How a Postponement Strategy can Reduce Cost and Lead Time

How a Postponement Strategy can Reduce Cost and Lead Time for

Pharma Supply Chains

by

Rebecca Nolan

and

Lukasz Ploszczuk

SUBMITTED TO THE PROGRAM IN SUPPLY CHAIN MANAGEMENT IN PARTIAL

FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED

SCIENCE IN SUPPLY CHAIN MANAGEMENT

AT THE

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

June 2021

© 2021 Rebecca Nolan and Lukasz Ploszcuk. All rights reserved.

The authors hereby grant to MIT permission to reproduce and to distribute publicly paper and

electronic copies of this capstone document in whole or in part in any medium now known or

hereafter created.

Signature of Author: ____________________________________________________________

Department of Supply Chain Management

May 14, 2021

Signature of Author: ____________________________________________________________

Department of Supply Chain Management

May 14, 2021

Certified By: ___________________________________________________________________

Dr. Matthias Winkenbach

Director, MIT Megacity Logistics Lab and MIT CAVE Lab

Capstone Advisor

Accepted by: __________________________________________________________________

Prof. Yossi Sheffi

Director, Center for Transportation and Logistics

Elisha Gray II Professor of Engineering Systems

Professor, Civil and Environmental Engineering

How a Postponement Strategy Can Reduce Cost and Lead Time for

Pharma Supply Chains by

Rebecca Nolan

and

Lukasz Ploszczuk

Submitted to the Program in Supply Chain Management

on May 8, 2021 in Partial Fulfillment of the

Requirements for the Degree of Master of Applied Science in Supply Chain

Management

Abstract

Nowadays end customers and shareholders are setting high expectations, putting pressure on

companies to do their best not only to meet their needs but also deliver shareholder value at the

same time. In order to be both competitive and customer-centric, firms are increasingly focused

on their supply chains as one of the areas for future improvements. Some industries (e.g., rare

medicines) are even more complex, as demand is stable over time, so the only way to increase

profitability is to constantly focus on cost reduction. In order to be more competitive and meet

customer demand, our sponsoring company must ensure that its supply chain is agile enough to

provide its customers with life-saving medicines quickly and without delays. These might be

achieved by switching to late product customization at a third party-logistics-provider's location.

The goal of the model is to provide the proper tools and information necessary for our

sponsoring company to use when evaluating whether to put in place a postponement strategy.

Thus, in order to capture the entire end-to-end changes in the supply chain, we broke it out into

three separate models, including a materials model, “naked vials” (unlabeled) model, and

finished goods model. This allowed us to process the information in separate parts and capture

all costs at the level they were acquired. A comparison of the base scenario to different

customization scenarios is also included in order to understand potential cost benefits. Based on

the sensitivity analysis, it makes sense for the company to implement a customization strategy

for almost all analyzed scenarios, as in 84% of cases the company can deliver cost savings.

Overall, this model reduced the information gap within the sponsoring company by providing

them with the proper tools needed not only to evaluate their current inventory strategy but also as

a tool to use when negotiating with their third-party providers.

Capstone Advisor: Matthias Winkenbach

Title: Director, MIT Megacity Logistics Lab and MIT CAVE Lab

Table of Contents

List of Figures ................................................................................................................................. 5

List of Tables .................................................................................................................................. 6

1. Introduction ............................................................................................................................. 7

2. Literature Review.................................................................................................................... 9

2.1. Supply Chain Network Design ......................................................................................... 9

2.2. Production Postponement Strategies .............................................................................. 10

2.3. Inventory Theory ............................................................................................................ 11

2.3.1. Total Cost Equation ................................................................................................ 12

2.3.2. Safety Stock Theory ................................................................................................ 12

2.4. Performance Trade-offs and Supply Chain Configuration Problems ............................ 14

3. Data ....................................................................................................................................... 18

4. Methodology ......................................................................................................................... 21

4.1. Model Inputs and Assumptions ...................................................................................... 21

4.2. Models ............................................................................................................................ 23

4.2.1. Materials Model ...................................................................................................... 23

4.2.2. Naked Vials Model ................................................................................................. 24

4.2.3. Finished Goods Model ............................................................................................ 25

4.3. Model Formulation ......................................................................................................... 26

5. Analysis................................................................................................................................. 27

5.1. Product Selection............................................................................................................ 27

5.2. Total Cost and Breakeven .............................................................................................. 28

5.3. Sensitivity Analysis ........................................................................................................ 30

5.3.1. Demand and Volatility Sensitivity Analysis ........................................................... 31

5.3.2. Leadtime Sensitivity Analysis ................................................................................ 32

5.3.3. Service Level Sensitivity Analysis ......................................................................... 33

5.4. Monte Carlo Simulation ................................................................................................. 34

6. Conclusion ............................................................................................................................ 35

6.1. Insights and Management Recommendations ................................................................ 35

6.2. Future Research .............................................................................................................. 36

Appendix ....................................................................................................................................... 40

List of Figures

Figure 2-1 Efficiency – Responsiveness Trade-Off Curve ........................................................... 15

Figure 3-1 Aggregated Quantity and Orders Frequency Based on Future Forecast ..................... 20

Figure 3-2 Aggregated Quantity and Orders Frequency Based on Historical Orders .................. 20

Figure 5-1 Breakeven Curve for Product C .................................................................................. 30

Figure 5-2 Lead Time Sensitivity Analysis .................................................................................. 33

Figure 5-3 Service Level Sensitivity Analysis.............................................................................. 34

