Understanding Supply Chain Trade-Offs through Models and Scenario Planning with a
Focus on Postponement
by
Concepcion Alexandra Kafka
B.S.E. Chemical Engineering, Princeton University, 2009
Submitted to the MIT Sloan School of Management and the Engineering Systems Division in
partial fulfillment of the requirements for the degrees of
Master of Business Administration
and
Master of Science in Systems Engineering
in conjunction with the Leaders for Global Operations Program at the
Massachusetts Institute of Technology
Coi \J
~LL
June 2015
C*
C 2015 Concepcion Alexandra Kafka. All rights reserved.
The author hereby grants 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
_________Signature redacted
MIT Sloan School of Management and the Eygineering gys;tems Division
May 8, 2015
Certified by
Signature redacted
Daniel Whitne>;hjiesis Suervisor
Senior Lecturer, Emeritus
Engineerg Systems Division
Certified by
Signature redacted
___
'Donald Rosenfield, Thesis Supervisor
Senior Lecturer
MIT Sloan School of Management
Approved by
Signatu re redacted
Munther A. Dahleh
William A. Coolidge Professor of Electrical Engineering and Computer Science
Chair, ESD Education Committee
Approved by
Signature redacted
V
Maurae
n
Director, MBA Program
MIT Sloan School of Management
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2
Understanding Supply Chain Trade-Offs through Models and Scenario Planning with a
Focus on Postponement
by
Concepcion Alexandra Kafka
Submitted to the MIT Sloan School of Management and the Engineering Systems Division
on May 8, 2015, in partial fulfillment of the
requirements for the degrees of
Master of Business Administration
and
Master of Science in Engineering Systems
Abstract
The two objectives of this project were to develop an understanding of the challenges and
opportunities of the supply chain of a family of currently marketed products manufactured
overseas and distributed/sold worldwide and to increase the agility of the supply chain while
achieving a target service level of 99% and maintaining or decreasing costs.
A model was created to explore the current supply chain as well as the idea of supply chain
agility through the implementation of postponement as models can easily be used to understand
the cause and effect relationships through the ability to analyze any number of possible outcomes
in a time and cost effective manner. Monthly demand and forecast data was analyzed to
determine if there were in biases in the forecasts and to understand the relation between demand
and forecast error through the use of a power law model.
The forecast and demand data demonstrated a strong log-log relationship between RMSE and
demand implying that there are economies of scale when demand is aggregated. The model
shows that the implementation of postponement can reduce overall inventory levels, leading to
decreased supply chain costs (if the cost of implementing postponement is less than the savings
achieved through the inventory decrease). In looking at air versus ocean transport, the savings
coming from inventory reduction due to decreasing lead times outweighed the increase in costs
for both supply chain designs. As expected, increasing forecast accuracy leads to a decrease in
safety stocks while decreasing forecast accuracy leads to an increase. Finally, increasing demand
lead to increasing safety stocks and costs while decreasing demand had the opposite effect. For
the forecast accuracy and changing demand scenarios there is a larger magnitude of savings for
the current design of the supply chain than for one with postponement.
Thesis Supervisor: Daniel Whitney
Title: Senior Lecturer, Emeritus
Engineering Systems Division
Thesis Supervisor: Donald Rosenfield
Title: Senior Lecturer
MIT Sloan School of Management
3
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4
Acknowledgements
First, I would like to thank my advisors Dan Whiney and Don Rosenfield whose insights, advice,
direction and most importantly, unwavering support were instrumental. This process would not
have been the same without you.
I would also like to thank my supervisor, Linda, and all of the individuals at my internship
company who were part of this unique experience. Additionally, to the self-named kryptonite
team, thanks for your company and, when the unexpected happened, for your support and
creativity in always helping me figure out the next step.
I am grateful to the LGO Program for its support of this work. The staff was incredibly helpful
and understanding in their guidance.
LGO classmates and friends, I am glad to have shared the last two years with you. There are so
many unforgettable memories and I cannot imagine going through the last two years with a
different group of people.
Finally, Donald, Juliana, Mom and Dad, you are the best. You are always there for me every step
of the way. Your love and encouragement have helped me reach my goals, and for that, I am
extremely grateful. Thank you.
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6
Table of Contents
A b stra ct ...........................................................................................................................................
3
Acknowledgem ents.........................................................................................................................
5
Table of Contents ............................................................................................................................
7
L ist o f F igures .................................................................................................................................
9
L ist o f Tab les ................................................................................................................................
10
1
11
2
3
4
5
Introduction...........................................................................................................................
1.1
Project Context and Problem Statem ent.........................................................................
11
1.2
Project Objective and Goals........................................................................................
12
1.3
Project Approach............................................................................................................
12
1.4
Thesis Overview .............................................................................................................
12
Product Context and Overview ..........................................................................................
13
2.1
Background of Company ............................................................................................
13
2.2
Overview of Product and Supply Chain.........................................................................
13
Literature Review ..................................................................................................................
16
3.1
Supply Chain Agility ....................................................................................................
16
3.2
Postponem ent .................................................................................................................
21
3.3
Power Law M odel ..........................................................................................................
25
3.4
Application of Literature to Project ............................................................................
27
M ethodology: Data Collection and Analysis ....................................................................
28
4.1
Current State High Level Analysis.............................................................................
28
4.2
Dem and and Forecast Analysis ....................................................................................
29
4.3
Possibilities for Future States......................................................................................
30
Supply Chain M odel .............................................................................................................
33
5.1
Software Selection......................................................................................................
33
5.2
M odel Assumptions....................................................................................................
33
5 .3
In p uts ..............................................................................................................................
34
5 .4
O utp uts ...........................................................................................................................
35
5.5
M odel Design .................................................................................................................
35
7
6
7
5.6
Equations........................................................................................................................
36
5.7
Effects of Dem and Aggregation..................................................................................
40
Analysis of the Supply Chain ............................................................................................
42
6.1
Base Case .......................................................................................................................
42
6.2
Scenarios for Future ...................................................................................................
44
Recom m endations and Conclusions .................................................................................
61
7.1
Conclusions ....................................................................................................................
61
7.2
Recom m endations for Future W ork...........................................................................
63
References.....................................................................................................................................
8
65
List of Figures
Figure 1. Network design of the current supply chain...............................................................
29
Figure 2. Network design for supply chain utilizing postponement..........................................
31
Figure 3. Relationship between root mean square error and mean demand for all SKUs...... 40
Figure 4. Relationship between root mean square error and mean demand for product family... 41
Figure 5. Inventory type and and levels across the current supply chain. ................................
42
Figure 6. Enlarged scale of Figure 5..........................................................................................
43
Figure 7. Comparison of inventory levels between base case and postponement.................... 45
Figure 8. Comparison of transportation costs between the current supply chain and the
postponem ent design .....................................................................................................................
46
Figure 9. Comparison of safety stock costs between the current supply chain and the
postponem ent design .....................................................................................................................
47
Figure 10. Effects on inventory levels by changing the mode of transportation from ocean to air.
The graph on the left shows what the levels of inventory would be for the current supply chain.
The graph on the right shows what the levels of inventory would be for the postponement supply
ch ain ..............................................................................................................................................
48
Figure 11. Difference in inventory between current supply chain design and postponement
supp ly ch ain design .......................................................................................................................
49
Figure 12. Comparison of transportation costs between ocean and air shipments.................... 50
Figure 13. Comparison of safety stock costs between ocean and air shipments. ...................... 51
Figure 14. Effects on inventory levels of a increase in forecast accuracy. The graph on the left
shows what the levels of inventory would be for the current supply chain design. The graph on
the right shows what the levels of inventory would be for the postponement supply chain. ....... 52
Figure 15. Changes in safety stock costs as a function of increasing forecast accuracy........... 53
Figure 16. Effects on inventory levels of a decrease in forecast accuracy. The graph on the left
shows what the levels of inventory would be for the current supply chain design. The graph on
the right shows what the levels of inventory would be for the postponement supply chain. ....... 54
Figure 17. Changes in safety stock costs as a function of decreasing forecast accuracy. ........ 55
9
Figure 18. Diagram showing the total inventory units and costs of each of the supply chain
o p tio ns...........................................................................................................................................
