Evaluation of Postponement in the Drug Product Supply Chain
by
Nicholas Sazdanoff
B.S. Mechanical Engineering
Ohio State University, 2006
Submitted to the MIT Sloan School of Management and the Department of Mechanical
Engineering in Partial Fulfillment of the Requirements for the Degrees of
Master of Business Administration
and
Master of Science in Mechanical Engineering
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in conjunction with the Leaders for Global Operations Program at the
Massachusetts Institute of Technology
June 2015
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0 2015 Nicholas Sazdanoff. All rights 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 created.
Signature of Author
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MIT Slodi Schoof of Management, Mechanical Engineering
May 7, 2015
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Research Scientist, MI
Accepted by
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Brian-)mtb y, Thesis Supervisor
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Mauvraq'1eson, Director o MIT Sloan MBA Program
MIT Sloan School of Management
Accepted by
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David E. Hardt, Chair, Mechanical Engineering
Ralph E. and Eloise F. Cross Professor of Mechanical Engineering
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Evaluation of Postponement in the Drug Product Supply Chain
by
Nicholas Sazdanoff
Submitted to the MIT Sloan School of Management and the Department of Mechanical
Engineering on May 7, 2015 in partial fulfillment of the requirements for the degrees of Master
of Business Administration and Master of Science in Mechanical Engineering.
Abstract
This thesis evaluates the use of postponement in the Drug Product (DP) supply chain at
Amgen, which is characterized by highly variable production lead times. The motivation for the
use of postponement in the DP supply chain is to reduce the lead time and improve the service
level from the manufacturing site to the distribution centers (DCs). Amgen is undergoing a rapid
global expansion and is now serving markets that operate on tender (bid) systems that require
rapid fulfillment. To compound this challenge, FDA driven requirements have significantly
increased the likelihood of generating Non-Conformances (NCs) in DP manufacturing, which in
turn increases the production lead time variability.
A simulation model was created in Microsoft Excel that uses historic production lead
time and demand data to determine postponement levels and simulate performance of the system.
Leveraging the simulation model, this thesis demonstrates that utilizing postponement in supply
chains with highly variable production lead times can significantly improve service level and
diminish customer lead time while potentially reducing global inventory levels.
Thesis Supervisor: Thomas Roemer
Senior Lecturer, MIT Sloan School of Management
Thesis Supervisor: Brian Anthony
Research Scientist, MIT Laboratory for Manufacturing & Productivity
3
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4
Acknowledgements
I would like to thank Amgen, particularly Vishal Khanderia the DP Cycle Time Initiative
Lead, Rosana Morales my supervisor, and Noemi Romero the project champion, for sponsoring
the internship. Working at AML for six months was a great learning opportunity, and I would
like to thank my colleagues at AML for their support and instruction along the way.
Specifically, I would like to thank Stephen Hill, Hector Perez, Alex Candelaria, Lizbenette
Rivera, Sandra Mansilla, Oscar Olivencia, Nelcar Rivera, Greg Evans, Cristina Delgado, and
Emmanuel Sanchez.
This thesis would not have been possible without the support of the Leaders for Global
Operation Program as well as the guidance from my thesis advisers Thomas Roemer and Brian
Anthony. Finally, I would like to thank my wife Katie for her patience and support throughout
the internship and for planning our great many Puerto Rican adventures.
5
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6
Table of Contents
Abstract...............................................................................................................................
3
Acknowledgem ents........................................................................................................
5
Table of Contents..........................................................................................................
7
List of Figures.....................................................................................................................
9
Introduction ................................................................................................................
12
1
2
3
4
5
1.1
Project M otivation.............................................................................................
12
1.2
Problem Statem ent ..............................................................................................
14
1.3
Thesis Overview and Hypothesis........................................................................
15
Operations at Am gen...............................................................................................
16
2.1
Industry and Com pany Background..................................................................
17
2.2
Biopharm aceutical M anufacturing....................................................................
18
2.2.1
Drug Substance Production........................................................................
18
2.2.2
Drug Product M anufacturing ......................................................................
19
Literature Review ...................................................................................................
20
3.1
An Overview of Postponem ent ........................................................................
21
3.2
Benefits of Postponem ent...............................................................................
22
Lead Time Analysis - the Link between NCs and Service Level...........................
26
4.1
Probability of an N C in syringe DP production...............................................
27
4.2
N C resolution time ..........................................................................................
29
4.3
Impact of N Cs on service level ........................................................................
32
Postponem ent in Drug Product M anufacturing ....................................................
. 34
Current DP supply chain .................................................................................
34
5.1.1
DP M anufacturing ....................................................................................
. 34
5.1.2
From manufacturing to the custom er ....................................................
.. 35
5.1
7
5.2
6
DP Manufacturing with postponement ......................................................
37
5.2.2
From manufacturing to the customer with postponement.............
38
Simulation M odeling of DP Production with Postponement ..................................
9
10
Model formulation and inputs and outputs ......................................................
40
41
6.1.1
"Inputs" tab ...............................................................................................
43
6.1.2
"Simulation" tab.........................................................................................
48
Modeling results................................................................................................
50
6.2
8
37
5.2.1
6.1
7
DP supply chain with postponement.............................................................
6.2.1
Postponement inventory levels .................................................................
51
6.2.2
Impact of NCs ...........................................................................................
51
6.2.3
System performance......................................................................................
53
Conclusions ................................................................................................................
54
7.1
Impact on inventory and service level.............................................................
56
7.2
Impact on lead time .........................................................................................
58
Recommendations ...................................................................................................
59
8.1
Track flow time through production and adjust planning process accordingly.. 59
8.2
Reduce safety stock levels at the DCs when postponement system is stable ..... 60
8.3
Focus improvement efforts on reducing production lead time variability .....
61
Glossary ......................................................................................................................
63
References...............................................................................................................
64
8
List of Figures
Figure 2-1: Amgen sales by product and geography [3]...............................................
17
Figure 2-2: Process steps to create and produce biopharmaceuticals............................
19
Figure 2-3: Drug product manufacturing process steps: formulation, filling, inspection,
packaging, and disposition (release). Intermediate product stages are as follows drug product
(DP), inspected drug product (IDP), and finished drug product (FDP)....................................
20
Figure 3-1: Illustration of the push-pull supply chain strategy.....................................
21
Figure 3-2: An example of a generic product and its variants for the soluble coffee supply
chain differentiation point [9]...................................................................................................
23
Figure 3-3: Example of lead time pooling through the use of a centralized distribution
center adapted from Cachon, G. and Terwiesch, C. [6].............................................................
24
Figure 3-4: Inventory with the consolidated distribution supply chain and the directdelivery supply chain with different supplier lead times adapted from Cachon, G. and Terwiesch,
C . [6] .............................................................................................................................................
25
Figure 4-1: Drug product manufacturing process flow with hypothetical average cycle
tim e (CT) for each process step .................................................................................................
26
Figure 4-2: Drug product manufacturing process flow with probability of receiving an
NC in Formulation, Filling, or Inspection ................................................................................
28
Figure 4-3: Percentage of commercial syringe lots receiving NCs in 2013 and 2014 ..... 28
Figure 4-4: Percent of commercial syringe batches receiving a Class II or Class III NC by
quarter from Q i 2013 through Q3 2014 ...................................................................................
29
Figure 4-5: Histograms of Class I NC resolution time in days for commercial syringe
batches produced from January 1 through May 31, 2013 and 2014.............................................
30
Figure 4-6: Histograms of Class II NC resolution time in days for commercial syringe
batches produced from January 1 through May 31, 2013 and 2014........................................
31
Figure 4-7: Histograms of Class III NC resolution time in days for commercial syringe
batches produced from January 1 through May 31, 2013 and 2014....................
31
Figure 4-8: Hypothetical lead time through DP production from Formulation through
Inspection including NC Resolution for commercial syringe lots produced from January through
M ay 2013 and 2014 ......................................................................................................................
9
32
Figure 4-9: Commercial syringe batches with Class I or Class III NCs and service level
from AM L to Amgen distribution centers .................................................................................
Figure 5-1: Am gen DP supply chain ..........................................................................
33
. 35
Figure 5-2: Hypothetical inventory levels including planning limits ...........................
37
Figure 5-3: DP manufacturing with postponement at IDP ..........................
38
Figure 5-4: The current DP supply chain and proposed supply chain with postponement
.......................................................................................................................................................
40
Figure 6-1: Process flow diagram of the DP supply chain that underlies the DP
Sim ulation M odel..........................................................................................................................
41
Figure 6-2: Plot from DP Simulation Model that shows the inventory levels for a
hypothetical IDP SKU ..................................................................................................................
42
Figure 6-3: Screen capture of the DP Simulation Model "How to use" tab .................. 43
Figure 6-4: Screen capture of the DP Simulation Model "Input" tab ...........................
44
Figure 6-5: Screen capture of the DP Simulation Model "Input" tab showing the @Risk
"Define Distribution" dialog box that is used to assign a lead time probability distribution for
each production case .....................................................................................................................
45
Figure 6-6: Product X lead time distribution in days for the "No NC" case (batches that
do not receive and NC during DP production) ........................................................................
45
Figure 6-7: Product X lead time distribution in days for the "Class 1 NC" case (batches
receive a Class 1 NC during DP production) fit against historical data for a similar product...... 46
Figure 6-8: Product X lead time distribution in days for the "Class 2 NC" case (batches
that receive a Class 2 NC during DP production) fit against historical data for a similar product47
Figure 6-9: Product X lead time distribution in days for the "Class 3 NC" case (batches
that receive a Class 3 NC during DP production) fit against historical data for a similar product47
Figure 6-10: Screen capture of DP Simulation Model "Simulation" tab...................... 48
Figure 6-11: Output of DP Simulation model showing the service level of Product X
assum ing the inputs described is Section 6................................................................................
50
Figure 6-12: Simulation model output of service level for Product X using 2013 NC rates
and NC closure time distributions and a reorder point of 460,000 units ...................................
52
Figure 6-13: Simulation model output of service level for Product X using 2014 NC rates
and NC closure time distributions and a reorder point of 460,000 units ...................................
10
52
Figure 6-14: Simulation model output of service level for Product X using 2014 NC rates
and NC closure time distributions and a reorder point of 550,000 units ...................................
53
Figure 6-15: DP Simulation Model system performance plot for hypothetical SKU....... 54
Figure 7-1: Example of generic IDP and the corresponding FDP variants .................. 56
Figure 7-2: Current WIP inventory levels in DP manufacturing ..................................
57
Figure 7-3: Hypothetical DP production lead times .....................................................