Figure 5-4 Simulation Histogram ................................................................................................. 35

List of Tables

Table 4-1 Model Assumptions ...................................................................................................... 22

Table 4-2 Materials Model Cost Comparison ............................................................................... 23

Table 5-1 Summary Statistics of Sponsoring Company Products ................................................ 28

Table 5-2 Total Cost Summary for Product C .............................................................................. 29

Table 5-3 Demand and Volatility Sensitivity Analysis ................................................................ 32

Table 6-1 Detailed Table of Model Inputs .................................................................................... 40

1. Introduction

In highly competitive markets where end customers are setting high expectations,

companies need to do their best not only to meet their needs but at the same time to still deliver

shareholder value. In order to be competitive and customer-centric, firms increasingly focus on

their supply chains as one of the areas for future improvements. Some industries (e.g., rare

medicines) are even more complex, as demand is stable over time, so the only way to increase

profitability is constant focus on cost reduction. Our sponsoring company is a pharmaceutical

company engaged in developing innovative therapeutics to patients with serious and life-

threatening rare genetic diseases. The company constantly focuses on efficiency as time is critical

to their patients all over the world.

In order to be more competitive and meet customer demand, our sponsoring company must

ensure that its supply chain is agile enough to provide its customers with life-saving medicines

quickly and without delays. This is an important distinction from other types of pharma companies,

as there are risks for patients if a life-saving medicine is not delivered on time. Accordingly, our

sponsoring company would like to explore options for reducing customer lead time and optimizing

inventory. This will in turn lead to both cost and service benefits. Our sponsoring company

believes it is important to modify its current supply chain for these rare medicines to minimize

lead times while optimizing inventory in order to achieve the highest possible level of customer

satisfaction. Supply chains currently offer a wide variety of models for producing and storing

inventory. Those options often come with different trade-offs. In the pharma industry, there are

more trade-off considerations, as products typically have long lead times and relatively higher

inventory levels compared to other industries. In order to be competitive, pharma companies must

be agile, as demand for life-saving medicines for rare diseases needs to be fulfilled almost

immediately. Current agility is built on high inventory levels for finished goods, which from the

company’s perspective might not be the most cost-effective solution. This capstone will focus on

exploring different inventory replenishment strategies and policies in order to identify the most

cost-effective solution.

The objective of this capstone project is to explore supply chain design options, evaluate

the corresponding cost and performance trade-offs among these alternative designs, and offer a

recommendation on the desirable future design of our sponsoring company’s rare medicine supply

chain.

Our steps to solve this problem include following:

1. Run mapping and matching exercise to fully understand current supply chain design,

processes, and existing company and industry constraints

2. Explore different supply chain redesign options which might include:

• Moving distribution to different facilities closer to end customers

• Make-to-order strategy with fast shipping option i.e. airfreight

• Redesigned packaging operations with late-stage product differentiation

3. Calculate tradeoffs between possible lead time reduction and inventory gain for each of the

scenarios

4. Frame final recommendation

2. Literature Review

This literature review will generate insights about the different options to produce and store

products in the pharma industry, targeted towards lifesaving medicines for rare diseases. It will

start off by looking at general supply chain and network design options for the pharma industry,

introducing the concept of postponement as a key opportunity for companies to engage in to be

more agile. Next, basic inventory theory and total cost equation will be covered. After that,

performance trade-offs and general approach to supply chain configuration problems will be

addressed to understand the different options which will be later used to model and evaluate the

scenarios. We will discuss all these areas by analyzing the concept and the different studies

completed. We will take a holistic look at all of these options and tie them to the project’s overall

goal.

2.1. Supply Chain Network Design

Companies operating in the pharma industry face increased complexity when deciding how

to design their supply chain. This is because when a product is a lifesaving medicines, demand not

only needs to be met almost 100% of the time but inventory must also be readily available when

the product is requested. This results in challenges for pharma supply chains to achieve an

economical benefit, as it is difficult for these companies to progress and gain a competitive

improvement of supply chain management (Srimarut et al., 2020). Thus, it is crucial for pharma

companies to ensure they have an optimal network design that allows them to be agile and provide

the lifesaving medicine as soon as it is needed while trying to minimize costs in order to gain a

competitive advantage. These decisions often include where the location of the distribution centers

will be. In addition, smaller companies cannot afford owning distribution centers and often must

engage with third-party-logistics-providers (3PL), which adds additional layers of complexity in

their network. For a rare-medicine supply chain, there are additional factors to be taken into

consideration for a network design of the distribution centers as some of these decisions are

qualitative factors and require discussions with key stakeholders. For example, this may include

certain country or government requirements that specifies distance and location of production in

relation to the final customer. One study concluded that some type of value criteria should be

applied to “obtain an equitable vaccine distribution or to measure the humanitarian impact of a

vaccine supply chain” (Lemmens et al., 2016). This additional criterion considers the qualitative

factors that models currently do not have. As it is very difficult to make quick, small changes in a

network design, companies are still trying to explore different options to increase agility. One of

these factors that allows for a more robust and agile supply chain is postponement.