56
Figure 19. Effects on inventory levels by changing the demand. The graph on the left shows what
the levels of inventory would be for the current supply chain. The graph on the left shows what
the levels of inventory would be for the postponement supply chain........................................
57
Figure 20. Graph shows the changes in inventory between current design and postponement
d e sig n............................................................................................................................................
58
Figure 21. Comparison of transportation costs for increases and decreases in demand............ 59
Figure 22. Comparison of costs associated with safety stock for increases and decreases in
d eman d ..........................................................................................................................................
60
Figure 23. Summary of inventory levels for all scenarios presented........................................
61
Figure 24. Summary of total costs for all scenarios presented. ................................................
62
List of Tables
Table 1. Parameters used in model to simulate increases and decreases in power model estimated
R M S E ............................................................................................................................................
10
51
1
Introduction
This thesis explores two concepts. The first is exploring supply chain agility through the
implementation of postponement. The second is the use of a model to proactively perform
scenario planning as opposed to reactive planning to a specific disruption through the analysis of
a global supply chain of a medical device.
1.1
Project Context and Problem Statement
In 2015, an operating company of Company A, Company B, will be launching an unusually high
number of new products to the market. These products are medical devices that help individuals
manage and treat specific conditions. They are sold directly to its customers via retail as well as
to institutions via tenders. The high number of launches, combined with historically low forecast
accuracies, decreasing line item fill rates, and increasing demand then leads to a risk of the
company being unable to supply the market. Given the types of products that this company
produces, not meeting demand has a detrimental effect on their patients and goes against the
company's values.
To mitigate the risk of not meeting demand, changes in the supply chain need to occur. These
revolve around three key questions as to where to invest to improve the overall operation:
1) Does manufacturing capacity need to increase?
2) Does forecast methodology need to improve?
3) Will re-designing the supply chain network yield improvements?
Answering each question requires its own approach. Question one requires a deep dive into the
manufacturing operation, understanding the available capacity, and recommending solutions for
increasing capacity if that increase was needed. To provide an answer to question two, the
current manner in which a forecast is produced needs to be understood as well as the
implications to the overall supply chain of the accuracy of the forecast. Finally, re-designing the
supply chain to show possible future states and comparing the options to the current state answer
question three. Additionally, expecting that investments in the supply chain would occur, a
fourth question arises. As we make changes to ensure supply, can we simultaneously improve
supply chain agility without increasing costs? The work presented in this thesis provides a
11
method to answer question three and explore the idea of supply chain agility through
postponement.
1.2
Project Objective and Goals
There are two main objectives to this project. The first objective is to develop an understanding
of the supply chain's opportunities and challenges through the creation of a model to analyze
trade-offs between various scenarios. These scenarios represent possible future states for the
supply chain, including a network re-design. The second objective is to increase agility of supply
chain constrained to increasing service level and decreasing total costs. This objective will be
studied by analyzing the effects of re-designing the supply chain, implementing ideas of
postponement.
1.3
Project Approach
This project followed a linear approach. The first was to decide to build a model. Then, the
inputs for data/information that the company should have for the current supply chain were
decided as well as the outputs, the information that we wanted to learn/gain from a model. An
analysis was then performed on the forecast/demand data to understand the relationship between
these inputs and be able to build a model. Upon completing the analysis, a simple model of the
current supply chain was built. Validating the model was the next step to make sure that changes
in inputs lead to the expected changes in outputs. The last step involved scenario planning and
analysis in which an alternate supply chain is proposed that implements ideas of postponement as
well as scenarios that study forecast accuracy, transportation and changes in market demand.
1.4
Thesis Overview
This thesis will first provide an overview on the company, type of product being studied and its
corresponding supply chain. Then, literature on supply chain designs, postponement and a power
law model with regards to forecast error and demand will be presented. The methods utilized to
analyze the supply chain will be described including the development of the model and its
assumptions. Results, which include a second supply chain design as well as scenarios
representing possible future states, will be discussed. Finally, the thesis will conclude with
recommendations for future work and conclusions.
12
2
Product Context and Overview
This chapter will provide an overview on the company, the product and supply chain that will be
analyzed in later sections.
2.1
Background of Company
Company A is a large, diversified healthcare company with a global presence, operating within a
complex and regulated environment. The company has manufacturing and distribution centers
worldwide. It is divided into global franchises which are further subdivided into operating
companies. The focus of this project is specifically within one operating company, Company B.
Just like the parent company, Company B has an international presence and distributes medical
devices to patients around the world.
2.2
2.2.1
Overview of Product and Supply Chain
Product
The products manufactured by Company B are a device and its corresponding accessories, used
to control a chronic condition for patients. The device and accessories follow the model observed
for razors and blades. In this traditional model razors are sold at or below cost, and the profits
come from the sales of the blades. Specifically for this project, the device of interest mimics the
razor, and the corresponding accessories mimic the blades. Company B has decided to separate
the supply chain of the device from the supply chain of the accessories. This thesis studies the
supply chain of the device, the item that generates zero, or negative profits, on the majority of
sales of this item for Company B.
Company B produces many product families that provide similar functions. Within a product
family there are few, but significant, variations between stock keeping units (SKUs). The
variations arise out of a customization step in which one generic device (or assembly) creates
many SKUs. This customization step adds a significant percentage of the value/cost of the
product.
13
There are two major channels of distribution for the device. The first is through retail and the
second is through bulk contracts to governments and physicians. Given that this is a medical
device for a chronic condition, there is no seasonality in sales and theoretically demand is stable
and predictable as patients require the same level of access to the device and its corresponding
accessories throughout the year. However, when large entities place bulk orders, demand
becomes unpredictable leading to issues in the supply chain and stock outs.
The customers of this product require constant use of the device and therefore will purchase the
device that they see on the shelf at the retailer when it is time to upgrade or purchase a new
device to replace the old one. They are not specifically attached to a brand and will switch if
there is a device on the shelf that is available and low cost.
2.2.2
Supply Chain
As expected from products of a large and diversified healthcare company, the supply chain for
the product manufactured by Company B has a worldwide presence, crossing international
borders is complex. The devices are manufactured at a single facility overseas and shipped to
regional distribution centers as packaged finished goods in different continents. In the rest of this
thesis, regional distribution centers will be referred to as distribution centers. From there, the
finished goods are transported to local distribution centers (or also retailers) and then finally to
the end users. At the moment the majority of shipping occurs via ocean, with expedited shinning
for urgent, last minute orders occurring via air which over the years has been increasing in
There are two major challenges of the current supply chain. The first is the increasing levels of
air shipments. Although air shipments decrease the lead time between the manufacturing site and
the distribution center, they increase the cost of shipping as compared to ocean. The limitation of
the lower cost ocean shipment is the long lead time. It would not be possible to offer the desired
service to customers if urgent orders could not be expedited via air. Additionally, during peak
times for retail, it is difficult to find available capacity on airplanes for shipments leading to no
viable transportation alternative. The second challenge is that there is a possibility that they are
running into the capacity limit of the manufacturing facility. This could be due to a variety of
14
factors that include: the actual volume/growth of retail sales, the accuracy of the forecast, the
significantly larger size of unexpected orders, inventory management policies or operational
schedules of the facility.
Because of these challenges, as well as an increase in the number of product launches in the
coming year, it is important to further understand the supply chain and have a method with
which to proactively plan instead of making decisions reactively.
15
3
Literature Review
This chapter will review work that has been performed to provide context for the thinking,
analysis and approach presented. The idea of supply chain agility will be explored with an
emphasis on postponement. Then, a power law model will be presented which is an important
component to analyzing supply chains and the effects of the implementation of postponement.