57
Figure 8-1: Normalized plot of the impact of lead time on safety stock ......................
61
Figure 8-2: Class II NC resolution histograms for 2013 and 2014...............................
62
11
I
Introduction
The purpose of this thesis is to evaluate the use of postponement in supply chains with
high lead time variability through production. Specifically, this thesis focuses on the injectable
drug product (DP) supply chain at Amgen. The research was conducted as part of an internship
with the supply chain group at Amgen Manufacturing Limited (AML) from June 2014 to January
2015 in conjunction with the Leaders for Global Operations program at the Massachusetts
Institute of Technology. This thesis is a result of the internship and the collaboration between
Amgen and MIT.
1.1 Project Motivation
The motivation for the use of postponement in the Drug Product (DP) supply chain is to
reduce the lead time and improve the service level from the manufacturing site to the distribution
centers (DCs). Amgen is undergoing a rapid global expansion and is now serving emerging
markets that are characterized by low demand volumes and high variability. In addition, many
of these markets operate on tender (bid) systems which require rapid fulfillment. The challenge
is to improve responsiveness while maintaining a high service level. This challenge is
complicated by regulation from the FDA that is significantly increasing the number of NonConformances (NCs) in DP production which results in greater lead time variability through
production.
Amgen has been reliably serving patients in the US, Canada, and EU since the early
1990's. These mature markets have large stable demand and reliable transportation networks.
Today, the company is undergoing a rapid global expansion. As such, Amgen has begun serving
emerging markets that are characterized by low demand volumes, high variability, frequent
regulatory changes, and uncertain transportation networks. As a consequence emerging markets
often require faster service (expedited orders from manufacturing) in order to ensure supply.
In addition, many emerging markets operate on competitive tender systems for
pharmaceutical procurement, which require rapid fulfillment. A request for tenders (RFT) is a
formal structured invitation to suppliers, to bid, to supply products or services. For
pharmaceutical products, a government or public hospital association, as payer, requests tenders
12
for a certain class of drugs (such as statins) and then grants the contract to the pharmaceutical
company that offers the "best" bid. The bids are typically evaluated based on price, efficacy, and
quality [1]. Upon winning the bid, the contracts often specify delivery of a fixed quantity of
product within the fulfillment time stated in the tender bid. For some markets, the fulfillment
time can be just five weeks, which is dramatically shorter than the three to four month lead time
that AML is accustomed too.
Service from AML to the distribution centers (DCs) is significantly impacted by lead
time variability in DP production caused by the generation and resolution of Non-Conformances
(NCs). An NC is initiated when there is an event that deviates from an approved GMP
requirement, such as a procedure, specification, or operating parameter, and/or an event that
requires an investigation to assess impact to product quality. The product cannot be shipped
from the manufacturing site to the distribution centers or third parties until the NC has been
resolved. Depending on the deviation and corresponding NC Class (I, II, or III), the resolution
process can take from one week to several months. These delays lead to late shipments from
AML to the DCs. In 2014, AML experienced challenges with foreign particles [2] in finished
drug product production. The particle challenges have led to an increase in the number of NCs at
AML and greater lead time variability through DP production.
The increase in lead time variability through production caused by NCs has resulted in a
decrease in service level from AML to the DCs. The service level from AML to the DCs went
from over 90% in 2013 to around 70% in 2014. However, the safety stock levels at the DCs
were not adjusted to reflect the greater lead time uncertainty. As a result, inventory levels at the
distribution centers have been impacted, in some cases requiring expedited orders through
production to ensure supply. To date, Amgen has never had a stock out of any product, and
AML's exceptional track record has been key in ensuring consistent product supply to all
patients around the world.
The DP Cycle Time Improvement Initiative was launched by the supply chain group at
AML with the goal of improving service to the DCs while reducing customer lead time. The
initiative sought to enable the fulfillment of tender orders in less than five weeks while
facilitating the implementation of a broader corporate supply chain segmentation initiative. The
scope of the DP Cycle Time Improvement Initiative covers work in process (WIP) inventory and
13
production planning. However, changes to the manufacturing or NC resolution processes are out
of scope. There are separate teams at Amgen that are investigating how to reduce or eliminate
NCs in DP production. As such, the DP Cycle Time Improvement Initiative was focused on
staging inventory to buffer against NCs.
In order to reduce customer lead time, the DP Cycle Time Improvement Initiative
initially was focused on reducing the high levels of WIP at AML, which at times could account
for several months of demand. However, after analyzing the possible benefits of delayed
differentiation in DP production, it was decided to pursue a postponement strategy where these
high levels of WIP would be used to buffer the supply chain from the variability caused by NCs.
As part of the DP Cycle Time Improvement Initiative, the primary objective of the LGO
internship project was to evaluate the impact of postponement in the DP supply chain.
Specifically the goals of the internship project were:
*
Analyze lead time variability in DP production cause by Non-Conformances
(NCs).
*
Determine the appropriate postponement inventory levels to buffer against DP
lead time variability and improve the service level from the manufacturing site to
>90%.
*
Develop a process for adjusting postponement inventory levels that integrates
with existing supply chain planning activities
* Develop a simulation model to show how a postponement system operating on a
reorder point inventory policy would perform
1.2 Problem Statement
The DP Cycle Time Improvement Initiative and LGO internship project were created to
address the problems of below target service level and long lead time from AML to customers
(Amgen distribution centers and 3rd parties). AML targets a service level of greater than 90%.
However, in 2014 the service level from AML to company owned distribution centers was
around 70%. In addition, the customer lead time from AML is between three and four months.
14
For emerging markets with low forecast accuracy this long lead time results in frequent requests
for expedited orders and risk of stock out when there is significant overselling.
1.3 Thesis Overview and Hypothesis
To address the problems of below target service level and long lead time, a postponement
strategy, where inventory is held at an intermediary stage of DP production, is proposed. AML
has been utilizing postponement for several emerging markets for a number of years. This study
examines broadening the use of postponement at AML. The hypothesis of this thesis is that
AML can improve service level and reduce customer lead time, without significantly increasing
costs, by using a postponement strategy for syringe drug product. To test this hypothesis, an
analysis of DP production lead time was conducted to demonstrate the link between high lead
time variability from NCs and below target service level. Next, analytical and simulation
inventory models were created to determine the appropriate postponement inventory levels to
buffer against the lead time variability from NCs. Finally, the simulation model was used to
demonstrate the performance of the supply chain with the postponement strategy focusing on
service level, lead time, and inventory.
This thesis document is organized as follows:
Chapter 2: Operations at Amgen Manufacturing Limited (AML)
This section has a discussion of general background of Amgen and their current supply
chain along with an overview of drug product production at Amgen Manufacturing
Limited (AML), the company's largest production site.
Chapter 3: Literature Review
This section reviews postponement and provides examples of possible benefits.
Chapter 4: Lead Time Analysis - the Link between Service Level and Nonconformances (NCs)
This section analyzes the lead time through DP production from 2013 and 2014 and
demonstrates how the increase in the probability of receiving an NC and the NC
resolution time results in reduced service level.
15
Chapter 5: Postponement in Drug Product Manufacturing
This section describes how the implementation of postponement will impact operations at
AML. Also, the supply chain design that underlies the simulation model will be
examined.
Chapter 6: Simulation Modeling of DP Production with Postponement
This section describes the formulation of the DP Simulation Model that was used to
calculate postponement inventory levels and simulate the postponement supply chain
Chapter 7: Conclusions
This section explains how the postponement system will impact Amgen. Specifically, the
effect of postponement on service level, inventory, and lead time.
Chapter 8: Recommendations
This sections lays out recommendations for AML and other supply chains with high lead
time variability through production.
2
Operations at Amgen
Amgen is a biotechnology company that is committed to unlocking the potential of
biology for patients suffering from serious illnesses by discovering, developing, manufacturing,
and delivering innovative human therapeutics [3]. Amgen focuses on severe illnesses and
leverages its biologics manufacturing expertise to strive for solutions that improve patients'
lives. A biotechnology pioneer, Amgen has grown to be the world's largest independent
biotechnology company.
This section is an overview of the biotechnology (biotech) industry and Amgen. It also
provides an overview of biotech manufacturing.
16
2.1 Industry and Company Background
Biotechnology is the industrial use of living things, specifically genetically engineered
organisms. Biotech companies, such as Amgen, utilize biotechnology to create and manufacture
biopharmaceuticals, which are therapeutic products created through the genetic manipulation of
living things, including (but not limited to) proteins and monoclonal antibodies, peptides, and
other molecules that are not chemically synthesized, along with gene therapies, cell therapies,
and engineered tissues [4].
Amgen was founded in 1980 and today is one of the world's leading biotech companies.
The company had revenues of $18.7 billion in 2013 and approximately 20,000 employees
worldwide [3]. The company's mission is "to serve patients" which it does by discovering,
developing, manufacturing, and delivering innovative medicines to patients around the world.
The company is head quartered in Thousand Oaks, CA and has operations worldwide.
Amgen's strategy is to target severe illnesses, as such, the company's portfolio of ten
products address areas such as autoimmune diseases, oncology, and hematology. As shown in
Figure 2-1, the majority of revenues are generated by five blockbuster drugs Neupogen@ and
Nuelasta@ (treat infections in cancer patients); Enbrel@ (treats rheumatoid arthritis); and
Aranesp@ and Epogen@ (treat anemia).
Product Sales
Sales by Geography
2013, 2012,2011
$18.28
$15.38
wNeuiasta
xNEUPOGEN&
*ENBREL@
EPOGENO
A, Aranesl@
*XGEVA/Prcha@
se-niamimr
Other Pmaots
2013
1112
2011
Figure 2-1: Amgen sales by product and geography [31
17
Over the past several years Amgen has been pursuing an international expansion
initiative with the goal of serving more patients around the globe. The company has traditionally
focused on the North American and European markets. However, recent emphasis has been
placed on international markets as a source of growth. Despite the recent international push, the
majority of the company's revenues are derived from the U.S., as shown in Figure 2-1.
2.2 Biopharmaceutical Manufacturing
Manufacturing of biopharmaceutical drugs, also known as biologics, is a complex
process that requires strict adherence to procedures and stringent process controls [5]. The
following is an overview of this process with a focus on the Fill/Finish manufacturing steps.
2.2.1
Drug Substance Production
The production of biologics follows four major steps which are shown in Figure 2-2.