2.2. Production Postponement Strategies

In addition to deciding where to locate the distribution centers, another key factor is

deciding when to make the finished product and whether to store the finished goods or store a

work-in-progress inventory. In the case of our capstone, a key objective is to determine if the

company should follow a make-to-stock, make-to-order, or hybrid replenishment strategy as well

as identify postponement activities such as deciding at what point should the country label be put

on the vials. “Postponement is a supply chain strategy that enables a supply chain to achieve both

low cost and fast response by combining some common processes and delaying other product

differentiation process such as packaging and labeling” (Cheng, 2010). Thus, the goal is to reduce

inventory, lead time and achieve potential improvements to the service level. There are a variety

of ways to achieve these goals through postponement, including the following: track the flow time,

adjust the planning processes, and reduce safety stock levels of finished goods at the distribution

centers when the postponement system is stable (Pazdanoff, 2006). In the case of our sponsoring

company, postponement might be an optimal strategy for when they need to supply country-

specific versions of the products and for when there is less certainty with respect to total demand

than there is for individual versions. By moving forward with a postponement strategy, as

previously mentioned, it can increase the agility of a supply chain while significantly reducing the

inventory levels.

2.3. Inventory Theory

Once a company finalizes their supply chain network design, they face another challenging

topic of developing both an inventory management process and replenishment policies. Those are

critical to firm’s performance as they not only impact the operational part of the company but also

have influence on the company’s financial performance. Inventory managers usually need to

answer questions like: which form of inventory should they keep, where should they store that

inventory, and what would be sufficient safety stock level and reordering policy in order to meet

specific service objectives? Answers to all those questions are closely related to the total cost

equation, which ties factors such as unit value cost, inventory holding cost, ordering cost,

replenishment lead time, demand pattern, and cost of stocking out. In theory, companies should

design their optimal inventory replenishment policies by minimizing total relevant costs (Silver et

al., 2017).

2.3.1. Total Cost Equation

Companies continue to determine the optimal way to replenish their inventory in order to

minimize total costs while maintaining customer service level. The total relevant cost equation can

also be used to manipulate performance of certain lead-time reduction approaches. Inventory cost

has been seen to be increasing and is a function of both lead-time and order quantity. It was proven

in the International Journal of Production Economics that there is a linear relationship between

them (Li, 2020). This shows that key supply chain decisions such as changing the lead-time will

directly impact the total cost. Thus, it is important for supply chain managers to be responsive by

identifying the company’s strategy and objective in order to balance out the trade-offs. For

example, “excessive inventory incurred huge cost burden on the patient while the storage of

medical equipment, medicines and services can cause the health of the human being” (Srivastava,

2020). Thus, inventory decisions for a lifesaving medicine, such as in our capstone, do not just

have a cost impact, the decisions also have an impact to human lives. This is relevant to our

capstone project because there is a goal to reduce inventory levels while keeping lead-time low.

As we move through a solution, it is important to factor all these costs into account, so we don’t

negatively impact the overall total cost while only focusing on one of its components. However, it

is also important that when evaluating the cost trade-offs, the human aspect of the lifesaving

customer service be considered.

2.3.2. Safety Stock Theory

Nowadays, uncertainty has become an integral part of any business’s operations. The

literature describes many different sources of uncertainty affecting supply chains. However, one

of the biggest sources of uncertainty in any manufacturing company is demand volatility (Gupta

et al, 2003). To manage those disruptions, companies decide to keep more inventory on-hand as a

buffer. In many of these cases, there is no logic behind the safety stock calculation and firms use

an arbitrary (“rule of thumb”) approach. Inventory policies that are not optimized create different

supply chain risks. Insufficient levels of safety stock might lead to frequent stockout events, which

will impact customer satisfaction levels and, in the long run, will decrease revenue streams. On

the other hand, having too high inventory levels create obsolescence risks leading to additional

costs. A variety of different safety stock calculation methods can be found in different literature.

Jonsson et al. (2019) refers to the most common methods which are number-of-days and

target service level methods. The first approach ties the number of days with the average daily

demand, where number of days is an estimated parameter. The second method is a more

quantitative method and is a function of desired service level, demand and lead time uncertainty,

and reaction time. Regardless of the method used for the calculation, safety stock directly impacts

a company’s financial performance as it is part of the total relevant cost equation. Hernandez et al.

(2015) describes that commonality and make-to-order strategies are some of the ways to decrease

the total safety stock needed to support the desired company objective. Both of those strategies are

based on a risk pooling and are widely used in the industry to optimize safety stock levels. These

models assume that safety stock is stored as work-in-process inventory or raw materials and that

additional agility is built around final customization (Graves et al, 2003). Our analysis will

combine a target cycle-service method and a postponement production strategy to propose optimal

safety stock levels as well as quantify the impact on the total inventory levels. This will allow us

to focus on long-term value creation for the sponsoring company’s supply chain. The described

strategies might lead to specific trade-offs and decisions, which are discussed in the next part of

this literature review.

2.4. Performance Trade-offs and Supply Chain Configuration Problems

Modern supply chains are trying to be efficient and responsive at the same time. Efficiency

is highly focused on operations and asset productivity. The main goal of an efficient supply chain

is to maximize performance and minimize the cost at the same time (Changhee et al, 2019). Those

activities lead to increased bottom-line results and create competitive advantage. As customers’

expectations and level of uncertainty are growing over time, companies must find a way to satisfy

them by exploring different ways of increasing their order fulfillment responsiveness. “Firms

adopting a responsiveness-oriented supply chain aim to shorten lead time and/or improve customer

service level” (Nakano, 2020). Responsiveness is usually associated with higher operational cost,

which in the end, balances all the previous efficiency-oriented efforts. In order to monitor and

adjust supply chains, companies introduce indicators of operational performance. These are closely

related to a specific supply chain area. For example, logistics and customer service teams measure

timely performance and cycle service levels. On the other hand, inventory management teams

focus on asset productivity by looking at inventory turnover and obsolescence with a goal of

lowering those numbers over time. Different departments generate different improvement ideas,

which quite often negatively impact results of other teams. That is where the role of managers

starts and that is why their decisions should be data-driven. In majority of the cases those decisions

will require certain trade-offs between inventory, service level, cost and lead time (Nakano, 2020).