3.1
Supply Chain Agility
Supply chain agility is a term that has evolved over time, and has not yet reached a mature
definition. The concept began with an article published by Hayes and Wheelwright in 1979
making an argument that firms could gain a competitive advantage through strategically
matching the life cycles of the product and the process (Hayes 1979). Instead of divisions within
companies making silo-ed decisions, they suggested that manufacturing and marketing decisions
should be inter-related and continuously reviewed as they would impact making and distributing
of a product. This would affect how a company defines its product, allowing them to figure out
what the authors call their "distinctive competence" or, in other words, their value proposition to
their customers. This value proposition combines the product itself as well as a company's
response to providing the product.
Later, in 1997 Marshall Fisher described a framework for a company to think about how to
match the best supply chain to their products. He divides products into two categories based on
the behavior of the demand. The first is functional products which exhibit a predictable demand
and have low margins, requiring the supply chain to be efficient. The second is innovative
products which exhibit unpredictable demand, allowing for more company profits and therefore
requiring a responsive supply chain to deal with the uncertainty in demand. He expands upon the
concept of integrated decision making between marketing and manufacturing by dividing the
supply chain into two separate functions. The physical function does the sourcing, producing,
and transporting of raw materials, inventory and finished goods, while the marketing function
ensures that the right mix and volume are at the right place at the right time. Because both
functions need to perform to have a supply chain, decisions need to be inter-related. Finally, he
16
says that as supply chain designs should be governed by demand behavior, different products
within one company will require a different supply chain. (Fisher 1997)
A few years later, Martin Christopher defined agility as "the ability of an organization to respond
rapidly to changes in demand, both in terms of volume and variety." (Christopher 2000) He lists
the characteristics of agility as:
" Market sensitive, reading and responding to real demand
" Virtual, based on information and not just inventory
" Process integration, collaborative working between buyers and suppliers sharing
information
* Network, linked group of partners that leverage their strengths and competencies
The author goes on to note that an agile supply chain is not the same thing as a lean supply chain.
A lean supply chain is characterized by the removal of excess inventory and the idea of just-intime deliveries. It is a strategic decision for when demand is certain, the mix (or variety) of
products is low and the volume of these products is high. However, when mix of products is high
and volume of each variety is low, a lean supply chain is undesired because it is difficult to
remove the excess inventory from the supply chain.
Similarly, and more recently, John Gattorna also stressed strategic decision making on the supply
chain design based on the segmentation of consumer behavioral patterns, through "a deep
understanding of the range of customer buying behaviors that are present in any product/market
situation" (Gattorna 2010). He grouped supply chains into four categories, each intending to
serve a specific customer base: continuous replenishment, fully flexible, lean, and agile. A
continuous replenishment supply chain has a focus on customer relationships and should be
designed for products that have predictable demand. It can be managed through partnerships and
collaborations with customers. A fully flexible supply chain is required when demand is
completely unpredictable and a focus is required for creative problem solving in order to quickly
respond to the necessary demand. A lean supply chain is effective when there is predictable,
regular demand pattern but relationships with customers are not as important for performance, as
17
would be expected for mature products. Finally, he describes an agile supply chain as being
required when there is unforeseen demand and a loose relationship with customers.
Given the demand patterns for this product of interest discussed in this project, the behaviors
described by Gattorna in the agile and/or lean categories match the two channels of distribution
for Company B's product (Gattoma 2010). Demand can be forecasted for the devices sold via
retail because there is no seasonality and likely low variability due to the fact that the condition
that this product helps manage is chronic; however for products sold through contracts to large
entities, it is difficult to predict when the orders will come through the system, except for the fact
that at some point large orders will be placed. It is likely that these orders will be based on price
and therefore there is no guarantee for a return customer if a different manufacturer can provide a
similar device for a lower price. Gattoma describes the customer type that would benefit the
most from a lean supply chain as having a focus on efficiency/consistency. Their demand is
generally predictable and they are price sensitive, therefore they place a value on a supply chain
that is efficient and low cost. For this project these are the customers that purchase this product at
retailers. For customers that are demanding or require a quick response, the supply chain needs
to be agile. This supply chain can produce a rapid response to uneven demand or meet delivery
times for urgent, unplanned, last minute orders; however, there is a higher cost associated with
being able to meet these requirements. For the supply chain studied in this thesis, this uneven
demand arises from the large entities that place orders in bulk when they need them at
unpredictable times. At the moment these surges disrupt standard operations and at times, lead to
low service levels at the distribution centers.
3.1.1
Lean Supply Chain
A lean supply chain is one in which emphasizes 100% reliability and low cost to the customers
(Gattoma 2010). According to Gattorna, these customers value a steady supply and the lowest
prices for a stable product line, therefore they are not loyal to a specific product or brand - just
the product that is on the shelf when they need it. Because of this buying behavior, lean
implements a "push" strategy for its inventory, meaning that it operates in a make-to-forecast
model where the inventory is produced, transported and distributed according to a forecast and
not an order from a customer to ensure that it is immediately available when a customer orders it.
18
This entire system is then designed to deliver efficiency, and move as smoothly as possible to
maintain the low costs that customers value (Gattorna 2010).
This reliable and low cost supply chain is made possible by the fact that both demand and lead
times are predictable, allowing for more accurate forecasts and a high level of capacity
utilization. Forecasts are also generated at the generic form of a product (the standardized basis
of a product family) instead of individual SKUs as this forecast tends to be more accurate.
Building to these forecasts allows a company to make and distribute just enough inventory to
fulfill demand in the most cost effective manner. This reduces the amount of inventory being
held for long periods of time, or the possibility that this inventory could become obsolete on the
shelf. Through economies of scale a high level of utilization is achieved at the manufacturing
facility and throughout the distribution process which in turn lead to the lower costs and higher
cycle stocks. It is important to note that collaborations with suppliers on the supply side is
necessary to maintain the desired low and smooth level of inventory (and associated costs) across
the entire supply chain (Gattorna 2010).
The downside of this design is that responsiveness and resilience are reduced because of the low
inventory being held, fixed production schedule or associated long lead times to receive that
inventory, which are required to maintain lower costs. For this type of supply chain it is
important to consider continuity and maintain contingency plans in case unexpected disruptions
occur, as it may be difficult to recover from them given the minimalist design (Gattorna 2010).
3.1.2
Agile Supply Chain
The focus of an agile supply chain is "on being fast and also on being smart about how to align
with demanding customers" (Gattoma 2010). Therefore customers value fast service times for
any level of demand. Gattorna states that these unpredictable demand and service time
requirements tend to come from a lack of prior planning on the side of the customer, perhaps
because of chaotic and disorganized commercial practices that lead to large swings in the size of
the orders they place to their suppliers. The goal for that customer's supplier is to then design a
supply chain that can absorb the swings and satisfy customers when demand goes from
predictable to unpredictable (Gattorna 2010).
19
The lack of predictability and demand variation leads to more of a make-to-order environment
where inventory is "pulled" through the system based on customer orders, not forecasts as seen
in the lean supply chain. Gattorna continues to describe agility as a responsive mindset with a
combination of specific processes (Gattorna 2010). This is a similar definition to Christopher's
of agility being "a business-wide capability that embraces organizational structures, information
systems, logistics processes, and, in particular, mindsets. A key characteristic of an agile
organization is flexibility." (Christopher 2000) Quick decision making teams and cycles and
clarity regarding roles and responsibilities with regards to these decisions constitute the
responsive mindset. Strategic sourcing plays a key role as without fast supplier service times;
changes within the supply chain cannot take place. Suppliers become partners and are selected
not for low prices, but also for their service. Additionally, the design requires planning for
capacity and executing for demand, meaning that at times the resources will be under-utilized;
however without the expanded capacity, demand swings will not be met in a timely fashion
(Gattorna 2010).
The downside to this design resides with the redundancies required to meet the quick response
times. This requires redundancies to meet the unexpected demand surges, either within
production or distribution, which adds cost (Gattorna 2010).