First, after years of extensive research and development the master cell bank is created. This is a
bank of cells that have been genetically engineered to produce the protein or biologic drug of
interest. The master cell line is replicated to create the working cell bank, which are the cells
that will be used in the production process. In the upstream phase, the cells from the working
cell bank are grown in a series of progressively larger containers or bioreactors. During this
nhase, the cells are akso producing large amounts of the therapeutic protein that will form the
final drug product. After reaching the final bioreactor and producing the desired amount of
therapeutic protein the cells enter the downstream phase of drug substance production. In this
phase, the slurry of cells is run through centrifuges, chromatography columns, and filters to
isolate the therapeutic protein from the other components that make up the cells. The resulting
isolated and purified therapeutic protein is called bulk drug substance (DS). Finally, the bulk
drug substance enters the Fill/Finish phase where it is diluted to the desired concentration and
formulated with stabilizing agents to form the final drug product that has been approved for
patient use.
18
Coil Lnes:
A#eS
CeOLi PrOdUCW
Upstream Phase:
T
Downstream
Pro
Phase:
aloo 0800& Pwannaon
Drug Substance (DS)
----------------------------------Drug Product Phase
Pmriojekn for Hwaans
ProdoctDrugu
-------------------------------------------------------
Product (haP)
Drug Product (DP)
Figure 2-2: Process steps to create and produce biopharmaceuticals
2.2.2 Drug Product Manufacturing
The drug product phase of biopharmaceutical production is the primary interest of this
thesis. During this phase, product is formulated and filled into the vials and syringes that will be
packaged and distributed to patients. Figure 2-3 shows the primary drug product manufacturing
process steps. The process begins with Formulation. During Formulation, the therapeutic
protein or DS is put into a mixture of inert components (buffers, stabilizers, and other excipients)
to help ensure that it remains stable during distribution and storage. This solution is then passed
to a filling line where vials and syringes are filled becoming drug product (DP). At this stage the
vials and syringes do not have any identifying markings or labels so are referred to as nude vials
and syringes.
Next, the nude vials and syringes are sent through an inspection line, where automated
inspection equipment checks for characteristics such as color, turbidity (clarity), and foreign
particles. The inspection machines can be programmed to perform different tasks depending on
the batch of medicine being tested. Automation allows the inspection of more units of product
faster. Yet, it does not fully replace manual inspection. Units that provide test results at the
19
boundaries of tight specifications are visually re-inspected twice by highly trained staff. After
passing the inspection process, units are referred to as inspected drug product (IDP).
After inspection the units move to the packaging lines where nude vials and syringes are
labeled and then packaged into "packs" or boxes containing from one to ten vials or syringes.
All labels, inserts, and packaging materials are printed in specific languages and meet the
specifications of the country in which the product will be sold. Finally, before the medicines can
be shipped to customers each lot or batch (all of the units that were produced in a given filling
run) goes through a disposition release process, which is a final check that all of the testing and
procedures for a given batch did not have any deviations that would require additional
investigation. At this stage, product is referred to as finished drug product or FDP. The last step
is shipping to distribution centers, wholesalers, and ultimately distributed to patients.
Drug Product Manufacturing
DP
FDP
IDP
Figure 2-3: Drug product manufacturing process steps: formulation, filling, inspection,
packaging, and disposition (release). Intermediate product stages are as follows drug product
(DP), inspected drug product (IDP), and finished drug product (FDP).
3
Literature Review
Postponement or delayed differentiation is a form of risk pooling often referred to as lead
time pooling. Fundamentally, all forms of risk pooling seek to reduce the uncertainty that a firm
faces or hedge uncertainty so that the firm is in a better positon to mitigate the consequence of
20
uncertainty [6]. The following is an overview of lead time pooling through postponement and its
associated benefits.
3.1 An Overview of Postponement
Postponement or delayed product differentiation can be viewed as a strategy for a
company to improve the service level and reduce inventories [7]. It is an organizational concept
where products are held at an intermediary stage of production allowing some of the activities in
the supply chain to be performed after customer orders are received. This allows companies to
hold smaller amounts of finished goods inventory because postponement hedges the uncertainty
associated with product variety while not reducing the customer's variety [6].
A push-pull supply chain strategy is central to the concept of postponement [8]. In a
push-pull strategy some stages of the supply chain operate in a push-based manner, where
parts/products are processed based on a forecast. The remaining stages employ a pull-based
strategy, where the processes are not performed until a customer order is received. The pushpull boundary, also known as the differentiation or decoupling point, is the interface between the
push stages and the pull stages. Figure 3-1 shows an illustration of the push-pull boundary
concept.
Push-Ril
Boundary
Pull
Segment
Push
Segment
Figure 3-1: Illustration of the push-pull supply chain strategy
In the push section of this strategy, demand uncertainty is relatively small due to pooling.
Thus, managing this portion based on long-term forecast is appropriate [8]. The focus can
therefore be placed on cost minimization through mass production. Conversely, the pull section
21
will have higher demand uncertainty and therefore this section of the supply chain needs to be
flexible and responsive to quickly respond to demand.
In general, postponement is an ideal strategy [6] when:
1) Customers demand many versions - variety is important
2) There is less uncertainty with respect to total demand than there is for individual
versions
3) Variety is created late in the production process
4) Variety can be added quickly and cheaply
5) The components needed to create variety are inexpensive relative to the generic
component
3.2 Benefits of Postponement
When properly implemented, postponement can significantly reduce inventory levels
while improving supply chain flexibility and responsiveness. In the paper, Evaluation of
postponement in the soluble coffee supply chain: A case study, Wong et al quantifies the benefits
of postponement in the soluble coffee supply chain. The differentiation point chosen for the
soluble coffee supply chain is before final labeling and packaging. A similar differentiation
point is proposed for the drug product supply chain at Amgen which is why this example was
reviewed. Figure 3-2 shows the differentiation point for the soluble coffee example, where a
generic "nude jar" is held in inventory and then labeled and packaged based on customer
demand.
22
6 Jar Case
12 JarCase
____24Ja~s
6 Jar Case (Alternative Pack)
Coffee
Blend
F, 1 Og jar
6 Jar Case (Promotional Pack)
12 Jar Case (Promotional Pack)
24 Jar Case (Promotional Pack)
6 Jar Case (Specific Customer)
12 Jar Case (Specific Customer)
24 Jar Case (Specific Customer)___
Figure 3-2: An example of a generic product and its variants for the soluble coffee supply chain
differentiation point 191
Utilizing postponement at the generic jar level, Wong et al. calculated a potential
aggregated safety stock reduction of 61.1% using the square root law. The square root law is
commonly used in supply chain management. It states that if N individual SKUs have demand
that is independent (zero correlation) and identically distributed, then risk pooling reduces safety
stock levels by the square root of N [10].
However, correlation of demand and the relative size of demand standard deviation for
the final SKUs can significantly impact the potential inventory reduction [9]. When there is a
positive correlation in demand between SKUs, the standard deviation of the aggregated demand
is larger and hence the reduction in safety stock is lower [11]. Additionally, the ratio of demand
standard deviations also influences the potential safety stock reduction. Large magnitudes
resulting when the standard deviation for one SKU is much larger than another SKU will lead to
smaller reductions in safety stock [12]. When correlation and relative size of demand variability
were considered in the soluble coffee example the potential aggregated safety stock reduction
was only 46.1% [9].
Lead time pooling through postponement can also significantly reduce the lead time
while still resulting in inventory reduction, especially for systems with long lead times. In their
book, Matching Supply with Demand, Cachon and Terwiesch present an example of lead time
pooling that cuts lead time and also diminishes inventory levels. In the example, a consolidated
distribution strategy is utilized where inventory is held in a central distribution facility as oppose
23
to only at the store locations. Figure 3-3 is a depiction of both a direct from supplier system and
a consolidated inventory system which utilizes a central distribution center.
Direct from Supplier
8-Week
Store 1
Lead Time
Supplier
0
0
Store 100
Centralized Inventory in a Distribution Center
Supplier
8-Week
I-Week
Lead Time
Lead Time
Store 1
0
Retail
DC0
Store 100
Figure 3-3: Example of lead time pooling through the use of a centralized distribution center
adapted from Cachon, G. and Terwiesch, C. 161
In the case of DP production at Amgen, the Supplier blocks shown in Figure 3-3
represent the DP manufacturing facility (AML), the Retail DC represents postponement
inventory at inspected drug product, and the Store 1.. 100 represent Amgen distribution centers
and third parties. In this case the distribution centers (Store 1.. 100 in Figure 3-3) experience a
seven week reduction in lead time, which would enable them to respond much quicker to
downstream demand.
The scenario depicted in Figure 3-3 and explained above results in a reduction in
inventory due to lead time pooling. For the two scenarios depicted in Figure 3-3, assume each
store is replenished using an order-up-to model and targets a 99.5% in-stock probability. Also,
demand for a single product occurs in 100 stores and average weekly demand per store follows a
Poisson distribution with a mean of 0.5 unit per week. When the two systems are compared, the
24
consolidated-distribution strategy is able to reduce the expected inventory by 28% relative to the
direct-delivery structure. Figure 3-4 shows how the inventory required for each strategy changes
with lead time. For the case depicted, as the lead time moves beyond four weeks the
consolidated distribution strategy requires less inventory than the direct-delivery strategy.
- - - Direct Delivery
-
Consolidated Distribution
700
600
or
500
1116 1
d'O
OP
400
0
S300
200
100
-
0
0
2
6
4
8
10
Lead Time from Supplier (in weeks)
Figure 3-4: Inventory with the consolidated distribution supply chain and the direct-delivery
supply chain with different supplier lead times adapted from Cachon, G. and Terwiesch, C. 161
In Amgen's case, the lead time from AML to the distribution centers is 14 weeks. Thus,
a supply chain strategy that takes advantage of lead time pooling could have a significant impact
on inventory levels.
25
4
Lead Time Analysis - the Link between NCs and Service Level
The following section details an analysis of lead time through DP manufacturing for
commercial syringes produced at AML. The analysis shows that an increase in the likelihood of
nonconformances (NCs) in syringe DP production at the end of 2013 resulted in a decrease in
service level from AML to Amgen distribution centers.
Drug product manufacturing is highly standardized and controlled and as a result the
processing time through each production step has low variability. As shown in Figure 4.1,
syringe DP production is comprised of five primary steps: Formulation, Filling, Inspection,
Packaging and Release. Production is carried out in batches of 20,000 to 250,000 syringes or
units. The batch sizes are dictated primarily by the size of the formulation tanks for a given drug
product. The majority of Amgen's commercial products have fixed batch sizes. Given the fixed
batch sizes and highly controlled operating environment, the cycle time through the major
production steps have a coefficient of variation of less than 0.5 with an average cycle time of
around 12 hours. As such, the actual processing time of the drug product is not a major source of
supply lead time variability.