Figure 2-1 illustrates basic shape of trade-off curve between efficiency and responsiveness.

Figure 2-1 Efficiency – Responsiveness Trade-Off Curve

Depending on the nature of the business, companies might prefer to be at point A, B or at

any other point on the curve which will reflect their short/long term strategic goals. Ideally, every

company would like to be as close to the bottom left corner as possible (following direction of

green arrows). However, in order to achieve that state, companies need to invest more into assets,

processes, operations, etc. In the case of our sponsoring company, one of the options might be

translated into higher operational cost which will change their model from make-to-stock to make-

to-order for a certain number of markets and products combinations.

The literature presents several different approaches for solving supply chain configuration

problems. General methodology consists of eight major steps starting from data gathering and

conceptual modeling, moving into scenario pre-selection and quantitative analysis, and finishing

at implementation, and monitoring and evaluation of results (Chandra et al, 2016). This capstone

project will touch areas such as problem definition, data gathering, conceptual modeling and

quantitative analysis. In order to fully address a supply chain configuration problem, every analysis

should start with problem definition and objective formulation which must be aligned with overall

supply chain strategy. The next crucial step is data gathering. Data should be gathered from

multiple organization such as logistics, planning and customer service team. The quality of the

data is also important as it will have an impact on all future calculations. The next phase is

conceptual modeling which plays crucial role in solving supply chain configuration problems. It

not only forms a final objective but also gives a holistic overview. Different techniques (i.e., value-

stream mapping) are used to understand and explore potential opportunities within supply chain.

With that, process and concepts are re-designed and supply chain descriptive models are created.

Those models will be further used for evaluation. Conceptual modeling outlines potential gaps in

the data and clearly connects required data with specifics models (Chandra et al, 2016). With all

previous steps done, the project can finally move to a quantitative analysis phase. Each of the

previously formed models will most likely deliver different supply chain trade-offs and at this

point all of them need to be calculated and compared with project objectives and performance

metrics. The final analysis will yield a few different configuration scenarios, which will be ranked.

Based on the results, a final recommendation will be created.

This capstone was directed to provide a literature review on different topics and studies to

help solve the issue that our sponsoring company currently faces. The four key topics discussed

included: supply chain network design, postponement, inventory theory, and performance trade-

offs and supply chain configuration problems. All these together provide a holistic view of how to

solve our sponsoring company’s problem by providing insight into what has previously been done

and how theoretical terms can be applied to real life scenarios. All relevant materials to what is

currently in scope has been reviewed. Currently, there is a gap in the awareness and knowledge of

where the sponsoring company can save money. By using a tailored approach to produce and store

inventory, our capstone brings awareness how a postponement method can allow the sponsoring

company to be more flexible and reduce overall inventory.

3. Data

Due to the high confidentiality of information and non-disclosure agreement in place, our

capstone was fully based on anonymized data. Original information such as SKU, product and

destination market were replaced with set of letter and number combinations. The sponsoring

company provided corresponding anonymized information regarding past and future forecast, and

orders history. The provided forecast data contained information about past and future monthly

forecast by item, covering periods from March 2019 to December 2023. This was provided for

178 product-country combinations. The order data included 56,221 observations and

corresponding information about shipment date, product, ordering market, quantity shipped and

shipment location.

We started with data cleaning and data exploration by performing the following steps:

• Identified and deleted lines without any forecast or order information

• Checked all lines with missing forecast information – missing value instead of zero in

forecast dataset

• Identified and merged duplicated lines in forecast dataset – as per guidance from the

sponsoring company

• Identified and deleted missing values in the orders’ dataset

• Identified codes with forecast and no order history and excluded them from further analysis

• Identified codes without forecast and with order history and excluded them from further

analysis

• Consolidated forecast and order history information into one dataset

• Removed outliers (2 SKUs) with aggregated quantity > 1 million units

At this point we considered our dataset to be clean and ready for further analysis. In order

to understand the portfolio of the products, we plotted dependency of aggregated quantity (x-axis)

and order frequency (y-axis) for both forecast Figure 3-1 and shipment history Figure 3-2. The

different colors on both graphs represent different SKUs. After plotting the forecast data and

comparing it to the actuals we saw that the forecast did not mirror observed actuals as we would

have expected and proposed postponement scenarios for actuals as the forecasts would not provide

relevant results. This was addressed with our sponsoring company and as a result only historical

data was used for further modeling.

Based on the historical data, we clustered the company portfolio into two groups, high and

mid/low volume SKUs. Our capstone project addresses the second group of the codes. It aligns

with our literature review and best industry practices because late product differentiation becomes

too expensive for high volume SKUs as costs acquired by the company exceed potential benefits.

Figure 3-1 Aggregated Quantity and Orders Frequency Based on Future Forecast

Figure 3-2 Aggregated Quantity and Orders Frequency Based on Historical Orders

4. Methodology

In this chapter we will be discussing the methodology used to develop a model for having

a postponement strategy in the pharma industry. This model includes comparing the base scenario

to different customization scenarios to generate managerial insights. We will discuss the inputs

used in our model and assumptions made. After we will go over our development of the actual

models which included a materials model, “naked” vials (i.e., unlabeled) vials model, and finished

goods model. This end-to-end view of the process allowed us to capture all costs and processes

involved.

