhang includes the square of cost in their metric. We will not square this characteristic in order
to avoid putting undue importance on that characteristic.
31
4.2.4
Criticality
For the purposes of this thesis, criticality is generally defined as the importance of
an item to the continued success of the business. For example, it could relate to importance
of a spare part to the continuing operations of a manufacturer, the strategic importance of
offering an item for sale (such as to retain a high value customer), the lost profits
associated with a shortage, or the ability of the item to directly facilitate the sale of other
items. Most of the literature relating to spare parts classification recognizes the
importance of criticality in setting up the segmentation. However, they generally rely upon
subjective assessments of the relative importance of various criteria relating to criticality,
as well as of the relative degree of criticality between different modes within those criteria.
Teunter et al (2010) includes a shortage cost as a measure of criticality in their
segmentation metric, however because shortage cost cannot be objectively measured for
Company X's Implant instruments, we cannot include that as a measure of criticality.
Instead, we looked for an alternative measure to compare SKU criticality.
One of the motivations for the project that led to this thesis was a desire to better
understand how the various instruments within portfolio were contributing to Company
X's revenues. Many stakeholders within Company X had directly expressed this desire to
the author of this thesis. Thus, we determined that the best measure of criticality for the
purposes of segmenting the instrument portfolio would be an objective measure of Implant
revenue contribution by each individual instrument SKU. Section 4.3 below details how
Implant revenue was assigned to the instrument SKUs.
32
4.2.5
Other Possible Segmentation Characteristics
Strategicimportance
Although it is recognized that certain Implant platforms, and their associated
instruments, are more critical to Company X's long term business strategy, any
measurement of this strategic importance would be inherently subjective. Because we
wish to include only objective data in the segmentation metric, we will not include any such
criteria in the analysis. It should be noted that the criticality metric, as defined below,
could easily be multiplied by a strategic importance factor if Company X wishes to assign
strategic importance values to its instrument SKUs.
Demand variability
Demand variability directly affects the holding costs for a given SKU due to
necessary increases in safety stock levels. However, we will not include this factor because
it is well known that the coefficient of variation of an item is inversely correlated with its
periodic demand.
Order Quantity
Teunter et al (2010) included order quantity in their segmentation metric because,
according to their analysis, higher average order quantities lead to, leaving all else equal,
lower cycle service levels and fill rates. However, we are assuming a periodic order up to S
policy with a monthly review period for all SKUs, where
D/Q as Teunter did, neither D nor
assume
Qwould
Q is equal to D.
Thus, if we include
have any effect on the ranking. 9 Because we
Q is equal to the monthly demand for each
item, we do not include it in the
segmentation metric.
9Teunter
et al. assert that their method is applicable to a periodic review policy by accounting for
the expected level of "undershoot". However, no details are provided.
33
4.2.6
Segmentation Metric Definition
Based on the above analysis, we include periodic demand, lead time, item cost (as a
proxy for capital and holding cost rate) and criticality (to be objectively measured with
Implant revenues). Higher periodic demand and higher criticality are assumed to have
higher importance, and are thus placed higher in the numerator of our segmentation
metric. Higher lead time and higher costs directly increase the holding costs of stocking an
item, so they are placed in the denominator of the metric. Thus, we define the metric as
follows:
Mi =
DC
(1)
where mi is the segmentation metric value for a given SKU I, D is the period demand, C is
the criticality factor, 1 is the lead time, and c is the item cost.
4.3
Instrument Revenue Contribution
As described above, we wish to include an objective measure of criticality in our
segmentation metric. Because Company X wishes to better understand how individual
instruments are contributing to Implant revenues, we will use the available data to assign
Implant revenues to instrument SKUs based on the level of spending on each instrument in
each territory.
Ideally, we would collect data on every deployed instrument set throughout the
world and the revenues generated from each surgical procedure using that instrument set,
and assign the revenues from each procedure based on which instruments were actually
used in that procedure. Over a period of time, the revenues assigned to each SKU could be
summed and objectively compared. However, such data as described are not available.
The most useful data Company X was able to provide to measure instrument contribution
34
is at the territory level. For each territory, we collected data on the cost of instrument SKUs
actually deployed as well as the revenues generated by each Implant SKU over a period of
nearly two years. Revenues generated by Implants were assigned to the instrument SKUs
based on data regarding which Implant platforms instruments were designed for. Because
customers are permitted to construct custom instrument sets from the entire catalog of
instruments, non-standard uses of instruments could not be accounted for.