3.1.3
Hybrid Supply Chain
A supply chain does not have to be lean or agile; it can be a combination of both by taking the
benefits of each design and combining them into something that can meet the behavior of the
customers more effectively (Christopher 2000). Again looking at the behaviors of the customers
for the product whose supply chain was analyzed in this thesis, this combination potentially
allows for the satisfaction of reliable supply with low costs for the retail distribution channel as
well as for the unpredictable swings in demand that come through the large entities.
Additionally, as this product is sold at or below cost, it is important for the entire supply chain to
be cost-conscious while still being able to maintain flexibility to be responsive. Van Hoek
effectively described this type of supply chain as enhancing the local responsiveness in
adaptation products to local markets while enhancing global efficiency in manufacturing of
generic modules (Van Hoek 1998).
20
A hybrid supply chain is designed around the point at which make-to-forecast and make-to-order
meet. This is known as the push-pull boundary - inventory is pushed through the system as
inventory is made from forecasts and then pulled by customer demand as needed. The physical
location of this boundary determines the type of inventory being held with the goal being to hold
inventory in the most generic form, as far downstream and geographically closest to the market
as possible to allow for the most flexibility (Christopher 2000). In addition to allowing
flexibility, this design allows for cost reductions through a smooth and efficient inventory flow
from the suppliers to the location of the push-pull boundary. The lean supply chain followed a
make-to-forecast model in which production and distribution occur based on only forecasts. On
the other hand, the agile supply chain followed more of a make-to-order model, particularly with
the demand swings in that production is based on actual orders from customers.
3.2
3.2.1
Postponement
Postponement Strategy
One structure of the hybrid supply chain described in Section 3.1.3 is known as postponement,
although postponement can take other forms as well. The concept was developed as an
innovation in supply chains (Zinn and Bowersox 1998 and Lee 1993). A definition of
postponement is the "principle of seeking to design products using common platforms,
components, or modules, but where the final assembly or customization does not take place until
the final market destination and/or customer requirement is known" (Christopher 2000). In other
words postponement is the "stocking of goods before final configuration or customization, with
final configuration and customization pulled from the postponement portal" (Beckman and
Rosenfield 2008). The key considerations involve not just where to place inventory, but what
form of inventory is going to be stored as it could be a finished good, a sub-assembly, or perhaps
even a component.
In postponement, the differentiation of a product is delayed as much as possible, until it is close
to the customer in terms of the processing/manufacturing and the geographical location of
storage. According to Christopher the benefits of this design are (1) greater flexibility in terms of
manufacturing as inventory is stored at the generic level; (2) reduced overall inventories as at the
21
generic level there are fewer variants than at the individual SKU level; (3) "easier" and more
"accurate" forecasting at the generic level as the variation captured within the individual SKU
level forecast is reduced when moving to the aggregated forecast; and (4) economies of scale
coming from the larger volume quantities of the generic inventory (Christopher 2000).
Beckman and Rosenfield provide factors to consider when thinking about designing a supply
chain that utilizes postponement and the placement of the push-pull boundary in that system
(Beckman and Rosenfield 2008). These factors are assembly capacity, lead time and modularity,
value added at distribution stage, demand uncertainty, degree of product proliferation, and
economical delivery costs/value density. On the push side of the boundary (in the case presented
here, the manufacturing of the generic, un-customized assembly) capacity needs to handle only
average demand over a specified time horizon, lead times can be long and modules can be more
complex, demand is more certain given that the volumes are higher for the generic form of a
product, and there is less variety of assemblies (as assemblies proliferate into different SKUs).
On the pull side of the boundary capacity needs to be high enough to be able to handle short term
variations, the lead times should be short to quickly satisfy customer demand, modularity should
be simple to again quickly satisfy demand, and demand will be more uncertain as there are many
more products for customers to select. The authors state that in some systems value is added at
the distribution centers as minor product customizations which allows for a decrease in delivery
costs as product proliferation is occurring at a centralized location with shorter lead times.
Upon having an understanding of these factors, there are stages in the supply chain where it is
more advantageous than other stages to build inventory buffers. There are stages where the value
of holding inventory can be maximized (regardless of the type of inventory being held) as there
are significant costs associated with holding inventory. These value maximizing stages are
before high value-added steps, before significant increases in product variety, or after variable
lead times (Beckman and Rosenfield 2008). Adding an inventory buffer before a high value add
step allows for the storing of inventory at a lower cost, therefore minimizing the holding cost of
the inventory and adding the value only when it is necessary. As the variety of products
increases, so does the inventory required to maintain a particular service level for a specific
product. Therefore anything that can be done to maintain the generic assembly configuration for
22
as long as possible before customizing the one generic assembly into many SKUs decreases the
customized inventory being held, as well as reduces the risk of that inventory becoming obsolete.
This is because the rate of product obsolescence can vary between markets as products get
discontinued at different times. As this occurs, having the ability to reassign SKUs to other
markets becomes important. If the inventory being held is not generic, then there is not the
ability to reassign SKUs to the market of interest and therefore the inventory may become
obsolete as it cannot be utilized. Finally, the lead time after the buffer inventory node should be
short and with low variability so that the longer lead time occurs before it. This provides an
additional buffer in terms of transportation time, as the direct lead time to the customer is shorter
and predictable. This shortened lead time allows for an increase in the forecast accuracy because
instead of forecasting demand two or three months out, demand can be forecasted for a week or
two of lead time.
Not all of the advantages of a hybrid supply chain are on the demand/customer side. Significant
benefits can be achieved through selecting and working on supplier relationships. Christopher
reminds us that the "lead time of inbound suppliers . . limits the ability of a manufacturer to
respond rapidly to customer requirements" (Christopher 2000). If suppliers cannot react quickly
to requests or changes, then the flexibility of the entire supply chain is dependent on how fast the
suppliers can react. Partnering with suppliers can provide a competitive advantage through the
linking of systems and processes as well as through sharing information such as visibility of
downstream demand. This sharing and integration with suppliers requires time and resources,
making it impossible to do with a company's entire current supplier base requiring supplier
rationalization (or selection) for the purposes of building these relationships. Key suppliers that
would be beneficial to consider when building these relationships would be those that provide a
critical or expensive component, many different components, or is the single source for a critical
component.
3.2.2
Example of Effective Utilization of Postponement
Several companies have been extremely successful in implementing postponement. Some of
these are Hewlett-Packard (HP) Company, Dell, and Zara (Beckman and Rosenfield 2008,
23
Christopher 2000). This section will take a closer look at HP as an example to understand the
effects of postponement.
In the late 1980s HP developed an inventory model to perform scenario planning focusing on
supply chain designs, inventory costs, and delivery service for the manufacture of Deskjet-Plus
printers at Vancouver, Washington Division of HP (Lee 1993). They began their work by
understanding the region/country specific (localization) requirements as well as their sources of
uncertainty, which were delivery of incoming raw materials, internal processes (yields), and
demand. As the process continued they realized that increasing forecast accuracy would decrease
costs and increase service levels, however increasing the accuracy of a forecast is extremely
difficult to do. They observed that air shipments reduced lead times (and therefore inventory)
between locations while costs of transportation increased.
They concluded that what they had overlooked was the "relationship between design and the
eventual customization, distribution, and delivery of the product to multiple market segments.
Segments may have different requirements of the product due to differences in taste, language,
environment, or government regulations." (Lee 1993) The product design and manufacturing
process can affect operational costs and its delivery to customers. Therefore they decided to
consider designing the printer for manufacturability in order to allow for localization. This would
occur by shipping generic printers to their distribution centers, and allowing the distribution
centers to add the few components that differentiated the printer in the different regions -the
power supply and instruction manual. It is important to note that the extra components that are
added to allow for localization should be added on/to the outside of the product. This prevents
rework from disassembling and reassembling the product, and prevents the shipment of products
in a disassembled state. The effects of delaying localization of the printers increased their
flexibility to react, reduced their inventories, reduced downtime frequency at the manufacturing
site, increased their service levels (specifically fill rate) although in the short term increased their
engineering requirements to re-design the printer to make localization feasible.