The model introduced was developed with the goal of providing the proper tools and

information necessary to our sponsoring company for when they evaluate whether to put in place

a postponement strategy. Thus, in order to capture the entire end-to-end changes in the supply

chain we broke it out into three separate models including a materials model, naked vials model,

and finished goods model. This allowed us to process the information in separate parts and capture

all costs at the level they were acquired. Within the three models we broke it out even further to

include a base scenario, using the current costs and processes, and potential customization

scenario(s) to reflect additional costs and process that come with having a postponement strategy.

In order to build the models, assumptions were made regarding some of the numbers and processes

as not all information was provided by the sponsoring company due to the nondisclosure

agreement.

4.1. Model Inputs and Assumptions

The input data used directly in the models contain a mixture of both data received from the

sponsoring company as well as assumptions made based on industry averages and discussions with

the sponsoring company. We split it into 3 levels including: uncertain data, data provided by the

sponsoring company, and assumed data based on conversations with the sponsoring company or

based on industry averages. Table 6-1 in Appendix presents full list of input parameters and source

of the data. In addition, all demand data (both historical and forecast) was provided by the

sponsoring company and was also used as input data. Prior to receiving the data, the sponsoring

company anonymized the data in order to ensure no proprietary information was going to be shown

to the public. This allowed us to fully utilize the demand datasets.

Assumptions were made during the creation of both the base and customization models in

order to use consistent numbers and knowledge of the processes across all models. The below

assumptions in Table 4-1 were based on discussion with the sponsoring company as well as

industry knowledge.

Table 4-1 Model Assumptions

Assumption General

1 30 days in a month

2 Demand reviewed on monthly basis

3 Regulatory approvals not considered in model

4 RMSE used to calculate demand deviation over lead time

5 Periodic review is used (not observed right away or actionable)

6 Holding cost = 10% of purchase cost

7 90 days safety stock for finished goods in current scenario

8 The current process for producing individual SKUs consists of both filling vials

and labeling/packaging

9 Finished goods models assumes individual SKU has same cost across product

line

10 Current scenario is used to calculate the model summary and simulation (vs.

optimal scenario

11 Optimal scenario is used to calculate sensitivity analysis

4.2. Models

The base models were created in order to benchmark the postponement strategy against the

current production process and associated costs. It allows an analysis to be made by comparing

what is currently happening to what could happen with a change in the inventory production

process. To fully visualize the impact of a postponement strategy on the company’s supply chain,

we built models for both naked vials and finished goods inventory in order to capture all inventory

changes. Each of the models contains input data such as demand parameters, lead times and cost.

As an output, we get different cost components and different total costs for each specific stage of

production.

4.2.1. Materials Model

Materials model covers full planning process for packaging materials, labels and vials. As

agreed with the sponsoring company, the only part of materials process which might change while

switching to postponement strategy, will be labels planning. Example of results can be seen in

Table 4-2. Some of the 3PL companies offer “live” printing of labels during last stage of

customization which allows the company to not keep any labels inventory in their supply chain.

Table 4-2 Materials Model Cost Comparison

Model Base With Live Printing

Labels $ 3,963 -

Bottles $ 22,271 $ 22,271

Packaging $ 5,391 $ 5,391

Total $ 31,625 $ 27,662

Each of the models uses demand and lead time data as an input for the calculation which

are further used to calculate purchase, ordering and pipeline inventory holding costs. These cost

equations are used across all models (materials, naked vials, and finished goods). As per current

planning process, all models assume that the company keeps 90 days of safety stock and that

number is used to determine final inventory holding cost. With that information we can determine

the final materials inventory that the company has in its supply chain.

4.2.2. Naked Vials Model

The make-to-order customization strategy is almost identical to that of the base model for

naked vials. It assumes that inventory is stored on the naked vial level and that final SKUs are

produced just after the customer orders are received. This approach would help the company to

optimize their inventory and reduce the risk of obsolescence. The initial stage of manufacturing is

identical to that in the base scenario where drug substance is produced in big tanks in

manufacturing facility. After that, tanks are transferred to another facility where naked vials are

filled with drug substance. The model assumes that the company keeps most of their safety stock

for naked vials inventory, taking into consideration the aggregated demand coming from final

SKUs.

The total cost of naked vials has three components. First, the purchase and ordering costs

are the same as in the base scenario because the company aggregated planning process for their

drug substance. In addition, order quantity and purchase cost don’t change. The additional

component which is not included in base model is there is now safety stock on naked vials. Under

the customization model, all naked vials are packed into boxes and shipped into a 3PL facility

where they will wait for customer orders to be placed, while previously they were labeled in

packaging plant and stored in the warehouse at final SKU level. The final customization part of

the process is covered in finished good part of the model.

4.2.3. Finished Goods Model

The last stage of the process occurs after the customer order is received. Naked vials are

labeled at a 3PL facility and shipped directly to customers. Since the sponsoring company did not

connect us with 3PLs of their choice, we were not able to model their actual problem. However,

based on available literature and best industry practices we identified three possible scenarios.

The first scenario assumes that the company acquires an incremental cost per unit for final

customization. There is no ordering cost component, and the minimum customization quantity is

equal to one unit. This scenario represents the most agile and flexible customization option,

however, in many cases it is the most expensive one. The second scenario assumes only a fixed

cost component per order with a fixed order quantity. Based on industry research those scenarios

are the most popular across the industry and the cost associated with them is usually the lowest.