4.3.1
Revenue and Spending data
The revenue from each Implant SKU sale is first assigned to one of three market
segments, hereinafter referred to as Market Segments 1, 2 and 3, which each correspond to
different parts of the body. Revenues are further divided among each of the product lines,
or platforms, within each market segment, with the notation that a SKU in platform 1-a
belongs to Market Segment 1 and platform a. In some cases, an Implant SKU can be used
with multiple platforms. In those cases, they will have the notation 1-ab, belonging to
Segment 1 and both platforms a and b. In some other cases, an Implant SKU can be used
across most or all platforms within a given segment. This will be indicated with the
notation of platform 1-0. In total, there are three types of instruments, single platform (ex.
1-a), mixed platform (ex. 1-ab), and generic (ex. 1-0).
The spending on each instrument SKU is also assigned to one of segments 1, 2 or 3.
Again, within each segment, an instrument is generally assigned to a given platform, with
the notation of platform 1-a. In some cases, an instrument SKU is used for multiple Implant
platforms. In those cases, they will have the notation 1-ab, as with Implants. In some
other cases, an instrument may be used across most or all platforms within a given
segment, in which case they will be noted with platform 1-0.
35
The Implant revenue data was aggregated for each platform in each territory during
the revenant time period. A table of fictitious data is presented below to illustrate how the
data was aggregated.
Territory
Platform
1-a
1-b
1-ab
1-c
1-0
2-d
2-e
2-f
2-de
2-0
Mercury
$200,000
$400,000
$500,000
$1,000,000
$300,000
$5,000,000
$4,000,000
$8,000,000
$6,000,000
$2,000,000
Venus
$300,000
$500,000
$100,000
$2,000,000
$50,000
$10,000,000
$6,000,000
$3,000,000
$2,000,000
$10,000,000
Earth
$1,000,000
$800,000
$1,200,000
$3,000,000
$1,200,000
$8,000,000
$12,000,000
$18,000,000
$10,000,000
$5,000,000
Mars
$500,000
$300,000
$50,000
$800,000
$1,000,000
$3,000,000
$5,000,000
$5,000,000
$1,000,000
$800,000
Table 1: FictitiousPlatform Revenue Databy Territory
Instrument revenue data was similarly aggregated to the total cost of deployed
instruments in each territory during the relevant time period.
4.3.2
Criticality Heuristics
As described in section 4.2.4 above, we decided that instrument criticality would be
incorporated into the segmentation metric, and that we would measure it by assigning revenues
generated form Implant sales to their associated instruments. Implant revenue in each territory
was assigned from Implants to instruments according to a set of heuristics, which will be
illustrated by way of several examples.
*
Case 1: In this simple case, also referred to as the base case, there are only
instruments within a Market Segment used for single platforms (ex. 1-a) and
Implant revenues attributable to single platforms (ex. 1-a). For this case,
Implant revenue was assigned to instrument SKUs in its platform in
36
proportion to the fraction of the cost of deployed instruments of each SKU
out of the total instrument spending in that platform.
For each of cases 2-5, a single additional type of instrument or Implant is added to
the base case.
" Case 2: In this case, instruments not attributable to an individual platform
within a segment (ex. Platform 1-0) are added to the base case. For these
instruments, Revenue was assigned from Implants within their Market
Segment in proportion to the fraction of the cost of deployed instruments of
each SKU out of the total instrument spending in that Market Segment.
*
Case 3: In this case, Implant revenue not attributable to an individual
platform within a segment (ex. Platform 1-0) was added to the base case.
That revenue was assigned to instruments within that segment in proportion
to the fraction of the cost of deployed instruments of each SKU out of the
total instrument spending in that Market Segment.
" Case 4: In this case, instruments attributable to two or more specific
platforms (ex. Platform 1-ab) were added to the base case. The addition of
mixed-platform instruments adds the complication that revenue from certain
platforms needs to be assigned to an additional category of instruments.