Form
CT15r
Fill
T=1r
RPacks
'Inspect
1
CT=11hr
CT=2wks
NC
NCNC
Resolution
Resolution
Rsou
n
CT=wksCT=wksCT=4wks:
Figure 4-1: Drug product manufacturing process flow with hypothetical average cycle time (CT)
for each process step
As shown in Figure 4-1, the primary source of variability in syringe DP Production
comes from the generation and closure of NCs. A nonconformance is issued when there is an
event that deviates from an approved GMP requirement, such as a procedure, specification, or
operating parameter, and/or an event that requires an investigation to assess impact to product
quality. For example, if during the formulation process a property of the drug product solution,
such as pH, exceeds its control limits then the batch will receive a nonconformance. A
nonconformance can be generated at any point in the production process including incoming
26
inspection of production materials as well as in-process testing. However, in syringe DP
production the majority of NCs are generated during Filling and Inspection.
Once a NC has been issued, it is assigned to an NC Closure team for resolution before the
batch can proceed to the next stage of production. NCs are designated into three categories
(Class I, Class II, and Class 1II), which require varying degrees of investigation. Class I NCs are
the least severe. They are generated to address deviations from procedure that do not impact the
product quality/safety. A typical Class I NC could come from a reporting mistake such as a
blank field in a batch record or test report. Class II and III NCs are generated for deviations that
may impact product quality. These investigations are more involved and often require additional
inspection or testing before the NC can be resolved. The NC resolution process may last a few
days, for a simple Class I or several months for a severe Class 1I or Class III. Given these long
resolution times, Class IL and Class III NCs are the major source of variability in DP production.
4.1
Probability of an NC in syringe DP production
In order to assess lead time variability in syringe DP production, it is critical to determine
the probability of receiving an NC. Historical production data from 2013 and 2014 was analyzed
to determine which batches received an NC and the duration of the corresponding resolution
process. This data could then be used to construct a histogram for lead time through DP
manufacturing. As seen in Figure 4-2, only NCs generated in Formulation, Filling, and
Inspection were considered in this analysis. The rationale for this decision was twofold. First,
NCs in these areas account for 90% of the NCs in DP production. Second, understanding the
lead time through these steps is required in order to calculate the IDP inventory in the proposed
postponement system. Through this analysis it was determined that the probability of a batch
receiving an NC during Formulation, Filling, or Inspection could be as high as 80%.
27
30-60% of batches
-
40-70% of batches
Figure 4-2: Drug product manufacturing process flow with probability of receiving an NC in
Formulation, Filling, or Inspection
Foreign particles challenges in drug product production led to an increase in the number
of NCs at AML. Figure 4-3 shows the impact of the particles challenge on NC generation. From
2013 to 2014, there was a 20% increase in the likelihood of receiving a Class II or III NC for
certain products. Because Class II and III NC can take several weeks or months to resolve, this
increase in the percentage of lots receiving a Class II or III NC has a significant impact on lead
time through DP production.
Percentage of AML Commercial Syringe Fill Lots with an NC
a No NC a Class I nuCass 2 aCass 3
2014
2013
Figure 4-3: Percentage of commercial syringe lots receiving NCs in 2013 and 2014
28
The increase in likelihood of generating an NC occurred during the fourth quarter of 2013
and has remained significantly higher than prior levels (before syringe particle challenges). In
the first three quarters of 2013, NC rates in syringe DP production were near their historical
averages and the service level from AML to the distribution centers was above the 90% target.
However, FDA particle restrictions [2] went into effect in the fourth quarter of 2013 and, as
shown in Figure 4-4, there was a significant increase in the probability of receiving a Class II or
Class III NC in syringe DP production. The probability of an NC has tapered off slightly from
the high point (Q1 2014) but is still significantly higher than before the particles challenges.
Crr
.2...
.....
4-.....
1.
CC
-
.....
s
F.g.re 4e
bis
W
Ii
frm1
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01
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beudrtodi
Q
21
okown
rdrt
h
ieioo
frciiga
mat
etrieth
fNsonD
NteN
rduto
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ea0ie
L29
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t NCimy urtero
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4-4ePserend comerial
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Thlas oias
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batcrfhe
and cmeria
Fre4vingtercsse
fromQi 213 hrouh
Q3201
ienest
the NC Resolution Teams. In order to resolve an NC, the NC Team follows strict protocols for
assessing the issue and any potential impact to product quality/safety. Class II and Class III NCs
will often require additional inspection and testing in order to reach a resolution. This process
can be very time consuming and as such leads to significant variability in DP production time.
Histogram of Cassl INC Age 2013, Cass INC Age 2014
36
24
12
0
25-
Class
1 NC Age 2013
30-
48
Class I NC Age 2014
25
20-
15.
101.0
24
36
48
Figure 4-5: Histograms of Class I NC resolution time in days for commercial syringe batches
produced from January 1 through May 31, 2013 and 2014
Figure 4-5 shows histograms of Class I NC resolution time for commercial syringe
batches produced from January 1 through May 31, 2013 and 2014. Because Class I NCs are
resolved in less than two weeks they are not a major source of lead time variability.
30
Histogram of Class 2 NC Age 2013, Class 2 NC Age 2014
0 60 120 180 240 300 360
70-se
Class 2 NC Age 2013
2 NC
2014
60.
50
so-,
40-
40
to.
0-
-
10
0
60 120 1io
240
300
0360
Figure 4-6: Histograms of Class II NC resolution time in days for commercial syringe batches
produced from January 1 through May 31, 2013 and 2014
Figure 4-6 shows histograms of Class II NC resolution time for commercial syringe
batches produced from January 1 through May 31, 2013 and 2014. As seen in Figure 4-6, Class
II NCs can take several weeks or even months to resolve. When coupled with the fact that for
some products 50% of batches receive a Class II NC, it is apparent that Class II NCs are a major
source of lead time variability in DP production. With the FDA particle restrictions this has
become more severe. From 2013 to 2014, both the probability of receiving a Class II NC and the
Class II NC resolution time have increased.
Histogram of Class 3 NC Age 2013, Class 3 NC Age 2014
Soo
iso
20
250 300 3W0 400
ass 3 NC Age 2014
Gass 3 NC Age 2013
3.0
2.5
1.0-
3
2-
0.5
Figure 4-7: Histograms of Class III NC resolution time in days for commercial syringe batches
produced from January 1 through May 31, 2013 and 2014
31
Figure 4-7 shows histograms of Class III NC resolution time for commercial syringe
batches produced from January 1 through May 31, 2013 and 2014. Although these NCs
typically take several months to resolve they historically affect less than 2% of production lots.
Therefore, they are not a major contributor to DP manufacturing lead time variability.
4.3 Impact of NCs on service level
It has been shown that a large percentage of batches receive NCs which can take several
weeks or months to resolve. This adds significant variability to the overall lead time for DP
production. Since the FDA particle restrictions, the impact of NCs on DP production lead time
has been more severe. Figure 4-8 shows lead time distributions for commercial syringe batches
produced in the first two quarters of 2013 and 2014. These are hypothetical distributions
because they assume a seven day cycle time through formulation, filling, and inspection. In
actuality, many of these batches took longer than seven days to proceed through these process
steps because they sat as work in process inventory between the steps. If a postponement system
is implemented inventory will be work in process inventory will be prohibited between process
steps so the hypothetical lead time distributions assume a seven day cycle time through
formulation, filling, and inspection.
LT No C3 or Out (Q1,Q2 2013)
UU-
-30 0 30 60 90 120
LT No C3 or Out (Q1,Q2 2014)
LT No C3 or Out (Q1,Q2 2013)
Mean 2D.85
SO"
71.9101
22.27
N
178
LT No C3 or Out (Q1,Q2 2014)
Mean 3.30
st ev 2L35
70-
60
N
44.3182
176
403020-
...55
10-
0
-30
.41
0
30
60
90 120
Figure 4-8: Hypothetical lead time through DP production from Formulation through Inspection
including NC Resolution for commercial syringe lots produced from January through May 2013
and 2014
32
As shown in Figure 4-8, 93% of commercial syringe batches in 2013 would have a lead
time of less than 60 days compared to only 78% of commercial batches in 2014. This difference
significantly impacted the service level from AML to Amgen distribution centers. Figure 4-9
shows the percentage of commercial syringe batches that received Class II or Class III NCs
plotted with the service level from AML to Amgen distribution centers by quarter for 2013 and
2014. A sharp increase in the percentage of batches receiving Class II or Class III NCs in the
fourth quarter of 2013 led to a significant reduction in service level to the DCs.
Commercial Syringe Batches with Class 2 or Class 3 NCs
and Service Level to the DCs for 2013-2014
-
%batches with Class 2 or Class 3 NC
.
-
Service Level from AML to DCs
100%
100%
0.......
.....
....
119j
........ 95% ...
0
..............
......... ...
80%
U
z
70%
70%
60%
60%
*
\
50%
5 %
L .......... .........
0
%
4
50%
40%
Jc
40%~
4:,
A -"4
30%
30%
20%
20%
.
10%
-
..--
-.-.
10%
0%
0%
1
2
3
5
4
6
7
8
Quarter (1 = Q1_2013)
Figure 4-9: Commercial syringe batches with Class II or Class III NCs and service level from
AML to Amgen distribution centers
33
a
To address the reduction in service level that resulted from an increase in NCs, a
postponement system is proposed that will hold inventory at inspected drug product, which will
buffer the system from NCs in Formulation, Filling, and Inspection.
5
Postponement in Drug Product Manufacturing
This section describes Amgen's current DP supply chain with and without postponement.
Again, this thesis examines expanding the current use of postponement to cover more drug
product SKUs.
5.1 Current DP supply chain
This section will provide an overview of the current DP supply chain and production
planning processes. The major manufacturing processes and supply chain performance
measures, such as inventory, lead time, and service level are discussed.
5.1.1
DP Manufacturing
Drug product manufacturing is comprised of five major steps. The process begins with
Formulation. During Formulation, the therapeutic protein or DS is mixed with inert components
(buffers, stabilizers, and other excipients) to dilute the DS to the proper dosage and ensure that it
remains stable during distribution and storage. The next step is Filling, where vials and syringes
are filled with the formulated solution becoming drug product (DP). The third step in the
process is Inspection, where the filled batch of syringes is sent through automated inspection
equipment that check for various characteristics such as color, turbidity (clarity), and foreign
particles. If no NCs were generated during Inspection, then the batch can be sent through
Packaging, the fourth stage of production. In the packaging lines, nude vials and syringes are
labeled and then packaged into "packs" or boxes containing from one to ten vials or syringes.