Additional insights from this study were published a few years later stating that "HewlettPackard's distribution centers can deliver highly customized PCs more quickly and cheaply than
24
competitors can" (Feitzinger 1997). HP had expanded its design for manufacturability concept
from printers to PCs, separating the purpose of a supply chain to that of (1) delivering a basic
product to customization facilities in a cost-effective manner and (2) being flexible, responsive,
and quick in delivering those orders. They then state that as "uncertainty, lead time, and
inventory and stock-out costs increase, so do the benefits of standardization" (Feitzinger 1997).
Meaning that the benefits of having a standard product that can be customized as late as possible
increase given these specific conditions. The last insight is that forecasting what the customers
want at the SKU level is most difficult at the beginning and end of a product's lifecycle. This
means that for product launches postponement yields additional benefits in order to allow a
company to react more quickly to what the actual demand happens to be. (Feitzinger 1997)
3.3
Power Law Model
To be able to model inventory requirements at different levels of the supply chain it is necessary
to be able to know the relationship between forecast accuracy and demand. The model allows for
an understanding of the impact of demand aggregation on expected forecast error and therefore
into the determination of safety stocks which has a large impact on the inventory levels of a
supply chain making use of the postponement strategy.
In Chapter 8 of Production in the Innovation Economy, a cost model is presented to look at the
decision making process of onshoring versus offshoring manufacturing (Rosenfield 2014).
Although that decision making process is out of scope for this thesis, the inventory modeling part
of that process is of interest. The relevant portion of that model is the inventory modeling part
which considers the effects of product variety on inventory levels (also presented in Rosenfield
1994). Product variety comes from having many different finished goods that come from the
customization of specific components for different markets (for instance many SKUs derived
from one generic assembly). As presented, the logic behind the model lies in the fact that when
product variety (number of SKUs) increases with decreasing volumes sold of each individual
SKU the cost of inventory will increase to maintain a specific service level. This is because you
will need to hold inventory of each SKU and as there are more SKUs, more inventory will be
needed overall to maintain a target level of service in a build-to-stock system. In a build-to-order
system there is also an associated increase in costs however it is not from holding more
25
inventory. This comes through the cost of customization of the individual SKUs with a reduced
lead time to the customer through flexible manufacturing systems or expedited transportation
costs as well as through the fact that it is more expensive to keep track of a higher variety of
components and ensure that quality is embedded into the customization and post-customization
processes.
Rosenfield states that inventory levels are driven by forecast error (Rosenfield 2014). If forecast
error is high, then the uncertainty in demand is high, leading to holding more inventories to
buffer against the uncertainties. To look at the impact of variety on inventory, it is necessary to
look at how variety impacts forecast error. Rosenfield looks at three types of relationships: (1)
inventory and forecast error, (2) forecast error and the lead time over which forecast error is
calculated and (3) forecast error and product volume. Upon theoretically analyzing these
relationships, he presents a model where,
UT,D =
where,
UD,T
KT 0 .5 D
(1)
is the standard deviation of demand (in this case units per month), D, for a given
product as a function of time, T, and K is a constant (Rosenfield 2014).
Upon presenting this theoretical model he critiques the fact that it may not match reality because
of an existent and consistent forecast bias or because demand could be correlated by time or
geography. Therefore, he states that the relationships between inventory, time, and volume must
be studied empirically.
Several authors have studied this empirical relationship in different industries (Rosenfield 1994,
Lehman 2011, Amati 2004, and Vega Gonzales 2009). They have found that the exact
relationship between the variables of interest do not show exact square root relationships;
however strong relationships do exist. The relationship then takes the following general form,
UT,D =
where,
UDT
KTaDP
(2)
is the demand variation (for the purpose of this thesis root mean square error will be
used to quantify demand variability) for a given product as a function of lead time, T, and
26
demand, D; a,
P are parameters
of the relationship between forecast error and lead time and are
between 0.5 and 1, and forecast error and demand, respectively; and K is a constant.
3.4
Application of Literature to Project
The concepts in the literature presented in this chapter revolve around supply chain agility,
supply chain design and how to best structure a supply chain to deliver a product to customers, as
well as a mathematical approach to analyze effects of time and demand on inventory. In
summary, different types of customers value different aspects of a product; therefore the supply
chain should be designed to meet these customer requirements. For the product and supply chain
presented in this thesis, a hybrid supply chain (Section 3.1.3) allows for Company B to provide
reliability, responsiveness, as well as the desired low cost. One approach to this hybrid design is
through the use of postponement. Through postponement (Section 3.2.1), customization is
moved closer to the customer allowing for shorter lead times and therefore faster service, while
the reduction in lead times should allow Company B to reduce inventory and therefore decrease
costs. Finally, the power law model developed by Rosenfield and presented in Section 3.3 will be
utilized in this thesis to model the effect of pooling the demand of all of the SKUs of a product
family to determine the number of assemblies required to satisfy the customer demand.
27
Methodology: Data Collection and Analysis
4
This chapter provides the process by which the data was collected and analyzed, as well as the
scenarios that will be analyzed with the supply chain model presented in Section 5.
4.1
4.1.1
Current State High Level Analysis
Data Collection
The data utilized for building this supply chain inventory model came from a value stream map
of the current process, SKU level data, and historical information on various costs. The value
stream map provided the material and information flows, location of inventory nodes, and
process times and lead times. The SKU level data provided a year's worth of monthly demand
and forecast information. Through conversations with individuals, the historical data for unit
standard costs, holding costs and transportation costs (ocean vs. air) were collected.
4.1.2
Current Supply Chain Map
Based on data collected, a simplified current supply chain map was generated which was the
basis for the model. The current supply chain model can be viewed, at a high level in Figure 1.
Raw materials (RM 1, RM2, etc.) are received at the manufacturing location. The materials, in
conjunction with the pre-assemblies, enter the assembly process. One type of assembly is made
per product family. Once the assemblies are made (with lead time LT 1, and process time PT 11),
they are stored as inventory until an order is placed by a distribution center. That assembly is
then customized into a finished good at the customization step (with lead time LT2, and process
time PT2). From here the finished goods are shipped to the distribution centers with lead times
LT3, LT4, and LT5 for distribution centers DC 1, DC2, and DC3, respectively. Finished goods
are stored in this form until they are "sold" to the local distribution centers. For this project, the
activities beyond the distribution centers were out of scope. It is important to note that the
Here, lead time is defined as the delay time between steps in either manufacturing or transport, while process time
is defined as the length of time of a unit operation. They could be combined, however, having them separate allows
for independently changing the length of time that the customization step can take at a postponement center vs. the
manufacturing site.
28
demand experienced by DC3 is about half of the demand experienced by DC 1 or DC2, both of
which are approximately the same size.
Raw Material Inventory
LTPre-
P1
Finished Goods
Inventory
P2
Assembiy
Pre-Assembly
DC 1
Finished Goods
LT_RM1
WIP
RM1
LTARM2
LT2
LT1
Assembly
Assemblies
LT4
DC 2
Customize
RM2
Finished Goods
A
RM2
LTRM3
RM3
LT5
LTRM4M
DC 3
Finished Goods
RM4
Manufacturing Location
Transit
Distribution Center
I
Figure 1. Network design of the current supply chain.
4.2
Demand and Forecast Analysis
For the purposes of aggregating demand at various inventory nodes, it is important to determine
whether or not a relationship exists between the forecast and the actual demand. The analysis
followed the methodology described in Rosenfield (1994 and 2014) and presented in Section 3.3.
In order to do perform this analysis, a year's worth of monthly forecast and demand data for all
of the SKUs (all of the product families) at one of the distribution centers was utilized. The
analysis was then repeated only for the 13 SKUs that compose the product family of interest. The
13 SKUs are a subset of the SKUs of the distribution center. The repetition of the analysis was to
determine whether or not the same relationship would hold for the smaller subset of items that
compose the use of one generic assembly.