Revenue was assigned to those instruments from Implants in each of their
associated platforms in proportion to the fraction of the cost of deployed
instruments of each SKU out of sum of the cost of deployed instruments in
both of those platforms
37
"
Case 5: In this case, Implant revenue attributable to two or more specific
platforms (Ex. Platform 1-ab) was added to the base case. The addition of
mixed-platform Implants adds the complication that revenue from an
additional category of implants needs to be assigned to certain platforms.
That revenue was assigned to instruments in each of their associated
platforms in proportion to the fraction revenues generated by each of those
specific platforms out of the sum of those revenues.
"
Case 6: In this case, every type of instrument and Implant described above is
added to the base case.
The assignment of Implant revenue to instruments in each of the above cases is
illustrated below.
Case 1: Market Segment without any mixed-platform or generic instruments or
revenues:
In this simple case, assume Market Segment 1 only has instruments and Implants
within each of two platforms, 1-a and 1-b with the following instrument spending and
Implant revenues in a given territory:
Implant Revenue
Platform
Instrument Spend
1-a
Sa
ra
1-b
Sb
rb
Segment 1 total
S= sa + sb
R=ra+ rb
Table 2: Instrument Spend and Implant Revenue without Mixed-Platform or GenericInstruments
or Revenues
The Implant revenue contribution (c) assigned to the ith SKU within platform 1-a is
as follows:
Cai = -i ra
Sa
38
(2)
where si is the instrument spending on the ith SKU. The equation for instruments in
platform 1-b is identical. The revenue contribution assignment in Case 1 is illustrated in
Figure 2:
S-
Dri-a
ri-b
S1-b
Figure 2: Revenue ContributionAssignment without Mixed-Platform or Generic Instruments or
Revenues
The total contribution for a given SKU, Ci from the segmentation metric ratio, would
thus be equal to the sum of the contribution ci from each territory.
Case 2: Market Segment with generic instruments:
In this case, assume Market Segment 1 has instruments within each of three
platforms, 1-a, 1-b and 1-c along with generic instruments, 1-0, and Implant revenue in
each of 1-a, 1-b and 1-c, with the following instrument spending and Implant revenues in a
given territory:
Platform
Instrument Spend
Implant Revenue
1-a
Sa
ra
1-b
Sb
rb
1-c
1-0
Sc
So
rc
n/a
Segment 1 total
S= Sa + Sb + Sc + SO
R=ra + r+rc
Table 3: Instrument Spend and Implant Revenue with Generic Instruments
39
The Implant revenue contribution (c) assigned to the ith SKU within platform 1-a is
as follows:
Cat =
(1
a
-)
(3)
Because there is a proportion of Segment 1 spending (Platform 1-0 SKUs) not
attributable to any specific platform within Segment 1, the revenues attributable to
Platform 1-a SKUs must be reduced in proportion to the fraction of Platform 1-0
instrument spending within Segment 1. The equations for Platform 1-b and 1-c SKUs are
identical. Implant revenue contribution assigned to the ith SKU within platform 1-0 is as
follows:
coj
=
S
R
The revenue contribution assignment in Case 2 is illustrated in Figure 3:
r-a
ri-b
Si-a
.G-
r1-c
s1-C
s1-O
Figure 3: Revenue ContributionAssignment with Generic Instruments
40
(4)
Case 3: Market Segment with generic Implants
In this case, assume Market Segment 1 has instruments within each of three
platforms, 1-a, 1-b and 1-c, and Implant revenue in each of 1-a, 1-b and 1-c, along with
generic Implants, 1-0, with the following instrument spending and Implant revenues in a
given territory:
Platform
Instrument Spend
Implant Revenue
1-a
Sa
1-b
sb
ra
rb
1-c
1-0
sc
n/a
Segment 1 total
S= sa + sb
rc
+sc
ro
R=ra+ rb + rc + ro
Table 4: Instrument Spend andImplant Revenue with Generic Revenues
The Implant revenue contribution (c) assigned to the ith SKU within platform 1-a is
as follows:
Ca,i = -"ra+Sro
s
Sa
(5)
Because there is a proportion of Market Segment 1 revenue (Platform 1-0 SKUs) not
attributable to any specific platform within Market Segment 1, the revenues attributable to
Platform 1-a SKUs must be increased in proportion to the fraction of spending on each
instrument within Market Segment 1. The equations for Platform 1-b and 1-c SKUs are
identical. The revenue contribution assignment in Case 3 is illustrated in Figure 4:
41
r1-a
S1-a
r1-c
s1-C
Figure4: Revenue ContributionAssignment with Generic Implants