All labels, inserts, and packaging materials are printed in specific languages and meet the
specifications of the country in which the product will be sold. The final stage of production is
the disposition release process which is a final check that all of the testing and procedures for a
given batch did not have any deviations which would require investigation. Once the product
34
has been released by the disposition team, it is then shipped to the distribution centers,
wholesalers, and ultimately distributed to patients.
Currently, the average flow time' for a batch of syringes is around 100 days. However,
the actual value added time, or part of the processing time when value added activities are being
performed, is only 2.2 days. Unplanned WIP inventory before and after Inspection accounts for
the majority of the non-value added time. Typically, batches will receive an NC in Filling or
Inspection and are not permitted for further processing until the NCs have been investigated and
resolved. The result is large unplanned WIP inventories before and after Inspection. Figure 5-1
is a process flow map of the DP supply chain that shows the major manufacturing steps and WIP
inventory.
AML Syringe DP Manufacturing
Avg Flow Time: 100 days
AML to Amgen DCs
SL Target: 90.0%
Amgen DCs to Customers
Service Level: 99.99%
Value Added Time: 2.2 days
WIP: 3 MFC
SLActual: <70%
Lead Time: 3 months
Lead Time: 3 days
DC Inventory: 3-6 MFC
AML DP Manufacturing
FIm
c
0A
DC IA
m*
E*A*l
R
DC 2
A
DC3
A
ocn
A
Custome
Note on acronyms: Months forward coverage (MFC), Service Level (SL)
Figure 5-1: Amgen DP supply chain
5.1.2 From manufacturing to the customer
After a batch has been released, it is shipped from AML to Amgen distribution center or
third parties. Over 95% of the products produced at AML are sent to Amgen distribution
centers, so the remainder of this section will focus on this leg of the supply chain.
All Amgen distribution centers, except for those serving the U.S. market, operate on a
make to order (MTO) system, where they place orders with AML by submitting a stock transfer
order (STOs). The agreed upon lead time between AML and the DCs is current plus three
months. AML has to deliver plus or minus 10% of the order quantity anytime during the
a flow unit (batch of syringes) spends in the process, which includes the time it is
worked on at various resources as well as any time it spends in inventory
I Flow time measures the time
35
requested delivery month in order to meet their service requirements. Adherence to these
requirements is how Amgen measures service level from AML to the DCs. The target service
level from AML to the DCs is 90%, however due to the sharp increase in NCs the current service
level is around 70%.
Amgen's sales and operations planning (S&OP) activities leverage SAP enterprise
resource planning (ERP) and Kinaxis Rapid Response Supply Chain Planning software. As
such, AML's supply chain team has visibility to the demand from the distribution centers and can
preemptively schedule batches in advance of an STO in order to balance production. Also for
distribution centers serving the U.S. market, the AML supply chain team manages the inventory
directly in a make to stock (MTS) system. In this system, performance is measured by the
percentage of FDP SKUs whose inventory levels at the U.S. DCs are at or above their targets.
The goal is to have greater than 90% of SKUs above their target inventory level. However as
with the MTO DCs, less than 75% of the U.S. distribution center's SKUs are at or above their
targets.
Amgen targets an extremely high service level from its DCs to customers (on the order or
99.99%). Pharmaceutical distributors, such as Cardinal Health, are typically the DCs' direct
customers. The DCs hold large amounts of inventory in order to fulfill distributors' orders
immediately. The distributors ensure that Amgen's medicines get to medical providers and
ultimately to patients. Thus, fulfilling Amgen's mission of "every patient every time".
To achieve such high service levels, Amgen distribution centers typically hold between
three and six months of inventory to buffer against supply and demand variability as well as for
strategic reasons, such as buffering against natural disasters or other unpredictable events. As
shown in Figure 5-2, safety stock falls into two categories, Operations Safety Stock (OSS) and
Strategic Safety Stock (SSS). Operational Safety Stock is held to buffer against supply and
dmnan Variaillity whime Strategic Safety
StOCk
is
11eLdO
emergencies.
36
buffer against unplanned events or
MaXinum LOv*
Exoiry mlnimae i vshef Ie (RSL4
reqrefents(Scraptmad
LL.
Upper Planning Limit
-
.....
.......
------------
Low
4! ol Nom w.
0
annvil Um, t +60% ofteyc)e stoe*
Lower Planning Limit
tota safety stocltoren oeiabno
Seategie
retes.d imertornly
Opwoa SAVy Stok
....... Mfnimum Lev*1
mnivminentyry re.treto mtgate .gaist
Contamiation &nMwrel CksGSleqc
loom
Time
Figure 5-2: Hypothetical inventory levels including planning limits
5.2 DP supply chain with postponement
This section provides an overview of the proposed DP supply chain with postponement at
IDP. The system described below is very similar to the way postponement is currently being
implemented at AML.
5.2.1
DP Manufacturing with postponement
Moving to a postponement system does not alter any of the manufacturing operations
(Formulation, Filling, Inspection, Packaging, or Disposition) but only where WIP inventory is
held in the system. Currently, inventory builds before and after inspection. With postponement,
WIP inventory will only be held after inspection at the Inspected Drug Product (IDP) stage of
production. IDP is a nude syringe that has been inspected and is awaiting labeling and
packaging, an example photo is shown in Figure 2-3. Batches will move through the first three
manufacturing stages (Formulation, Filling, and Inspection) targeting a goal of single batch flow
- ensuring that WIP inventory does not build between the stages. Along with monitoring the
inventory levels between these stages, a new metric of Filling through Inspection cycle time
could be monitored in order prevent unwanted inventory accumulation.
37
Moving to this single batch flow system requires changing the current NC protocol that
prevents further processing of a batch that has a pending NC. In order to operationalize single
batch flow, batches that receive an NC in Formulation or Filling need to be allowed to proceed
through Inspection without the NC being resolved. After discussions with the quality,
manufacturing, and supply chain teams, it was determined that this procedural change is feasible.
A second modification to the manufacturing process is the addition of an NC check after
inspection. This check would be performed by members of the quality and manufacturing teams
that would essentially review the batch records for the given production lot in order to determine
if the batch has an NC. As shown in Figure 5-3 if the batch does not have an NC, then it is
transferred into postponement or unrestricted IDP inventory, where it would await packaging. If
the batch has been tagged with an NC, then it is designated as restricted and cannot be packaged
until the NC is closed or the batch is released for further processing.
(
LIO
Pice r
em
YES
Make-to-Order model ONLY
One Piece "Flow'
Figure 5-3: DP manufacturing with postponement at IDP
5.2.2 From manufacturing to the customer with postponement
The major impact of postponement on the broader supply chain is modified supply
planning, reduced lead time from AML to the DCs, and potential reductions in FDP safety stock.
Figure 5-4 is a comparison of the current DP supply chain and the proposed DP supply chain
with postponement. In the postponement system, the IDP inventory represents the push-pull
boundary. Orders from the distribution centers and third parties would trigger packing, release,
and shipment while production upstream of IDP would be planned using forecasts. This is a
deviation from the current system where the DCs submit orders to AML which trigger the
formulation and filling process for a given batch. However as noted in section 5.1.2, Amgen's
S&OP systems provide AML's supply chain team visibility to the DCs demand and forecasts.
38
As such, upstream production (Formulation, Filling, and Inspection) can be planned and
executed based on forecasts.
The customer lead time from AML to the DCs is dramatically reduced with the
postponement system. In the current system the target lead time to the DCs is current plus three
months. This long lead time is required because the lengthy flow time through DP
manufacturing. However, the majority of the flow time is dedicated to resolving NCs from the
Formulation, Filling, and Inspection manufacturing stages. Holding a supply of unrestricted IDP
inventory (product that has moved through Formulation, Filling, and Inspection including NC
Resolution if required), allows for rapid packaging and shipment upon receipt of an order from
the DCs. Because there are few NCs in syringe packaging and the process steps have short cycle
times, the overall lead time from order placement to delivery could be reduced to as little as three
weeks. This is a significant reduction from the current lead time which is greater than three
months.
The dramatic reduction in lead time, could enable the DCs to reduce their safety stock
levels. The current long lead time requires the DCs to hold significant safety stock to buffer
against demand variability during lead time. Reducing the lead time would in turn reduce the
amount of safety stock. In the example detailed in section 3.2, using a consolidated distribution
strategy (a lead time pooling strategy similar to postponement) led to an inventory reduction of
28% compared to the direct distribution strategy when the lead time was reduced from eight
weeks to one week. In Amgen's case the lead time would be reduced from 13 weeks to around
three weeks.
39
Current DP Manufacturing
DcI
A
DCC2user1
DCt
Autmer
A *7
Dc1
A
DC 3
DP Manufacturing with Postponement
Customers]
One Piece low"
Maketo-Order model ONLY
AML Syringe DP Manufacturing
AML to Amgen D Cs
Amgen DCs
"Push" to and "Pull" from
Lead time from 3
unrestricted IDP inventory
months to 3 week s
Potential reduction in DC's
safety stock levels
Figure 5-4: The current DP supply chain and proposed supply chain with postponement
In order to determine if the proposed postponement supply chain is feasible, the IDP
inventory levels need to be determined. If the IDP inventory levels required to execute the
postponement strategy are much larger than the current WIP inventory levels, then the increased
inventory cost may not be justifiable. In order to determine the appropriate IDP inventory levels
and understand the performance of the system, a simulation model was created of the DP supply
chain from manufacturing to the DCs.
6
Simulation Modeling of DP Production with Postponement
This section describes the formulation of the DP Simulation Model that was used to
calculate postponement inventory levels and simulate the postponement supply chain. An
overview of the simulation model and results that were generated are discussed. The model
setup and results described in Section 6 are based on a hypothetical syringe product produced at
AML referred to as Product X.
40
6.1 Model formulation and inputs and outputs
The DP Simulation Model is an Excel based model that uses Palisades @Risk plug-in to
simulate the DP supply chain with postponement that is described in section 5.2 and shown in
Figure 6-1. The model works by simulating the replenishment of postponement inventory, at
IDP, that follows a continuous review reorder policy. In a continuous review system, an order is
placed when the inventory position reaches a particular level or reorder point (ROP). The
inventory position at any point in time is the actual inventory in the warehouse (on-hand
inventory) plus any batches that are in production (pipeline inventory).