The last scenario assumes both a fixed cost per order and an incremental cost per unit with a fixed

order quantity. Similarly, to the second scenario, this customization strategy sacrifices agility but

usually decreases customization cost and should be used for mid-volume SKUs. For each scenario

we calculated all three components of total cost taking into consideration the final cost per unit,

final order cost as well as lead-times associated with that process. Figure 4-1Error! Reference

source not found. shows final postponement model proposed to our sponsoring company.

Figure 4-1 Postponement Model Overview

4.3. Model Formulation

The equation below represents total cost equation used as a base for our model formulation.

It includes purchase cost, ordering cost and inventory holding cost. The same equation was

used to formulate all three inventory models. Differences between base and postponement

models were described in Section 4.2.

𝑇C = 365D𝑐p + 𝑐t (365D

Q) + 𝑐e (

Q

2+ kσDL + DL),

Table of Notations:

Symbol Meaning

𝐷 Daily Demand

𝑐𝑝 Purchase Cost

𝑐𝑡 Order Cost

𝑐𝑒 Holding Cost

𝑄 Order Quantity

𝐿 Lead-Time

𝑘 k-Factor

𝜎𝐷𝐿 Demand Deviation over Lead-Time

5. Analysis

In our analysis we will explore how change of average volume, volatility, and purchase

price impacts final total cost. We will generate insights about potential total cost saving, main

drivers of that difference, breakeven costs and recommended actions which might reduce our

sponsoring company’s costs. Throughout we will analyze one product with medium volume,

medium volatility, and medium price to analyze throughout by doing a series of sensitivity

analyses to evaluate how different parameter values impact the total cost. In addition, we will look

at how changes to volume, volatility, and price also impact the overall breakeven analysis. We

finished our analysis by running a Monte Carlo Simulation to provide additional insight to

randomized changes in the values.

5.1. Product Selection

Based on the data provided by the sponsoring company, we calculated summary statistics

for the products presented in Table 5-1. For further analysis we selected Product C, with average

monthly demand of 2,690 units, monthly standard deviation of 700 units, root mean square error

(RMSE) of 593 units, relatively low volatility with coefficient of variation equal to 0.26 and

purchase price of $1,437.

Table 5-1 Summary Statistics of Sponsoring Company Products

Summary Statistics (Actuals)

Product Mean (monthly) StdDev (monthly) Min Max 25% 75% COV RMSE

A 699 294 294 1404 478 898 0.42 193

B 2425 1274 504 5001 1492 3650 0.53 3441

C 2690 700 1428 4986 2405 3021 0.26 593

D 1585 1598 0 5604 290 2643 1.01 2488

E 107 94 0 300 16 173 0.88 118

F 11010 5682 4560 28581 7675 12330 0.52 7784

G 8342 4993 654 21387 4794 11231 0.60 6919

H 1269 2899 0 13662 0 1412 2.28 3570

I 119 181 0 630 0 215 1.52 697

J 207 598 0 3240 0 24 2.89 5879

5.2. Total Cost and Breakeven

Total cost consists of materials, naked vials and finished goods costs, which in the end are

summed and compared to the base scenario which is currently used by the company. Table 5-2

presents cost breakdown for Product C.

Total materials cost represents only 0.03% of total cost for the company’s products. Cost

savings on materials can be achieved by switching to live printing of labels at the 3PL location

instead of keeping them in stock. However, this is only a small component of total cost and should

not be a major focus of our sponsoring company. In addition, final numbers need to be evaluated

once the company gets a financial quote for live printing activities from their 3PL partner.

Naked vials inventory will always be higher for make-to-order scenario compared to base

scenario. This is because in all postponement scenarios we make a conscious decision to keep

safety stock of naked vials. This activity for Product C will increase total cost by $0.26 million but

will help the company to reduce the risk of obsolescence and increase their order fulfillment agility.

Finished goods inventory is the major driver of cost saving if the company decides to

switch to the postponement model. In their current supply chain design, the sponsoring company

keeps their full safety stock in finished goods to cover total lead time of 104 days. If the company

decides to change their operations and switch to postponement model, they will keep safety stock

only to cover uncertainty in the labeling process, which takes 5 days. This change will enable the

company to deliver $2.83 million in cost savings as a result of lead-time reduction, which implies

much lower safety stock.

Table 5-2 Total Cost Summary for Product C

Model Base Scenario Modeled Scenario Comparison

(Model vs Base) Total Cost Model Total Cost

Materials $34,685.24 Live printing $27,963.92 -$6,721.33

Vials $36,412,390.99 Customization (the same for all) $36,670,790.97 $258,399.99

Finished Goods $50,319,314.97

Customization #3 (fixed + variable cost) $47,473,820.88 -$2,845,494.08

Final Total Cost $86,766,391.20 $84,172,575.77 -$2,593,815.43

Breakeven analysis gives us visibility of the maximum additional cost for each

postponement scenario. It also informs us when it makes sense to change planning strategy for a

specific product. It generates managerial insights and makes negotiations with potential 3PL

partners easier and is data driven. For scenarios in which the company acquires only fixed or

variable additional cost, breakeven analysis will give us a specific number. If real cost is lower

than breakeven cost, the company should change their current operations model to fully leverage

the financial benefits. The breakeven analysis for the last scenario, which combines both fixed and

variable cost, will give us an understanding of what the maximum fixed and variable cost might

be for that planning strategy. Figure 5-1 presents a graphical interpretation of breakeven.