Case 4: Market Segment with mixed-platform instruments
In this case, assume Market Segment 1 has instruments within each of three
platforms, 1-a, 1-b and 1-c along with mixed platform instruments, 1-ab, and Implant
revenue in each of 1-a, 1-b and 1-c, with the following instrument spending and Implant
revenues in a given territory:
Instrument Spend
Platform
1-a
Sa
1-b
Sb
Sab
1-ab
1-c
sc
Segment 1 total S= sa + sb + sab + sc
Implant Revenue
ra
rb
n/a
rc
R=ra + rb + rc
Table 5: Instrument Spend and Implant Revenue with Mixed-Platform Instruments
The Implant revenue contribution (c) assigned to the ith SKU within platform 1-a is
as follows:
Ca,i = -ra
Sa
sa+sb
Sa+Sb+sab
42
(6)
The contribution of revenues from Platform 1-a Implants attributable to Platform 1a instruments is reduced by the fraction of spending on mixed-platform instruments out of
the sum of instrument spending on Platform 1-a, 1-b and 1-ab instruments. The equation
for Platform 1-b instruments is identical. The Implant revenue contribution assigned to
Platform 1-ab instruments is as follows:
Cab,i =
Si
sa+Sb+Sab
(ra + rb)
(7)
Because in this case there are no mixed-platform instruments for platform c, and no
generic instruments or implants, the equation for instruments in platform c in this case is
identical to equation 2 in Case 1. The revenue contribution assignment in Case 4 is
illustrated in Figure 5:
ri-a
sl-a
S1-ab
Sr-c
Si-c
Figure 5: Revenue ContributionAssignment with Mixed-Platform Instruments
Case 5: Market Segment with mixed-platform Implants
In this case, assume Market Segment 1 has instruments within each of three
platforms, 1-a, 1-b and 1-c, and Implant revenue in each of 1-a, 1-b and 1-c, along with
43
mixed-platform instruments, 1-ab, with the following instrument spending and Implant
revenues in a given territory:
Platform
1-a
Instrument Spend
Implant Revenue
Sa
ra
1-b
Sb
rb
1-ab
n/a
rab
1-c
Sc
Segment 1 total
S=sa+ Sb + sc
rc
R=ra+ rb+ rab+ rc
Table 6: Instrument Spend andImplant Revenue with Mixed-Platform Implants
The Implant revenue contribution (c) assigned to the ith SKU within platform 1-a is
as follows:
Ca,i =
r rab)
+(ra+
(8)
The contribution of revenues attributable to Platform 1-a instruments is increased by the
fraction of spending on each SKU out of the sum of instrument spending on Platform 1-a
and 1-b instruments. The equation for Platform 1-b instruments is identical. Because in
this case there are no mixed-platform instruments for platform c, and no generic
instruments or implants, the equation for instruments in platform c in this case is identical
to equation 2 in Case 1. The revenue contribution assignment in Case 5 is illustrated in
Figure 6:
44
r1-ab
r1-b
S1-b
ri-c
Si-c
Figure 6: Revenue ContributionAssignment with Mixed-Platform Implants
Case 6: Market Segment with mixed-platform instruments and Implants and generic
instruments and Implants
In this case, we combine all the added elements from Cases 2-5. Segment 1 has
instruments within each of three platforms, 1-a, 1-b and 1-c, along with mixed-platform
instruments 1-ab and generic instruments 1-0, and Implant revenue in each of 1-a, 1-b and
1-c, along with mixed-platform Implants, 1-ab and generic Implants 1-0, with the following
instrument spending and Implant revenues in a given territory:
Platform
1-a
1-b
1-ab
Instrument Spend
Implant Revenue
Sa
ra
Sb
rb
Sab
1-c
Sc
rab
rc
1-0
So
Ro
Segment 1 total
S= sa +Sb + Sab + Sc + SO
R=ra + rb+ rab+ rc + ro
Table 7: Instrument Spend andImplant Revenue with Mixed-Platform and Generic Instruments
and Implants
45
The Implant revenue contribution (c) assigned to the ith SKU within platforms 1-a,
1-b, 1-ab, 1-c and 1-0 are as follows:
ca,i
)a
sasb
ra rab
ra+rb
ra
SakSa+Sb+Sab
(
k
Cb, i :-LL
sa+sb
b
Sb (sa+Sb+sab)
(rb + r+rab
CabJ
si
sa+Sb+sab
ccj =
-(Ta
+
Tb
rc (1 coj =
R
0
(9)
S (10)
- LO b +b s L s
S
+ rab
) +
+ Lr
-)
sb
S(
ro
1)
sS
(12)
(13)
As is evident from these equations, when the various values for mixed-platform and
generic instruments and revenues are reduced to zero, the equations reduced to those
equations in the earlier cases. The revenue contribution assignment in Case 6 is illustrated
in Figure 7:
r1-a
S1-a
ri-ab
sl-ab
r1-b
S-
Figure 7: Revenue ContributionAssignment with Mixed-Platform and Generic Instruments and
Implants
46
As noted above, and as seen in the equations used, Implant revenues were assigned