-----------
DP Manufacturing with Postponement
One Piece "FloW
J
A
A
IDDC
J)C3
Dc 3
N2A
Dc n
YES
Make-to-Order
Dc I
model ONLY
A
1A
Figure 6-1: Process flow diagram of the DP supply chain that underlies the DP Simulation Model
Figure 6-2 is a plot that was generated by the DP Simulation Model that shows how the
system performs. Whenever the Inventory Position (green line) drops below the Reorder Point
(purple line) a new filling batch is ordered. The new batch is randomly assigned one of four
designations No NC or a Class I, Class II, or Class III NC based on probabilities that are entered
on the "Input" tab of the DP Simulation Model. Each designation has a corresponding lead time
probability distribution based on historical data. So, the lead time for each new batch is
randomly assigned based on the probability of receiving an NC and the corresponding
production and NC closure time. A stockout occurs when the On-Hand Inventory cannot satisfy
the demand. The model tracks the number of stock outs over 100 periods and uses this
information to calculate the service level and fill rate based on the input settings of the model.
41
IDP Inventory Simulation
-U-Demand
-4-On-Hand Inventory (end of month)
-*-
-44-Reorder Point
Inventory Position
250000
200000
--
2150000
E
-
-.-.-.-.-......-.
C
%.0
~100000
,
- - -1 -1 -A
--A1
1 1 1 -I l
50000
0
1
2
3
4
5
6
7
8
9
10 11
12 13
14 15
16
17
18
19 20 21
22
23 24
Months
Figure 6-2: Plot from DP Simulation Model that shows the inventory levels for a hypothetical IDP
SKU
The model was created to determine the appropriate IDP inventory levels for
postponement and to evaluate the strategy's feasibility. Additionally, the model was developed
as a tool that future Amgen staff could use to rebalance postponement inventory levels as supply
lead time and demand change. The model is setup so that input data can be easily transferred
from Amgen's existing supply chain planning systems (Rapid Response and SAP).
As shown in Figure 6-3, the model is comprised of an "Input" tab and a "Simulation" tab
as well as "How to use" and "Definitions" tabs which provide instructions for future users.
42
I
2
3
C
Ar
d
D
Instructions for IDP Inventory Simulation Model Workbook
Overview
The IDP Inventory Simulation Model simulates a continuous reiew inventory reorder system
for sytinge drug product (DP) 10P. The user inputs values for beginning inventory, supply
lead time distribution, demand, batch size, and reorder point Based on these inputs the
model determines the corresponding service level and il rate that would result The inputs,
specifically the reorder point, can be adjusted to give the desired service levet
4
5
7
Data sources
9
1) .. Dmd- Executve Demand Report in RapidResponse
Report in RapidResponse that can be used to determine the forecast demand for AML DP
SKUs for the next 12 months
10
11
12
13
14
15
16
17
2) Lead Tine AnaiysisF g to ADP_2013-2014 Excel Mrkbook
An analysis thatshows how to determine the lead time distribution for AML syringes from
Filling to DP (unrestricted)
Notes on formating of this workbook
18
19
Instructions
20
Instructions for IDP Inventory Simulation Model
21
1. input tab-fillIn all inputcelisshownin
22
23
?4
40
How to use
Input
simutation
Figure 6-3: Screen capture of the DP Simulation Model "How to use" tab
6.1.1
"Inputs" tab
Probability distributions for supply lead time and demand are required in order to
calculate the IDP inventory levels using a continuous review reorder point policy. The data
required to construct these distributions is entered on the "Inputs" tab. Figure 6-4 shows a screen
capture of the DP Simulation Model "Input" tab.
43
Multiperiod Inventory Simulation - Input Parameters
inni
n
:
Demand for Next 12 Months
PUobabilrty of an N
Lowe
wr
Proabdes
2Month
StDwv
Leadtime Distributions
No NC
clans I
Class 2
Ckme
Figure 6-4: Screen capture of the DP Simulation Model "Input" tab
In Figure 6-4, all of the cells highlighted in yellow are inputs. The first field is Beginning
Inventory. This is the amount of IDP inventory at the start of the simulation. This amount can
be pulled from Rapid Response, Amgen's supply planning system. For Product X a beginning
inventory level of 580,000 was selected because it is greater than the proposed Reorder Point for
this product.
The next input field is the probability of an NC. Based on historical production data for
each product, the user enters the probability that a new batch is produced without an NC or
receives a Class I, Class II, or Class III NC. For Product X the probability of receiving No NC,
Class I, Class II, or Class III was 38%, 15%, 45%, and 2% respectively.
Using @Risk, a lead time distribution is assigned for each production designation No
NC, Class 1, Class 2, or Class 3. Figure 6-5 shows the @Risk "Define Distribution" dialog box
that is used to assign a distribution to each production designation.
44
Multiperiod Inventory Simulation - Input Paramete
Inve
N,~ ass
A:
I~ecnn~aEOQ~tNV
BeI.nning
A
@RISK -
s
CFA
kLofnormnS3.544 54.947 Risk5hift12.59111
Ladan
Class 2dass2
ognom599,R
Parameters
Leedtine Distributions
NMC I
- M J=
DWfine Disribution: D25
as
Standard
F%
jCeu2[
:akpD25
Mnun
54947
AMI
'4.
Stats
Clw
+1259
66.14
3079
49.96
54.95
4m
LOW
GaD
0ev
KTrial Version
ation Purposes Only
4,1593
447217
21.9
x
ft Ps0%
LOW4
tight x
9rk.4
OAM
Vg. X
- PD.
141.53
90.0%
17.79
0*12
1%
7 1. Fr.
MJAiJ
j Jil
.41- jM01
Figure 6-5: Screen capture of the DP Simulation Model "Input" tab showing the @Risk "Define
Distribution" dialog box that is used to assign a lead time probability distribution for each
production case
For Product X the lead time distribution for the No NC case followed a log normal
distribution with a mean of eight days and a standard deviation of six days. Figure 6-6 shows a
plot of this distribution.
No NC
2.13
5.0%
-
5.0%
I
0Logmmn(SA)
@RISK Trial Vers ion
LEvaluation Purpos s Only
Mkwnuar
000
+W2
0163nwns
Mean
Sid Dev
&ODD
6.000
-
D.02
in
0
03
In
n
r4
U1
r4
C1
Figure 6-6: Product X lead time distribution in days for the "No NC" case (batches that do not
receive and NC during DP production)
45
For Product X the lead time distribution for the Class 1 NC case followed a log normal
distribution with a mean of 15.1 days and a standard deviation of 9.3 days. Figure 6-7 shows
plot of this distribution fit against historical data for a similar product.
Fit Comparison for Class 1
RiskLognorm(9.4103,9.3315,RIskShft(5.7045))
31.0
7.0
5.0%
ZIIZ.lZIIZIZZIZZ
.4
a
1
2.4%
0.10
Minimum
7.000
MaxImum 63.000
0.08.
Mean
Std Dev
Values
@RI! K Trial Version
For Evalk ation Purposes Only
0.06
14.982
8.433
110
-Lognonn
Minmum 5.705
+Ws
Maximum
Mean
15.115
Std Dev
9.332
0.04
0.02
0
o
o
0)
'4-
0
In
01
ID
0
1'~.
Figure 6-7: Product X lead time distribution in days for the "Class 1 NC" case (batches receive a
Class 1 NC during DP production) fit against historical data for a similar product
For Product X the lead time distribution for the Class 2 NC case followed a log normal
distribution with a mean of 66.1 days and a standard deviation of 54.95 days. Figure 6-8 shows a
plot of this distribution fit against historical data for a similar product.
46
Fit Comparison for Class 2
RiskLognorm(53.544,54.947,RiskShlt(12.591))
200.0
23.0
5.0%
2.9%+
5.o%
6.6%
0.025
0
0.020-
Input
Minimum
0.015-
15.00
Maximum 398.00
66.42
Mean
52.77
Std Dev
336
Values
@RISK Trial Version
For Evaluation Purposes Only
SLognm
0.010-
Minimum
Maximum
Mean
Std Dev
0.005
0.000
12.59
+e
66.14
54.95
C
03
Figure 6-8: Product X lead time distribution in days for the "Class 2 NC" case (batches that
receive a Class 2 NC during DP production) fit against historical data for a similar product
For Product X the lead time distribution for the Class 3 NC case followed a triangular
distribution with a minimum of 42 and a maximum of 430 days. Figure 6-9 shows a plot of this
distribution fit against historical data for a similar product.
Fit Comparison for Class 3
RiskTrtang(42,42,430.36)
347.0
42.0
E5.0%
0.0%
0.006-
0.005-
flinput
0.004-
Minimum 42.00
Maximum 392.00
Mean
Std Dev
values
0.003-
-
Triang
Minimum
42.00
Madimum 430.36
171.45
Mean
91.54
Std Dev
0.002-
0.001 4
0.000 L0
173.79
97.06
29
a
08
Figure 6-9: Product X lead time distribution in days for the "Class 3 NC" case (batches that
receive a Class 3 NC during DP production) fit against historical data for a similar product
47
Finally, forecast or historical demand data is taken from RapidRespone, the supply
planning system, and entered into the demand fields on the "Inputs" tab as shown in Figure 6-4.
This data is available in RapidResponse as the aggregated demand at the DCs for a given IDP
SKU. These values are converted into a uniform distribution that the model uses to simulate
demand. For Product X, the forecast aggregated DC demand for a similar product was used.
6.1.2 "Simulation" tab
With the model inputs defined, the user then moves to the "Simulation" tab to run the
model. A screen capture of the "Simulation" tab is shown in Figure 6-6, all fields shown in
yellow are inputs. The Batch Size field sets the reorder quantity for the continuous review
inventory policy that underlies the model. In this case, the reorder quantity is determined by the
batch size of the product being simulated. For the majority of AML's products, the batch size is
fixed. However, for a few products the batch size can change based on the size of the
formulation tanks that are used in production. The average batch size for most products is
around 60,000 units (syringes) with a maximum batch size of close to 250,000 units.
Multiperiod Inventory Simulation
Service Level Over 100 Months
Fill Rate Over 100 Months
Ave
Demand
Lead Time
Ave
Aveme inven" Position nib
Ave
In
Position MF
nit)
Aves On-Hand Inve
Average On-Hand Inventory (MFC)
255 DD
7
295,543
8
423,165
9
10
11
12
13
14
15
548,127
42%.468
%-7,,113
850
604,495
485A3
385.573
-
No
-
-
120802 25696
130038 383658
118859 264999
224456 295543
127378 168165
130038 38127
118659 429468
117355 312113
100263 211850
117355 94495
11859 230836
100263 385573
118659 266914
0
Yes
0
No
0
Yes
0
No
0
0
0
0
0
0
0
0
0
Yes
Yes
No
Yes
No
Yes
No
No
Yes
255,000
-
510,
-
-
230836
255 *
255 0DG
385573
-
255,000
-
255,000
-
2
0D
sio,000
4furM
949513,696
38519999
295.543
42165
548127
0.37
-
No NC
0.1
-
0.13
No NC
0.54
0.27
Cdass 2
No NC
1.6
0.1
2.5
1.2
429.468
-
0-71
255 000.