Figure 5-1 Breakeven Curve for Product C

5.3. Sensitivity Analysis

The sensitivity analysis gives us an overview of how different variables impact final total

cost. In this model volume, volatility, price, lead time, and service level will be analyzed in

separate sensitivity analyses in order to see the impact on the total cost. It allows us to determine

at which values the total cost is the lowest, providing for each product a loose recommendation of

the optimal parameter value for optimizing the inventory cost. The sensitivity analyses for lead

time and service level are executed only at the naked vial level, due to the sponsoring company’s

request, while volume, volatility, and price are executed at the overall level in order to get a sense

of how these parameters impact the entire process and corresponding costs.

5.3.1. Demand and Volatility Sensitivity Analysis

Due to the nature of anonymized data and similarities between different products, instead

of focusing on analyzing specific SKUs, we performed sensitivity analysis to compare

postponement strategy and base scenario currently used by the company to get an understanding

under which circumstances each of the scenarios might be more cost effective. As a baseline for

our analyses, we used planning and financial parameters for Product C, and we created 25 different

scenarios (five average demand values, five volatility parameters) to fully evaluate the impact of

volume and volatility on final total cost. Results are shown in Table 5-3, where favorable scenarios

are highlighted in green.

In 21 out of 25 scenarios it makes more sense for the sponsoring company to use the

postponement model. The only four situations when the company should continue doing their

current processes are very high volatility products with medium/high monthly volume.

Table 5-3 Demand and Volatility Sensitivity Analysis

5.3.2. Leadtime Sensitivity Analysis

When looking at our lead time sensitivity analysis, we were able to evaluate how different

lead times impact the total cost for naked vials. We used an array of lead times ranging from 70 to

150 days in our analysis. These values directly impacted safety stock values, which in turn

impacted the holding cost. In this sensitivity analysis, holding cost is what changed across the final

total costs. Figure 5-2 presents a relationship between lead time and total cost, which is almost a

perfect linear relationship since the majority of cost is driven by purchase cost. For example, 96%

of cost is still purchase cost. It is important to note that lead time is still curving a bit, however, its

influence on the total cost is insignificant.

Avg Monthly Demand Volatility Measure RMSE (monthly)

Total Cost

Postponement Total Cost Base

Cost saving (postponement

vs base)

10000 0.1 1000 312,154,698$ 319,667,129$ (7,512,431)$

6000 0.1 600 187,357,583$ 191,948,678$ (4,591,095)$

3000 0.1 300 93,759,746$ 96,159,840$ (2,400,093)$

1000 0.1 100 31,361,189$ 32,300,614$ (939,426)$

200 0.1 20 6,401,766$ 6,756,924$ (355,158)$

10000 0.3 3000 313,420,330$ 319,667,129$ (6,246,800)$

6000 0.3 1800 188,116,962$ 191,948,678$ (3,831,716)$

3000 0.3 900 94,139,436$ 96,159,840$ (2,020,404)$

1000 0.3 300 31,487,752$ 32,300,614$ (812,862)$

200 0.3 60 6,427,078$ 6,756,924$ (329,846)$

10000 0.8 8000 316,584,409$ 319,667,129$ (3,082,720)$

6000 0.8 4800 190,015,409$ 191,948,678$ (1,933,269)$

3000 0.8 2400 95,088,660$ 96,159,840$ (1,071,180)$

1000 0.8 800 31,804,160$ 32,300,614$ (496,454)$

200 0.8 160 6,490,360$ 6,756,924$ (266,564)$

10000 1.2 12000 319,115,672$ 319,667,129$ (551,457)$

6000 1.2 7200 191,534,167$ 191,948,678$ (414,511)$

3000 1.2 3600 95,848,039$ 96,159,840$ (311,801)$

1000 1.2 1200 32,057,286$ 32,300,614$ (243,328)$

200 1.2 240 6,540,985$ 6,756,924$ (215,939)$

10000 2 20000 324,178,199$ 319,667,129$ 4,511,070$

6000 2 12000 194,571,683$ 191,948,678$ 2,623,005$

3000 2 6000 97,366,797$ 96,159,840$ 1,206,957$

1000 2 2000 32,563,539$ 32,300,614$ 262,925$

200 2 400 6,642,236$ 6,756,924$ (114,688)$

Figure 5-2 Lead Time Sensitivity Analysis

5.3.3. Service Level Sensitivity Analysis

Our final sensitivity analysis looked at how different service levels directly changed the

total cost. Since service level is a lever companies can directly manage, the sponsoring company

wanted to understand how higher service levels impacts naked vials inventory costs. Since the only

parameter that was changing in the equation was a k-factor, the curve as seen in Figure 5-3 is the

normal inverse function of the normal distribution with exponential growth.

Figure 5-3 Service Level Sensitivity Analysis

5.4. Monte Carlo Simulation

To fully understand the impact of possible uncertainty on the final total naked vials cost,

Monte Carlo simulation was incorporated into the analysis. The parameters which we decided to

randomize are total lead time, average daily demand, and variability of demand over lead time.