to instrument spending proportionally to the dollar value of the cost of the instruments
deployed. Alternatively, we could have could have assigned revenue proportional to the
number of instruments deployed. We felt assignment proportional to cost was more
appropriate in order to avoid giving undue weight in the criticality measurement to
relatively inexpensive items. This assumes that the relative importance of instruments to
surgical procedures is correlated with the cost of an item.
4.4
ABC Segmentation
Using the above heuristics to calculate the contribution (and thus criticality) by each
instrument in each territory, and summing across all territories yields a total contribution
across all territories.1 0 The segmentation metric given in equation 1 was then calculated
from the available data, and SKUs were then ranked according to that value. There is no
universal agreement on how many classifications to use, and the sizes of those groups. We
chose to use four segments, denoted Segments A, B C and D, using percentages of 10, 15, 25
and 50, respectively.
10 We also added revenues for instruments that were actually sold by Company X to the total
contribution. These were almost universally significantly smaller than revenues assigned using the
heuristics.
47
5
Differential Supply Chain Strategy
With the Segmentation completed as detailed above, we now turn our attention to how to
treat the different suggestions with respect to supply chain strategy. Guidance from the literature
concerning segmentation and classification range from vague suggestions such as pay closer to
attention higher priority segments, to more specific control policy decisions such as make-toorder versus make-to-stock.
5.1
Segments A, B and C - Base Stock Policy
For Company X, who wishes to maintain a make-to-stock policy for as many SKUs as
possible, we recommend for Segments A, B and C using a traditional order up to S base
stock policy. Company X's already uses a monthly review policy and suppliers are already
accustomed to monthly orders. Periodic review order up to S inventory policies are well
known in the literature, the industry, and Company X, and will not be described in detail
herein. It should be sufficient to note that safety stock levels are related to the expected
demand during the review period plus lead time. Company X already uses supplier
managed inventory for many of their suppliers depending on the volume of items
purchased and supplier capabilities. Supplier managed inventory is recommended where
feasible in order to provide suppliers with earlier and more frequent data regarding SKU
level demand. Minimum and maximum inventory requirements can be set to the same
levels that they would otherwise be without supplier managed inventory (safety stock and
the order up to quantity minus the demand during lead time, respectively).
The benefit of the segmentation in applying this control policy is to easily set the
desired service level for each segment. For segments A, B and C, we may set a service level
of 99.9%, 99.5% and 99%, respectively, to minimize the probability of a stockout without
48
allowing safety stock to reach unreasonable levels. Depending on how comfortable
Company X is with more frequent stockouts, service levels can be decreased in order to
decrease safety stocks.
5.2
Segment D - Minimum Order Quantity Management Model
Segment D presents an interesting problem. A sensible recommendation may be to
use a make-to-order inventory control policy for instruments within this segment, or at the
very least to use an appropriate order up to quantity and safety stock, given the expected
demand and demand variability. Most of the items in Segment D have such low demand
(many with zero demand during the observation period) that the order up to quantities are
far below the minimum order quantities required by the supplier. These minimum order
quantities are in part responsible for the excessive levels of inventory being experienced by
CompanyX.
The question remains how Company X can deal with these minimum order
quantities. We hypothesize that it is more economical for items with extremely low
demand, when looking at the total instrument cost (purchase plus holding costs), to order
the minimum order quantity, but discard some amount of them instead of holding all of
them in inventory.