The first step was to calculate the mean demand and mean forecast for each of the 12 months and
generate a time vs. units (both demand and forecast) graph to determine if a forecast bias was
present and get an initial sense of the data. This calculation was performed for each month, each
29
containing a different number of SKUs; the goal was to look at monthly variability. The second
step was to calculate mean demand, mean forecast, standard deviation of demand, and the root
mean square error (RMSE) for each of the SKUs, making sure that months in which both
forecast and demand were zero did not impact the calculations. The goal of this calculation was
to look at the performance of each individual SKU, not the performance by month as in the first
calculation. The months where there is no forecast or demand signal either a new product
introduction or a product removal from the market and therefore, only the months in which there
was either a forecast or demand influenced the calculations. In this case only the relationship
with demand was explored, assuming independence for the relationship with regards to time. The
third step was to take the log of each of the calculations in order to generate two plots: (1) log(D)
vs. log(standard deviation demand) and (2) log(D) vs. log(RMSE) and determine whether or not
a relationship existed within the data. Adding a linear regression to the plots then provided a
relationship of the form:
y
x
+ k
(3)
Where y is log(RMSE), P is the parameter of interest, x is log(D), and k is a constant. If the
correlation proves to be strong, the relationship can be converted to the following relationship in
equation (4),
,Jr- E ==viK
where K is
10 k,
L) Tk ()
T is time in months, and ca is 0.5 given the assumption of independence with
regards! to timeV.
4.3
Possibilities for Future States
4.3.1
Requirements
There are only two requirements for a proposed future state. These are:
(1) The target service level of each of the distribution centers must be 99% or above
(2) Overall supply chain costs must remain the same or decrease
The reason for the first requirement is that there are some distribution centers that at times are
able to reach that 99% service level, while for others there is significant variability in service
30
levels which are not near 99%. As some of the distribution centers can reach this target, it is
possible for all of the distribution centers to have this level of service as a target. This target then
ensures that stock outs are minimized. More of these devices on the market drive the sales of the
accessories which bring in the profits for Company B. The second requirement stems from the
fact that these devices tend to be sold below cost, increasing the cost of the supply chain further
increases the gap between the revenues from the device and the costs associated with the
materials, production, and distribution.
4.3.2
Scenarios
Several scenarios will be presented to understand the implications to the supply chain in terms of
inventory levels and costs utilizing the power model. The first is re-designing the supply chain to
show the effects of delaying product differentiation, assuming that all other factors are held
constant. This is done through the addition of a postponement center (in this case not in a DC),
and by modifying the lead times between assembly and customization as well as between
customization and the DCs. This moves the push-pull boundary from the customization step at
the manufacturing site to the customization step to the postponement center, which has a shorter
lead time to the distribution center. Figure 2 shows the postponement supply chain.
Raw Material Inventory
P1
LTPre-
WIPe
Finished Goods
Inventory
P2
LT2
LT
Customize
Pre-Assembly
Assemblies
F A
Finished Goods
DC
1
P3
RM1
LT3
AMAssembly
Customize
DC 2
Finished Goods
Assemblies
-
RM2
P4
RM3
LT4
Customize
Assemblies
RM4u
A
Finished Goods
DC 3
g
S Manufacturing Location
I
Transit
Postponement Centers ITransit [Distribution Centers
I
Figure 2. Network design for supply chain utilizing postponement.
31
.
. .............
. ::'-
Separate from re-designing the supply chain, there are several levers that can be pulled to affect
overall performance without modifying the network. These are mode of shipment (air vs. ocean),
forecast accuracy (improvement vs. worsening), and demand (increasing or decreasing). Each of
these will be studied for both the current design and for the postponement design to understand
the trade-offs. Scenario 1 will look at mode of transportation in which items can be shipped via
ocean or via air. This change affects lead time between the manufacturing center and either the
distribution center or the postponement center as well as cost of transportation per unit. Scenario
2 studies the effects of forecast accuracy. Although forecasts will always be inaccurate, changes
in the methodology can lead to either improvements or worsening of the accuracy of future
forecasts. Utilizing the same methodology described in Section 4.2 to find the relationship
between RMSE and demand, parameters K and P were determined for 10%, 25%, and 50%
improvements in forecast as well as declines of 10%, 25%, and 50%. The improvements and
decline in forecast accuracy were calculated by multiplying the actual forecast error by the
appropriate percentage and then either adding or subtracting this new error to the original
forecast to allow the forecast to get more or less accurate, as desired. Scenario 3 looks at changes
in demand that could occur because of growth or loss of market share (demand increase or
decrease). The size of the expected market can affect the decision-making process in terms of
whether or not to invest in changing the design of the supply chain). If a loss in market share is
expected in the intermediate time horizon, then why invest in modifying the supply chain. These
changes in demand were done by maintaining the base case parameters and either increasing or
decreasing the actual demand by 25%.
32
5
Supply Chain Model
This chapter reviews the creation of the supply chain model from the selection of software to the
equations utilized to the application of the power model.
5.1
Software Selection
One of the first questions when deciding to build a model revolved around software. The options
were to either use Excel, or to use process simulation software, such as ARENA® or ProModel.
After looking at both types of software the decision was made to use Excel although process
simulation software was available for use within the company. The reasons for the decision were
that (1) Excel is a familiar tool for the company, meaning that it would be easy to transfer the
model back to the company and have it be utilized, (2) a clear, simple model could be built in
Excel to answer the question at hand given the available data, (3) the variability in the process
could be captured through simulation using Crystal Ball in the future if desired (and data was
collected to determine the demand, process time, and lead time distributions), (4) longer learning
time to be able to use the process simulation software, and (4) not enough detail was available to
make full use of the process simulation software. Because of these decisions, the model currently
takes inputs as averages (average monthly demand, costs, lead times, and process times) and
therefore the output is an average inventory level or cost. However, it is important to note that
each input has a distribution of values (for instance, the lead time will rarely be the average lead
time) and therefore the output should also be a distribution of possible inventory levels or costs.
5.2
Model Assumptions
There were several assumptions that were made in the process of formulating the model. The
first is that the model for this supply chain begins with tier one suppliers shipping raw material to
the warehouse at the manufacturing facility and ends with finished goods inventories at
distribution centers. A model could go into more detail around the manufacturing of raw
materials at the first tier supplier or into second tier suppliers at the beginning of the supply chain
as well as into local distribution centers and retailers at the end of the supply chain; however, for
these purposes they are considered to be out of scope. Suppliers, local distribution centers, and
33
retailers are outside the immediate area of control and should be considered as future
opportunities for this work. As part of this assumption, no re-work occurs once the assemblies
are packaged as finished goods and each distribution center places its own orders to the
manufacturing facility. The unit is delivered to the distribution center that placed the order as a
finished good and remains in that exact same condition - the same condition that it will be
received in by the customer.
The second and third assumptions involve the customization of the assemblies. Instead of
looking at all of the product families within the category of products, the model looks at one
product family. A product family is defined by all of the SKUs proliferating from one assembly
- the only difference from one SKU to another being the customization that occurs after the unit
is assembled. This customization involves software languages and packaging. This assumption
then leads to the third one. As each assembly is customized with a specific language and
package, it is destined for a specific distribution center and shipped immediately upon the
completion of customization. Therefore it does not require building inventory post-customization
since the specific SKU cannot be sent to any other distribution center.
Finally, the last assumption is that the inputs for raw material lead times are dictated by service
contracts, not actual manufacturing processing time and transportation lead time. The contracts
dictate a 6 month lead time. The impact of this assumption is that overall lead times for a raw
material are incredibly long, with implications for holding a large amount of safety stocks. The
relationship between Company B and the suppliers is guided by the contracts, and therefore lead
times quoted by suppliers mimic the delivery time stated in the contract. In reality this may not
be the case. Depending on a variety of factors, such as capacity, utilization, and production
scheduling, lead times may be significantly shorter.