M0 00I
567,113
466850
604.495
-
Cass 2
-
0.17
No NC
485,836
-
521.914
0.50
255,000
-
25,0001
0.1
-
-
255,000
1-
Figure 6-10: Screen capture of DP Simulation Model "Simulation" tab
48
--
-
-
264999
295543
168165
38127
4294M8
312113
211850
94495
NO
0
479Trr
-
383658
519.999
a
100239 379498
-
383,6M
1073
-
-
-
5
6
-
-
513.696
3
4
80.00
479 3
379,498
-
I
50000
479737
379498
258896
1
2
-
-
Reorde Point
Batch Size
Class 1
0.4
The Reorder Point field sets the reorder point for the continuous review inventory policy
that underlies the model. Once the Batch Size and Reorder Point have been populated the user
presses the "F9" key to run the simulation. The user enters a Reorder Point (number of syringes)
to target a specific service level and uses trial and error to determine the Reorder Point that
results in the desired service level or fill rate.
In the simulation, a new batch is ordered whenever the inventory position drops below
the Reorder Point. The inventory position at any point in time is the actual inventory in the
warehouse (on-hand inventory) plus any batches that are in production (pipeline inventory). For
each batch that is ordered, the program randomly assigns a lead time based on the probability of
receiving and NC and corresponding lead time distribution in the "Inputs" tab. This cycle is
repeated for 100 periods with each period receiving demand from a uniform distribution based
on the demand entered on the "Inputs" tab.
A stock out occurs when there is insufficient on-hand inventory to cover the demand for
the period. The model uses the following equation to calculate the service level:
Service Level
=
(Total#Orders- Total#Stockouts)
Total#Orders
If a stockout occurs, the model assumes that a partial order is shipped and the remaining
number of units are placed on backorder. The units on backorder are fulfilled as soon as new
product arrives in on-hand inventory. This is representative of the actual relationship between
AML and the Amgen DCs where AML operates on a first in first out (FIFO) system and will
partially fulfill orders if there is not sufficient supply.
Fill rate is another measure of supply chain performance. The model uses the equation
below to calculate fill rate:
-
(Total#unitsDemanded - Total#unitsOnBackorder)
Total#unitsDemanded
The other outputs that are displayed are Average Demand, Average Lead Time, Average
Inventory Position, and Average On-Hand inventory. The averages are taken from the 100
simulation periods calculated by the model. With these outputs, the user can determine the
49
1IN07
amount of inventory that will achieve the desired performance (service level or fill rate) given
the model inputs. The user will target a specific service level or fill rate and will set the Reorder
Point (number of syringes) using trial and error to achieve the targets.
Using the Simulation feature in @Risk, the user can rapidly run and document several
iterations of the simulation. The outputs of the simulation model such as service level and fill
rate are recorded and can be plotted to gain an understanding about the likelihood of possible
outcomes. For example, Figure 6-11 shows the distribution of service level for Product X over
100 iterations using the settings detailed above. For this case, the service level has a mean of
94.5% and a standard deviation of 2.9%.
Service Level Over 100 Months
98.00%
89.00%
15.0%
5.0%
0.18
stow=
777::777
AM
390%
95.W%
2Dv
.926%
0.16
3.M-
0.14
89.00%
0.12
9.00%
3P
0.10
@RISK Trial Version
I5.000%
or EvaluationiPurposes On
0.08
95.0%
M1000%
90.000%
92000%
92.000%
%
25%
0.06
35%
95.000%
0.04
V%
9.000%
0.02
60
940%
0.00
I
II
II
~75%
8%
90
96.000%
97.%
W009%
Figure 6-11: Output of DP Simulation model showing the service level of Product X assuming the
inputs described is Section 6
6.2 Modeling results
The Simulation Model was run for all commercial syringe SKUs currently being
produced at AML to determine the amount of IDP inventory required to execute the
postponement strategy. Additionally, the simulation model was used to determine how changes
in the probability of receiving an NC or the NC resolution time impact the service level or
50
inventory required for the postponement system. Finally, results from the model were used to
"socialize" the postponement concept with stakeholders at AML by demonstrating how the
system would perform if implemented.
6.2.1
Postponement inventory levels
A key to assessing the feasibility of implementing postponement in the DP supply chain
was to determine the amount of inventory that is necessary to execute the strategy. To determine
the total amount of inventory required for postponement, the simulation model was run for all
commercial syringe IDP SKUs currently being produced at AML. The amount of inventory
required for each IDP SKU depends on the lead time and demand distributions for that specific
product as well as the target service level. Using the simulation model, it was determined that on
average around two months forward coverage (MFCs) of demand are required for the DP
postponement strategy.
6.2.2 Impact of NCs
The simulation model was used to determine the impact of NCs on service level or
conversely the amount of inventory required to maintain a target service level. Specifically, the
model was run to compare the how the system would perform with the 2013 NC rates and
closure times compared to the 2014 NC rates and closure times that were negatively impacted by
the syringe particle challenges. The major difference was in the number of Class II NCs which
went from 41% of batches in 2013 to 62% of batches in 2014. Using Product X as an example,
the simulation results from both scenarios are discussed below.
The model was initially run using 2013 NC rates and NC closure time distributions. A
95% service level was targeted. The model determined that a reorder point of 460,000 units was
required in order to meet the 95% service level target. Figure 6-12 shows the service level that
was calculated after 500 iterations.
51
Service Level Over 100 Months
98.00%
89.00%
-
-.-...- -...
.-.--.--F-....
---- --------------....----- - -----__ ---_
_ _-
-
0.14
-
5.0%
0.16
0.12
Service Level Over 100
Months
0.10
IV r o
@IUSK T
0.08
E aluation
Fo
P p is
Minimum
Maximum
-ni
Mean
84.000%
100.000%
93.882%
Std Dev
0.06
2.811%
Values
500
rr.J
0.04
-f
0.02
0.00
r4J
0o
Go
0D
ON4
Co
Go
'.0
'e-
r'1
o~
CD
0D
00
0'
-4
Figure 6-12: Simulation model output of service level for Product
X
u ing 2013 NC rates and NC
closure time distributions and a reorder point of 460,000 units
Next the model was run using 2014 NC rates and NC closure time distributions (the demand
distribution, reorder point and batch size were not changed). With the reorder point still set at
460,000 units the service level dropped to 87%. Figure 6-13 shows the service level that was
calculated after 500 iterations.
Service Level Over 100 Months
93.00%
80.00%
[
0.12
5.0%
*S5.0%
0.10
~
-
0.08
Months
@_RISK 9a
0.06
4ri
__
For Evaluation P
-
.
0.04
Service Level Over 100
Minimum
Maximum
Mean
Std Dev
Values
_
ry
---.
-
74.000%
97.000%
87.532%
4.007%
500
0.02
0.00
0
1~.
U,
N-
0
in
00
ONC7
o
n
V-4
Figure 6-13: Simulation model output of service level for Product X using 2014 NC rates and NC
closure time distributions and a reorder point of 460,000 units
52
-
Using the 2014 NC rates requires a significant increase in inventory levels in order to
obtain a 95% service level. Simulation modeling determined that a reorder point of 550,000
units would be required to maintain a 95% service level with the 2014 NC rates. Figure 6-14
shows the service level that was calculated after 500 iterations.
Service Level Over 100 Months
99.00%
90.00%
0.18
0.16
0.14
[_
5.00/0
50
----- -----I--
......
Service Level Over 100
0.12
Months
@RISK Trial E sinn
0.10
0 y
For .a uat On p p
0.08
a
0.06
Minimum
Maximum
Mean
Std Dev
Values
85.000%
100.000%
94.552%
2.704%
500
0.04
0.02
--.
n nn
O
80-
%D~
0 "C)
0
to 00 0 0
r.1
Ale'3
-0 _00
Figure 6-14: Simulation model output of service level for Product X using 2014 NC rates and NC
closure time distributions and a reorder point of 550,000 units
In summary, higher NC rates result in lower service levels for a given inventory level or higher
inventory levels to maintain a target service level. In the example described above, increasing
the Class II NC rate from 41% to 62% and Class III NCs from 2% to 9% resulted in an eight
percent drop in service level from AML to the DCs (assuming a postponement system with a
continuous review inventory strategy). However, for the same system to maintain a 95% service
level in the face of higher NC rates requires a 20% increase in inventory. In the Product X
example this is a difference of 90,000 units. If the inventory holding costs were known, it would
be possible to translate these results into a proxy for the cost of NCs.
6.2.3 System performance
The simulation model also generates plots that show how the system is expected to
perform over time. Figure 6-15 shows a plot that was generated by the DP Simulation Model.
53
The plot indicates the anticipated reorder frequency and inventory levels for a given SKU. This
information was helpful in demonstrating to the supply chain, manufacturing, and quality teams
how the postponement system would function. Specifically for planning and manufacturing, the
model helped determine how often and when each SKU would be produced. Aggregating the
various SKUs, the model could provide and expected order frequency per IDP SKU, which could
be used to assist in production planning.
IDP Inventory Simulation
-4-On-Hand Inventory (end of month)
-C-Demand
-fr-inventory Position
-4-Reorder Point
30000
..........
..........
.......
..... .............
E 15000
100
5000
r-
---
--
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Months
Figure 6-15: DP Simulation Model system performance plot for hypothetical SKU
7
Conclusions
For certain products, the implementation of postponement in Amgen's syringe DP supply
chain will result in significant benefits. The majority of IDP SKUs in the syringe DP supply
chain meet the criteria outlined by Cachon and Terwiesch for when postponement is an ideal
strategy [6].
54
1) Customers demand many versions - variety is important
a. Amgen supplies the same high quality medicines to patients all around the
world. However, the labeling and packaging that accompany these medicines
is customized according to each country's language and regulatory
requirements. As such, although Amgen only produces a dozen medicines it
provides global markets with hundreds of finished goods SKUs.
2) There is less uncertainty with respect to total demand than there is for individual
versions
a. The majority of demand for Amgen's medicines comes from the U.S.,
Canada, and Europe. These large markets have stable demand that can be
forecast several months out to within a few percent of actual demand.
However, Amgen also serves emerging markets that have greater demand
variability and much lower forecast accuracy. While the demand from these
markets is difficult to predict, the overall demand for a given medicine is
much less uncertain.
3) Variety is created late in the production process
a. At the packaging stage of syringe DP production, 30 unique dosage forms of
IDP are converted into hundreds of finished goods SKUs destined for patients
throughout the world.