To test this hypothesis, we developed a model to calculate the present value of the
lifetime cost of stocking a slow moving SKU subject to a minimum order quantity. Several
assumptions are built into the model, including constant expected demand, nonperishability of individual items, indefinite SKU lifetime with a constant probability of
obsolescence, and order size of one. Given the following parameters:
D = expected demand per year
49
N = Number of items purchased
M = Minimum order quantity
c = unit cost
i = cost of capital
r = inventory holding cost rate
The present value of the total cost (purchase cost plus discounted holding cost) of stocking
all items purchased at the beginning of the cycle is defined as:
C= cM+;I=
,
cret zdz
(14)
which is equivalent to:
)(i/D)
C=cm + r1 y1
-
(5
which is equivalent to:
C = cM +
N- e-i/D ('1 /D
(16)
Equation 16 being the present value of the cost of purchasing and holding a slow
moving SKU for one purchase cycle, the lifetime cost of stocking the SKU for an expected
lifetime of T is equal to:
= )TDIN
CM
+
()
(N -
e~lD (1-eID
e(-1
)iN/D
(17)
This is a non-linear function that can be used to find the optimal N to minimize total
lifetime costs with non-linear optimization minimization function. The function can be
used to incorporate a probability of obsolescence (rendering the entire stock
simultaneously obsolete) by substituting i+h for each instance of i in equation 17.
50
The formula can alternatively be formulated assuming a setup cost, s, and a separate
unit cost, c. If the supplier is willing to charge separately for the setup cost and waive the
minimum order quantity, the cost function becomes:
C*=
Ns
+ cN( +
( ) (N- eLID (e- )iN/D
In order to illustrate the use of equation 16, we present an example using fictitious
data and parameters in order to protect Company X's confidential information. For this
example, we will assume an item cost of $100, a holding cost rate of 20%, a cost of capital
rate of 10%, a probability of obsolescence of 5%, an expected demand of 0.5 per year, a
minimum order quantity of 25, and an indefinite expected lifetime.
Using these parameters, we find that total cost is minimized with N=7, meaning that
company X would pay its supplier for the cost of the minimum order, 25, but only accept
delivery of 7. This lowers the present value of total lifetime costs for this item by over 35%.
As shown in Figure 8, shows that the cost function is less sensitive as N is increased than if
it is decreased. As such, Company X may wish to err on the side of holding more inventory
if it is not certain of the true demand. It should be noted that the sensitivity is dependent
on the other parameters, and the exact behavior of the example shown in Figure 8 is not
universal.
51
8000
C*
-
--
10000
-
-
-
---
6000
-
12000
_-
---
-
4000
2000
--
0
5
10
15
20
25
N
Figure 8: Lifetime Cost of Minimum Order Quantity Management Function
It should be noted that the use of the minimum order quantity management and
inventory disposal tool may have additional beneficial side effects for Company X, in
addition to direct inventory holding cost reductions. Due to the reduced quantity of the
orders, but the same total purchasing cost (and in turn, increased effective unit cost),
Company X may wish to increase its standard cost for certain slow moving items. An
increase in this cost will increase the cost to the budgets of territory distributors who fulfill
orders of these items. Without a corresponding increase to their budget, this extra hit to
their budget may give them the incentives to push sales of faster moving substitutes with
lower cost in order to preserve their budgets. The overall effect on the supply chain would
be to move sales from slower moving, more difficult to forecast items to faster moving
predictable items. This would improve Company X's overall ability to service its portfolio
and reduce overall inventory holding costs. It may also provide a justification for the future
rationalization of slow moving items in order to reduce portfolio complexity.
52
6
Results
6.1
Segmentation Analysis
Based on the segmentation metric defined above in Section 4.2.6, and using the
Implant revenue assignment heuristics described in Section 4.3, Company X's portfolio of
active instrument SKUs was divided into four main segments. As described above, the
segmentation metric, mi is defined as DC/ic, where mi is the segmentation metric value for
a given SKU i, D is the period demand, C is the criticality factor, 1 is the lead time, and c is
the item cost.
A visual representation of the results of the segmentation and their characteristics is
shown below in Figure 9. It should be noted that the value of items held in inventory
shown in Figure 9 indicates the average inventory value over the observation period, not
the current state of inventory.