5.3
Inputs
The inputs were determined by data the company was expected to have as part of its operations
as well as requirements for the future. Data that was built into the model was areas for inputs of:
mean demand by SKU at each of the three distribution centers, root mean square errors for each
of these SKUs,
34
"
Monthly SKU level: demand, forecast
" Time: Processing time, lead time between processes, variability in these times
*
Costs: Transportation, ordering, holding, standard cost per unit, raw material costs
In designing for the future, the target service level, as well as differences in lead times between
the supply chain design options were inputs. The target service level was set at 99% given that
this was one of the stated requirements for the supply chain. The lead time between
customization and finished goods changes between the two different network designs and has an
impact with regards to delaying product differentiation.
5.4
Outputs
The outputs are the parts of the supply chain that can be changed by strategy and operations. For
this model, the outputs are separated into two main categories, inventory levels and costs. The
level of inventory required is calculated for each SKU at each distribution center, at the assembly
stage (regardless of whether it is at the postponement center or still at the manufacturing facility),
for the pre-assemblies, as well as for select raw materials at the manufacturing site warehouse.
Costs are calculated in terms of inventory safety stocks (by unit as well as holding costs),
transportation costs based on method of shipment, and total costs.
5.5
Model Design
The model was designed to allow for user flexibility in terms of data inputs and ease of
comparison between supply chain designs in terms of outputs. There are three spreadsheets. The
first is for the current supply chain design, the second for the alternate design with
postponement, and the third for summarizing the outputs.
The spreadsheet for the current design contains a data input section and is separated into three
sections; one section per distribution center of interest, allowing for each to have its own costs
and number of SKUs. The number of SKUs and average monthly demand for each SKU has to
be entered individually. The RMSE for each of these SKUs is then calculated using the
relationship in equation (4) with the parameters calculated in Section 4.2. The safety stock for
each of these SKUs is then calculated utilizing equations to be described in Section 5.6.1.1.
35
Based on the assumptions in Section 5.2, the demand per distribution center is summed to
determine the total demand of assemblies required. In a similar calculation described in Section
5.6.1.2, the RMSE of the aggregated demand of assemblies per distribution center is calculated
and the safety stocks are calculated. Finally the last section incorporates the pooling of demand
of assemblies for the three distribution centers to calculate the number of pre-assemblies required
as well as other raw materials. The calculations for this portion are described in Section 5.6.1.3.
Finally, the associated costs of each of these three sections (finished goods, assemblies, and raw
materials) are calculated as described in Section 5.6.2.
A second spreadsheet exists for the second supply chain designed being considered. This sheet is
essentially the same as the one for the current supply chain design, even copying over the inputs
that do not change between the designs. Although these shared values carry over, it is possible to
alter them if the user believes that the design change will lead to differences in the inputs.
The outputs, inventory levels at the various locations and costs, are then summarized in the third
spreadsheet where a comparison can be performed between the current supply chain design and
the design implementing postponement for the scenario being studied.
5.6
Equations
The following two sections describe the equations used to determine the levels of inventory
required at the various inventory nodes, as well as the cost calculations corresponding to the
nodes.
5.6.1
Inventory Calculations
To calculate safety stock (SS) at any of the inventory nodes, equation (5) was used (Simchi-Levi,
Kaminsky, & Simchi-Levi 2008). The differences between nodes were RMSE, lead time (LT),
and process time (PT). LT and PT can be determined based on the design of the supply chain as
this is information that is inherent in a supply chain design. RMSE was calculated in the manner
that will be described below for each of the nodes. The target service level for the supply chain is
set to 99%. In equation (5), z represents the service level.
SS = z(RMSE)NILT + PT
36
(5)
5.6.1.1
Finished Goods Inventory
The calculation RMSE of finished goods to input into equation (5) was calculated utilizing the
relationship in Section 4.2 and equation (6) from the power model described in Section 3.3 to
normalize for time given that the demand input is monthly demand. In this equation, RMSE
finished goods
is the root mean square error for each finished goods SKU, K is the constant
determined through the power law model, demand is an input from the planning organization,
LTfrom customization
is the lead time between the end of the customization unit operation and the
receipt of finished goods at the distribution center, PTustomization is the process time of
customization, a is 0.5, and
P is a parameter
determined through the power law model as well.
The sum of the lead and process times is divided by 30 days per month to normalize the time to
units of months.
RMSEfinished g
-
(6)
K(LTfrom customization + PTcustomization
30
Upon calculating the finished goods RMSE of each SKU at each distribution center, the safety
stocks for each SKU were calculated utilizing equation (5). The SKU level safety stocks were
then added up by distribution to calculate the level of finished goods inventory at the distribution
centers.
5.6.1.2
Assembly (Work-in-Process) Inventory
The RMSE of assemblies was calculated in the same way that RMSE was calculated for finished
goods except for one difference. The input of demand is the sum of all of the SKUs per
distribution center. From the assumptions in Section 5.2, all SKUs in one product family are
derived from the same assembly and each distribution center places its own orders for SKUs.
This allows for the demand to be added per distribution center to determine the total demand for
the single assembly design that creates the product family. Additionally, the relationship in
Section 4.2 scales with demand, producing equation (7). In this case the
LTfrom assembly
between the end of assembly and the beginning of the customization process and
is the time
PTassembly
is the
process time for the assembly operation. Again, this time is divided by 30 to normalize to the
monthly demand.
37
RMSEassembies
-
Ufrom assembly
PTassembly)a(sum SKU demand by DC)f
(7)
The SS of assemblies was then calculated for assemblies for the distribution center that placed
the order using
5.6.1.3
RMSEassemblies, LTfrom assembly,
and PTassembly as inputs into equation (5).
Raw Material Inventory
The RMSE of raw materials was calculated in a similar way, based on equation (8). The
manufacturing facility knows how many assemblies it is building and the number of units of
each raw component that go into one assembly. Therefore the demand of one raw material is the
total demand of assemblies from all of the distribution centers. As an example, to build one
assembly, five units of component x are required. The demand from distribution center 1 is 15,
25 from distribution center 2, and 10 from distribution center 3. The total demand of assemblies
then is 50. The LTfrom supplier is the time stated by the supplier contract, as this is the quoted lead
time from the suppliers. The PTreceiving is the time required to receive the raw materials into the
warehouse.
RMSEraw materials = K(fLTrom supplier + PTreceiving)'totalassembly demand)f
30
reevn)(
1
The SS of raw materials is then calculated using equation (9) with RMSEraw
materials, LTfrom
(8)
(8
supplier,
and PTreceiving as the inputs. As each assembly may require many parts of the same component,
the safety stock requirements are then multiplied by the number of components of that raw
material per assembly. More inventory will be required for raw materials that are used more per
assembly.
SS = Z(RMSEraw materials)(#components per assembly)/LTfrom supplier + PTreceiving
5.6.2
(9)
Cost Calculations
The model calculates total costs, as well as a breakdown of costs which include transportation,
and safety stock unit and holding costs.
38
5.6.2.1
Transportation Costs
To calculate the transportation cost, the average monthly demand was multiplied by the per unit
transportation cost as in equation (10). The unit transportation cost varies depending on the type
on inventory being shipped (finished good vs. assembly) as well as by method of transportation
(ocean v. air). In the case of shipping individual SKUs, the total transportation cost is the sum of
the transportation cost for each of the individual SKUs.
Transportationcost = average monthly demand * per unit transportationcost
5.6.2.2
(10)
Safety Stock Costs
The costs of safety stock are calculated by determining the actual cost of the inventory being
held and the cost associated with having that inventory in storage. The cost of the inventory is
calculated by multiplying the standard cost of a unit of inventory by the level of inventory being
held (see equation (11)). The cost of storing that inventory is then determined by multiplying that
cost of inventory by the holding cost, or opportunity cost (see equation (12)).
Cost of Inventory = Unit Standard Cost * Safety Stock Inventory
(11)
Inventory Holding Cost = Holding Cost * Cost of Inventory
(12)
5.6.2.3
Total Costs
The total cost is calculated as the sum of the transportation cost and the unit/holding cost of the
safety stock inventory. It is important to note that the cost of the postponement centers is not
taken into account. This is because several factors play into the costs of the postponement centers
that are unknown. These are, the cost of postponement implementation, the cost of daily
operations once the centers are up and running, as well as the cost savings from the reduced labor
force and footprint at the current manufacturing site. However, this analysis allows for the
determination of the maximum costs that the postponement centers can incur in order to lead to
cost savings.