4) Variety can be added quickly and cheaply
a. At the packaging step, variety comes in the form of labels, inserts, and boxes
that are printed in the specific language and according to the regulatory body
requirements of the destination market.
5) The components needed to create variety are inexpensive relative to the generic
component
a. Relative to the cost of producing the actual medicines, printing the labels,
inserts, and boxes is relatively inexpensive.
However, not all of Amgen's syringe products meet these criteria. There are some IDP
SKUs that only serve one market. This means that only one finished drug product or FDP is
derived from the IDP SKU. For example in Figure 7-1, IDPXYZA supports four FDP SKUs
while IDPXYZB is only converted into FDPABCE. Because the packaging process does
55
not add any variety, postponement should not be implemented for IDPXYZB or any other IDP
SKU that only serves one FDP SKU.
FDPABCA
DAB
IDPXYZA
B
FDPABCC1
FDPABCD
IMPXYZBJ
FDPABCEl
Figure 7-1: Example of generic IDP and the corresponding FDP variants
The majority of Amgen's syringe IDP SKUs support several FDP SKUs and therefore
would benefit from postponement.
7.1 Impact on inventory and service level
For applicable IDP SKUs, postponement will improve the service level from less than
75% to around 90%. This improvement is not dependent on holding dramatically larger amounts
of inventory but by more effectively utilizing the high levels of WIP that are already in the
system. Also, this assumes the high NC rates continue to result in production lead times similar
to those discussed in section 4.
The Simulation Model was run for all applicable syringe IDP SKUs targeting a 90%
service level, which is the current goal for AML. The average amounts of on-hand inventory and
pipe-line inventory were tabulated and summed. On average, a total IDP inventory position of
8.9 million units are required for the postponement strategy. Although this is a large amount of
56
inventory, it is only 2% greater than the average amount of syringe WIP inventory held at AML
in 2014.
As shown in Figure 7-2, 34% of the current WIP inventory is held before Inspection.
However, holding inventory in this position is of little value because it does not help to buffer
against the variability caused by NCs in Inspection. To buffer against NCs, all batches should
move through Formulation, Filling and Inspection as rapidly as possible so that NCs can be
identified immediately and the resolution process can commence.
Current Drug Product Manufacturing
DP
3.0 M units
34%
qp
T
5.7 M units
66%
Figure 7-2: Current WIP inventory levels in DP manufacturing
By shifting all of the current WIP inventory after Inspection, the unrestricted IDP
inventory (the green triangle in Figure 7-3) buffers the downstream system from variability
caused by NCs. Figure 7-3, shows a hypothetical average production lead time from
Formulation to unrestricted IDP inventory.
Pack
CT=13hr
Reso utlon
Figure 7-3: Hypothetical DP production lead times
57
Relea e
CT=2wks
Ship
With the postponement system, a reduction in lead time variability would result in higher
service levels or reduced postponement inventory levels. By decreasing the likelihood of
generating Class II NCs as well as the Class II NC resolution times, AML could achieve a
reduction in lead time variability, which would result in improved service levels or reduced
postponement inventory levels. For example if the NC rate and closure times returned to the
averages measured for the first two quarters of 2013, then the service level from AML to the
DCs would improve to around 98%. However if Amgen is satisfied with a 90% service level to
the DCs, then the amount of postponement inventory could be reduced. Results from the
Simulation Model show that with 2013 NC rates and NC closure times, postponement inventory
could be reduced by more than 10% or around one million units.
7.2 Impact on lead time
The postponement system could reduce the lead time from AML to the DCs from over
three months to around three weeks. The current lead time from AML to the Amgen DCs is over
three months. All Amgen distribution centers, except for those serving the U.S. market, operate
on a make to order (MTO) system, where they place orders with AML by submitting a stock
transfer order (STOs). The agreed upon lead time between AML and the DCs is current plus
three months.
In the postponement system, receiving an STO would initiate the pack, release, and
shipping processes. Upon receiving an STO, the required quantity of IDP syringes would be
taken from released IDP inventory to the packaging lines along with the corresponding labels,
inserts, and boxes. After packing, the lot would proceed through the disposition release process,
which is a final check that all of the testing and procedures for a given batch did not have any
deviations which would require investigation. The packaged and released product is then
shipped to the DCs. This entire process is expected to take less than three weeks - dramatically
reducing the lead time to the DCs.
58
8
Recommendations
This section lays out recommendations for AML and other supply chains with high lead
time variability through production.
8.1 Track flow time through production and adjust planning process
accordingly
The increase in NC rate that began in the fourth quarter of 2013 has resulted in a below
target service level from AML to the DCs. A contributing factor to the lower service level
throughout 2014, was not updating the supply planning systems to reflect the longer production
lead times. AML's planning systems schedule production based on the date that the product is
needed by the customer (Amgen DCs and third parties). Given a required delivery date, the
system back calculates when to initiate the Formulation and Filling processes using predefined
goods release (GR) times. For example, if a product is scheduled for delivery on May 15 and it
has a GR time from Filling of 45 days then the system will schedule the batch to be filled on
April
1 st.
The GR times are adjusted infrequently and therefore the planning process does not
necessarily reflect the actual production environment. When the flow time through production
increases and the GR times are not adjusted, the result is missed orders and below target service
level. For example, the syringe particle NC issues led to an increase in average lead time
through production of more than 15 days. However, the GR times were not extended by 15 days
to reflect this change. The result has been, below target service level to the DCs.
Tracking the flow time through production and updating the GR times accordingly could
improve the service level to the DCs. Currently, AML does not measure and track the flow time
through production. As such, the GR times are not frequently updated. For the proposed
postponement system, an Excel workbook could be created that would pull data from SAP in
order to determine the flow time from Formulation to unrestricted IDP inventory. With this
system, the postponement inventory levels and GR times could be updated quarterly to reflect
the current operating environment.
59
8.2 Reduce safety stock levels at the DCs when postponement system is
stable
The implementation of postponement at AML could enable a reduction in safety stock at
the DCs. As discussed in section 3.1, postponement is often employed to reduce global
inventory levels in a supply chain. In the soluble coffee case reviewed in section 3.2, the authors
calculated a potential aggregated safety stock reduction of 46.1% through the use of
postponement. For Amgen, this strategy is expected to have a similar impact.
Amgen calculates safety stock levels at the DCs using the equation shown below:
SafetyStock = z * StDevDemand *
LeadTime
In this equation, demand is assumed to be normally distributed and StDevDemand is the
standard deviation of demand. LeadTime is the time from order placement to delivery.
Currently, this is around three months. As the lead time decreases so does the safety stock.
Figure 8-1, which is a normalized plot of lead time versus service level, shows how the reduction
in lead time impacts safety stock. In the case of postponement at Amgen, reducing the lead time
from AML to the DCs from three months to less than one month could allow for a 42% reduction
in operational safety stock (OSS) at the DCs.
60
Normalized lead time vs Safety Stock
1,20
1.00
0180
0.40
0.20
0.20
0.40
0.60
0.80
1.00
1.20
Leadtine
Figure 8-1: Normalized plot of the impact of lead time on safety stock
However, Amgen should proceed cautiously when drawing down safety stock levels at
the DCs. The safety stock levels should not be reduced until postponement has been fully
implemented and proven stable.
8.3 Focus improvement efforts on reducing production lead time
variability
For inventory reduction or service level improvement, two areas that managers focus on
are the reduction of the lead time and the variability of this lead time [13]. For AML, the long
right tail of the lead time distribution is difficult to plan for and manage. To improve the service
level from AML to the DCs over the long term and make the supply chain more robust, AML
should target reducing the variability in production flow time.
61
To illustrate this point, the postponement inventory levels were calculated for the case
where the lead time through production was constant at 60 days. In this case, the average lead
time of 60 days is significantly greater than the 2013 or 2014 average lead times used in section
7.1. However, because the variability in lead time is removed the resulting postponement
inventory levels are lower. Using the constant 60 day lead time results in a 40% reduction in
postponement inventory compared to the amount required when using the 2014 lead time and an
almost 30% reduction compared to 2013.
In order to address the heavy right tail of the current lead time distribution, AML should
focus on improving the Class II NC resolution process. As shown in Figure 8-2, a significant
portion of Class II NCs take longer than 60 days to resolve leading to the long right tail of the
lead time distribution. It is recommended that a follow on project be conducted to determine
why some Class II NCs close in a few weeks while others take months. The goal of this project
would be to modify the procedures, personnel and equipment involved in NC resolution so that
all Class II NCs are resolved in 60 days or less. Even if these improvements result in an increase
in the median Class II NC resolution time, the Amgen supply chain will still see a benefit as long
as the variability is reduced.
Histogram of Class 2 NC Age 2013, Class 2 NC Age 2014
0
Gass 2 NC Age 2013
7ass
60 120 180 240 300 360
2NC Age 2D14
606050
so
404
40
-
30-
330
10-
10
0
60
120 180 240 300 360
Figure 8-2: Class II NC resolution histograms for 2013 and 2014
62
9
Glossary
AML
Amgen Manufacturing Limited is Amgen's Puerto Rico manufacturing facility.
DP
Drug Product, or the stage in the manufacturing process that represents Drug
Substance (DS) filled into a presentation, or container such as a vial or prefilled syringe.
DS
Drug Substance, or the stage in the manufacturing process that represents a
scaled up, purified, and filtered drug held in a cryovessel or carboy.
ERP
Enterprise Resource Planning, a software system that integrates many
sources of enterprise information such as finance / accounting, manufacturing,
and sales data intended to aid management in cross-organizational planning
and decision-making.
FDA
Food and Drug Administration, the agency of the United States Department
of Health and Human Services, which regulates the biopharmaceutical industry.
FDP
Final Drug Product, the stage in the manufacturing process that represents a
filled, inspected, labeled, packaged and ready-to-ship drug intended for patient
use.
IDP
Inspected Drug Product, the stage in the manufacturing process that
represents an inspected Drug Product (DP).
LDP
Labeled Drug Product, the stage in the manufacturing process that represents
an inspected and labeled Drug Product (DP).
MFC
Months Forward Coverage, the number of months a certain level of inventory
can cover average monthly demand.
NC
Non-Conformance, or a deviation from defined procedures during DP
manufacturing and testing. The product cannot be shipped from the
manufacturing site to the distribution centers or third parties until the NC has
been resolved. NCs fall into three classes I, II, and Ill with Class I being the
least severe and Class IlIl being the most severe.
ROP
Re-Order Point, a predetermined specified level of inventory that, when
reached, triggers production
63
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