Obsolete
Critical
-10% of SKUs
e57% of Spend
*63%of Contribution
*30% of Inventory
I
I
*Outside of segmentation
analysis scope
*Coded in system as
obsolete or discontinued
High Priority
*15% of SKUs
of Spend
*25% of Contribution
-22% of Inventory
e26%
Cost Drivers
*25% of SKUs
-14% of Spend
*11% of Contribution
27% of nventory
I
Anemic
pop
Demand
*50% of SKUs
*2% of Spend
*0.7%
of Contribution
*21% of inventory
Figure9: S ?gment Characteristics
53
D1: 1958 stocked SKUs w/ demand
D2: 1013 SKUs already classified as MTO
D3: 2216 SKUs with zero demand
As shown in Figure 9, Segment A, which we have named "Critical", the top 10% of
SKUs according to the segmentation metric, account for 57% of the cost of deployed
instruments, 63% of revenue contribution, but only 30% of the cost of items held
inventory. There may be opportunities for the more efficient management of inventory,
but because we will be using a high service level for this segment, the priority will not be
inventory reduction.
Segment B, which we have named "High Priority", the next 15%, accounts for 26% of
the cost of deployed instruments and a proportional 25% of revenue contribution and 22%
of the cost of items in inventory. Again, there may be some opportunities for inventory
reduction in this segment, but we will still prioritize a high service level for this segment.
Segment C, which we have named "Cost Drivers", the next 25% of SKUs, accounts for
14% of the cost of deployed instruments, but only 11% of revenue contribution, while
taking up 27% of the cost of items held in inventory. There may be significant
opportunities for inventory reduction in this segment.
Segment D, which we have named "Anemic Demand", the bottom 50% of SKUs,
accounts for only 2% of the cost of deployed instruments, and less than 1% of revenue
contribution, while taking up 21% of the cost of items held in inventory. We will focus our
attention on a method to reduce inventories in this segment while dealing with minimum
order quantities that Company X's suppliers usually require, as described above. Segment
D is further divided into three sub-segments comprising items that had positive demand
during the period of observation, items that had zero demand, and items that are already
classified as make to order (some of which had positive demand). For items neither
already classified as make to order, nor subject to minimum order quantities above the
54
optimal periodic order quantity, we will specify a base stock policy at a service level below
Segment C in order to reduce inventory holding costs.
Also shown in Figure 9 is Segment E, additional SKUs that are still tracked by
Company X, but indicated to be obsolete and/or discontinued. These SKUs were not
included in the segmentation analysis, but still collectively account for a non-insignificant
value of items held in inventory.
6.2
Idealized Inventory Reduction
In order to assess the effectiveness of using the segmentation metric to set the base stock
policies and service levels, as well as the minimum order quantity management and inventory
disposal decision tool, we will compare the total inventory holding cost reductions that Company
X can achieve in the ideal state (that is, assuming optimized average inventory levels for a given
service level in each segment). In order to protect Company X's confidential information, the
data will be normalized and presented only on a percentage basis.
Figure 10 illustrates the normalized cumulative revenue contribution, calculated as
described in section 4.3.2, in segmentation rank order. Also shown are the normalized
cumulative inventory value in the current state and the cumulative inventory value in the ideal
state, normalized against the current inventory value.
55
Cumulative Contribution vs. Inventory Value
0.8
P
-
---- Cumulative Inventory
State
0
'
O.6
0.6Current
o-Cumulative Inventory - Ideal
State
0.4
0.0
0
2000
8000
6000
4000
10000
12000
Segmentation Rank
Figure 10: Normalized Cumulative Contributionand Inventory Value
As can be seen in Figure 10, lower ranked items account for a disproportionate amount of
cumulative inventory for Company X. In the ideal state, total inventory is reduced by over 60%.
However, to better understand the reductions across each segment, we look at the reductions of
each segment individually in Table 8.