The simulation had been built in such a way that, that the sponsoring company can input the desired

parameters directly into the simulation part of the final model. After that, all parameters are

randomized using normal distribution with mean and standard deviation provided in the previous

step. In order to evaluate the distribution of the simulation results, we used a bin size of $100,000

based on the array of average naked vials inventory costs. These bin sizes allow us to properly

analyze the spread of randomized values. At the end of simulation, final impact on total cost is

visualized on a histogram. When evaluating the results of the simulation for our model, the results

seen in Figure 5-4 reflect a normal distribution curve. This shows that the randomized probabilities

result in most of the total cost for Naked Vials values to be centered around the mean. In about

$36,300,000.00

$36,400,000.00

$36,500,000.00

$36,600,000.00

$36,700,000.00

$36,800,000.00

$36,900,000.00

Nak

ed v

ials

to

tal c

ost

Desired service level

95% of all cases cost would be between $300,000 and $1,300,000. This shows the upside and

downside risk associated with costs outside of that range. It is important to note there is a likelihood

that the policy recommendation would change based on this Monte Carlo Simulation. With 65%

of all possible realizations this is still the right recommendation; however, there the

recommendation would change in about 35% of scenarios. This shows there is always going to be

a variability in the cost. Figure 5-4 shows how likely the recommendation is wrong.

Figure 5-4 Monte Carlo Simulation of Naked Vials Cost

6. Conclusion

6.1. Insights and Management Recommendations

The modeling approach uses a realistic method even though the data may not necessarily

be “real.” It provides a real take on a version of a problem facing both the sponsoring company

and the industry. Based on the results of the analysis above, we can see that ~85% of the product

scenarios created by the sensitivity analysis, using different volume and variability values,

recommends moving forward with a customization model versus the base model. This shows that

using a customization model optimizes inventory and saves the company money in regard to total

cost. Thus, it is recommended that management of the sponsoring company plug in actual input

values to evaluate the specific costs of each product. Assuming similar results of the model data,

management should implement a postponement strategy for products where the total cost of the

customization model is less than the total cost for current, base model.

This paper presents a realistic approach on developing customization models for a

postponement inventory model. Due to the nature of the pharma industry, specifically the long

lead times and relatively high inventory levels, there is a need to be agile, as demand for life-saving

medicines for rare diseases need to be fulfilled almost immediately. Thus, there was a need for

new design to create the most cost-efficient distribution system for life-saving medicines, such as

postponement and make-to-order. We started off by analyzing the current problem faced by the

company as well as the industry. This led us to identifying the most favorable design option for a

postponement strategy by reviewing different research papers. Next, the actual modeling of the

total cost was done by breaking it down into three different models based on the overall inventory

processes: materials, naked vials, and finished goods. We analyzed the base scenario faced by the

company and compared it to different customization (postponement) scenarios to evaluate the

performance trade-offs and total costs. Overall, we were able to evaluate what an optimal

postponement strategy looks like compared to a base scenario, and how beneficial delaying the

customization portion of the process can be to a company’s inventory status as well as to cost

savings.

6.2. Future Research

Based on the outcomes of the model, the sponsoring company should incorporate industry

regulations into the model in order to properly evaluate the overall picture, how a postponement

strategy impacts the total supply chain processes and costs. Once this step is complete, they should

continue to explore additional contracts with current and potential 3PL partners. This additional

information of evaluating different costs and outcomes can increase the buying power of the

company by providing additional leverage of the insights. By evaluating other opportunities and

backing assumptions with realistic data and model outcomes, the sponsoring company could fully

maximize their opportunities of minimizing their costs with future partnerships based on current

state of the market.

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Appendix

Table 6-1 Detailed Table of Model Inputs

Item Type/Stage Input Data Data Source

Materials Service Level Assumed based on conversation with

the company

Materials Current MOQ/MPQ Uncertain data

Materials Target Safety Stock (coverage) Provided by the company

Materials Review Period Lead Time

(Days) Assumed based on conversation with

the company

Materials Frozen planning horizon Lead

Time (days) Assumed based on conversation with

the company

Materials Quality Release Lead Time

(days) Assumed based on conversation with

the company

Materials Transportation Lead Time


the company

Materials Lead Time Deviation Uncertain data

Materials Purchase Cost ($/unit) Uncertain data

Materials Current Order Cost ($/order) Uncertain data

Vials Service Level Assumed based on conversation with

the company

Vials MOQ/MPQ Uncertain data

Vials Review Period Lead Time

(Days) Provided by the company

Vials Frozen planning horizon Lead

Time (days) Provided by the company

Vials Quality Release Lead Time

(days) Provided by the company

Vials Transportation Lead Time

(days) Provided by the company

Vials Lead Time Deviation Uncertain data

Vials Purchase Cost ($/unit) Uncertain data

Vials Current Order Cost ($/order) Uncertain data

Finished Goods Base Scenario

# Countries Per Product Provided by the company


Service Level Assumed based on conversation with

the company Finished Goods Base

Scenario Target Safety Stock (coverage

in days) Assumed based on conversation with

the company


Review Period (days) Provided by the company


Frozen planning horizon (days)

Provided by the company


Quality Release (days) Provided by the company


Lead Time deviation (days) Uncertain data


Purchase Cost ($/unit) Provided by the company


Order Cost ($/order) Uncertain data


MOQ/MPQ Uncertain data


Transportation Lead Time to DC (days)

Provided by the company

Finished Goods Customization Scenarios

Review Period (days) Assumed based on conversation with

the company Finished Goods

Customization Scenarios Frozen planning horizon


the company


Quality Release (days) Assumed based on conversation with

the company


Lead Time deviation (days) Uncertain data


Purchase Cost ($/unit) Provided by the company


MOQ/MPQ Uncertain data


Cust #1 Potential Variable Cost ($/unit)

Uncertain data


Cust #2 Potential Fixed Order Cost

Uncertain data


Cust #3 Potential Variable Cost ($/unit)

Uncertain data


Cust #3 Potential Fixed Order Cost

Uncertain data

Documents

How a Postponement Strategy can Reduce Cost and Lead Time