Segment Inventory Value,
Current State
A
B
C
D
Total
0.305
0.221
0.265
0.209
1
Inventory
Reduction,
Absolute
0.174
0.133
0.173
0.148
0.627
Inventory
Value,
Ideal State
0.130
0.088
0.092
0.061
0.372
Table 8: Idealized Inventory Reduction by Segment
56
Inventory
Reduction,
Percentage
57%
60%
65%
71%
63%
As shown in Table 8, despite the fact that we have used higher service levels for higher
priority segments, there are significant inventory and similar reductions to be made across the
portfolio. Despite a 99.9% desired service level in segment A, we can achieve a 57% reduction
of inventory for items in Segment A for Company X. This indicates that Company X is being
overly conservative in its inventory policy across its portfolio. It should be noted that the 71%
reduction in inventory value for Segment D only incorporates the reduction of inventory value
due to the minimum order quantity management and excess inventory disposal tool, but does not
factor in the increased purchase costs due to its use. Thus, the total cost savings for Segment D
will be less than 71%, most likely about half that value.
11 In the example shown in in Figure 9 in Section 5.2, average inventory level is reduced by 72%, but the
present value of lifetime cost is reduced by only 35%.
57
7
7.1
Conclusions and Recommendations
Segmentation Strategy
As shown in Table 8, despite the fact that we have used higher service levels for higher
priority segments, there are significant inventory reductions to be made across the instrument
portfolio. The segmentation metric we defined has great utility in ranking items within the
portfolio, giving Company X the ability to efficiently manage items within each segment with an
appropriate inventory control policy. However, the data for this thesis was collected over a
limited period of time, and a greater time horizon would improve the measure of criticality.
Improved data granularity would also improve the measure of criticality. We recommend, to the
extent possible, Company X track the deployment and use of instruments at each customer in
order to better understand how instruments are actually used in practice. This type of data would
considerably improve Company X's ability to objectively measure individual SKU criticality.
7.2
Instrument Set Definitions and Portfolio
As described above, the use of instrument set definitions are a key contributor to
Company X's SKU proliferation and inability to efficiently manage its instrument portfolio.
There are a number of initiatives Company X could embark upon to prevent these problems
in the future. The use of standardized set definitions would greatly improve Company X's
ability to service its instrument portfolio. Instrument demand at the SKU level is incredibly
difficult to forecast because each territory may have a different instrument set for the same
platform (and in the US, customers can order custom sets). Eliminating this variability
would have a tremendous effect on Company X's ability to forecast demand, which in turn
would improve their ability to provide high service levels. Standard instrument sets may
58
be more expensive than the non-standard sets currently being ordered in various
territories, leading to overall increased instrument deployment spending. We recommend
further study regarding the overall effects of standardized set definitions on instrument
spending and inventory costs. Any such study should necessarily include an analysis of the
possibility of alienation of customers who may switch to the competition in response to
standardized sets.
Further study is also recommended on the area of SKU rationalization and lifecycle
management for Company X. Product lifecycles and the expanding portfolio of platforms
are major contributing factors to the complexity of the platform, especially to the plethora
of slow moving SKUs with high minimum order quantities. Efforts should be made to
identify candidates for SKU rationalization to reduce portfolio complexity.
7.3
Supplier Management
The characteristics of Company X's instruments (externally sourced, custom designs,
long lead time, low volume, minimum order quantities high degree of regulation) have
contributed to their growing inventory costs and poor service levels. There are, however, a
number of approaches Company X could take with respect supplier management that could
improve their ability to provide high service and lower inventory cost.
Company X should endeavor to consolidate its suppliers. For all suppliers, smaller order
quantities are not very profitable, and minimum order quantities are in place to ensure not only
that each order is profitable, but also to pay for setup costs on each order. By consolidating its
supply base, Company X could gain leverage against its major suppliers to decrease their
minimum order quantities or unit costs. Suppliers who do a lot of business with Company X
59
may be willing to supply smaller lots of low demand items in order to ensure it continues to get
the large orders of highly profitable higher demand demands.
Company X may also wish to renegotiate the structure of its supply contracts. Currently,
Suppliers do not charge separately for setup costs, but instead simply charge a per unit cost.
Sometimes this results in discounts for bulk purchases (incentivizing procurement analysts to
make larger than necessary purchases), and usually it results in minimum order quantities.
Instead, Company X should negotiate to pay separately for the setup cost and individual items
and waive the minimum order quantity. Suppliers may be more willing to do this, as discussed
above, if they are consolidated and do more business overall with Company X. With this
structure, Company X could reduce its purchasing and inventory costs without reducing supplier
profits.
60
8
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