39
5.7
Effects of Demand Aggregation
Utilizing the monthly demand and forecast data for all of the SKUs and method described in
Section 4.2, a relationship was found between the mean demand and the root mean square error.
At R2 ~ 0.65, the log-log relationship between the variables is significant. Figure 3 shows the
relationship.
*
:.
y
0.7466x
+0.5834
R ==0.6491
F
Log(Demand)
Figure 3. Relationship between root mean square error and mean demand for all SKUs.
To see if the relationship seen in Figure 3 for all of the products held for the SKUs of the same
product family, the analysis was repeated for the 13 SKUs in the product family. With this data
set, at R2
-
0.71, the log-log relationship between the variables is also significant. Figure 4 shows
the relationship for the 13 SKUs in the same product family.
40
k
H
-AN--
y =0.7829x + 0.6935R2 =0.7073
Log(Demand)
Figure 4. Relationship between root mean square error and mean demand for product family.
Because this log-log relationship is strong, both for all SKUs and for the SKUs in the product
family, the constant k, and exponent,
p selected
for the model were from the product family of
interest, as seen in Figure 4, were 0 is 0.7829 and k is 0.6935, making constant K equal to
100.6935 or 4.9374. This relationship can now be used when pooling the individual SKU demands
into aggregates to determine the demand for the assemblies as well as for the raw materials
utilizing the equation,
RMSE = 4.9374TO.5 DO. 78 2 9
(13)
where T is the lead time, D is the demand (either at the SKU level or an aggregate), and the
purpose is to calculate the corresponding RMSE.
41
-
-
I ..................
6
Analysis of the Supply Chain
The following section is a presentation of the results of the model, and possible future states of
the supply chain. In order to protect proprietary information, numbers in the analysis have been
modified; however, the functionality of the model remains the same. First, the base case will be
analyzed. Then future states of the supply chain looking at postponement, mode of
transportation, forecast accuracy, and changes in demand will be discussed.
6.1
Base Case
The base case is the model of the current supply chain seen in Figure 1. As a quick reminder, raw
materials are first received and pre-assemblies are put together before the assembly operation.
Assemblies are stored, as assembly inventory at the manufacturing location, until they are
ordered by the distribution center. At this point they are customized, shipped as finished goods
and held, as finished goods inventory at the distribution centers, until they are sold to the local
distribution centers. Figure 5 shows the levels of inventory at the different nodes of the supply
chain.
1.4E+07
1.2E+07
1.OE+07
8.0E+06
6.OE+06
4.0E+06
2.OE+06
O.OE+00
V-4
U
C14
0
Mn
r-4
U
CN
U
en
U
In
T
~
E
r4n
*1
2
E
FG
Assemblies
RMs
Figure 5. Inventory type and and levels across the current supply chain.
In the entire supply chain, the highest inventory requirements come from the raw materials. This
is due to the fact that the lead time for these raw materials is six months, as stated by the service
42
contracts. To maintain enough materials to cover 6 months of variability, coverage needs to be
high. All of the raw materials, except for the pre-assemblies, experience the same lead times. The
difference in inventory held between RM 1, RM2, RM3, and RM4 comes from the number of
units required per assembly. For simplicity in this model, three components of RM I are required,
two component of RM2, four components of RM3, and five components of RM4 for one
finished good. This corresponds to safety stock levels of 27,185 units of pre-assemblies, 7.4M
units of RM1, 4.9M units of RM2, 9.8M units of RM3, and 12.3M units of RM4; or 34.5M units
of inventory at a holding cost of $65.11 M which is a significant cost to the overall supply chain.
6.OE+05
5.OE+05
4.OE+05
S3.0E+05
g
2.OE+05
1.OE+05
0 OE+*-0
r-4
LU
L
r
U
W.
-4
LI
U
r
U
VN
-4
r4~
CO
q*
2
E
FG
Assemblies
RMs
Figure 6. Enlarged scale of Figure 5.
Given the disparity between levels of inventory of raw materials and the other categories, Figure
6 shows the same graph with an enlarged scale to be able to discern the differences in finished
goods and assembly inventories. The inventories of assemblies for the three distribution centers
and pre-assemblies are significantly lower than those of the finished goods at the distribution
centers because of the lead time preceding their corresponding unit operation. PT1 and LT1 are
each taken to be a single day; therefore the safety stock needs for these materials are low, and
vary slightly with the demand coming in from each distribution center.
43
For all of the future scenarios changes only occur after the manufacturing of assemblies,
therefore the analysis related to pre-assemblies and raw materials will remain the same as that
seen in the base case and will not be repeated or included in the total costs presented.
In looking at finished goods the distribution centers, DCl has 339,713 units, DC2 has 585,623
units and DC3 has 70,651 units. The monthly demand experienced is 30,081 units, 30,456 units,
and 13,359 units for DCl, DC2, and DC3, respectively. In this case, the shipment time between
customization and the distribution center is 30 days to DCl, 50 days to DC2, and 10 days to
DC3. From this it is easy to see how similar demand quantities (between DC 1 and DC2) lead to a
70% increase in units of safety stock. According to the model, to hold these safety stocks of
finished goods it costs DC1 $11.1M, DC2 $19.1M and DC3 $2.3M. Based on the model's
assumption, to transport the inventory needed to meet average monthly demand would then cost
(rounded to the nearest thousand) $12,600, $12,800, and $5,600 to each of the distribution
centers.
Similarly, the necessary safety stock levels for the assemblies are 20,175 units, 20,373 units, and
10,686 units for DCl, DC2, and DC3, respectively. According to the model, to hold these safety
stocks of assemblies for DCl, DC2, and DC3 it costs $0.48M, $0.49M, and 0.26M, respectively.
As assemblies remain within the manufacturing facility before customization, there is no
associated transportation cost.
The total cost of this current supply chain is $33.83M. This serves as the baseline with which to
compare the other possible options.
6.2
6.2.1
Scenarios for Future
Postponement
The goal of postponement is to delay the point of product differentiation for as long as possible.
To achieve this goal for this supply chain, it is proposed that the manufacturing site ship
assemblies to postponement centers that are geographically closer to the distribution centers. At
the postponement centers, the assemblies are customized and then shipped as finished goods to
their corresponding distribution centers. Depending on the capabilities of the distribution centers
44
it is possible that the postponement activities occur within the distribution center itself; however,
this is not a requirement for the model.
The model was run for the design seen in Figure 2 utilizing the same assumptions, demand,
RMSE, and cost inputs as the current design base case presented in Section 6.1. The major
difference between the two designs is the introduction of three postponement centers, each
having the capability of customizing the assemblies at different rates. For the purpose of this
analysis, it was assumed that the process time of customization for all of the postponement
centers was the same. This way it is the addition of the postponement centers (closer to the
distribution centers) that affects the levels of inventory in the supply chain and not the
productivity of the postponement center itself. Figure 7 compares the levels of inventory between
the current design and the postponement design of the supply chain.
7.OE+05
6.OE+05
5.OE+05
4.OE+05
3.0E+05
" Current
2.OE+05
" Postponement
1.OE+05
V.UE+ .nn
DC1
DC2
FG
DC3
DC1
DC2
DC3
Assemblies
Inventory Type and Location
DC1
DC2
DC3
Overall
Figure 7. Comparison of inventory levels between base case and postponement.
As expected, postponement decreases the inventory requirements for finished goods while
increasing the inventory requirements for the assemblies. This occurs because the lead time
between the customization step and the distribution center is decreased, allowing for the
distribution to hold 858,662 units less of finished goods stock. On the other hand, the lead time
between the assembly step at the manufacturing facility and the customization step at the
postponement centers has increased. Given this length of time, additional stock of 559,792
assemblies is required prior to customization to cover the longer lead time and to account for
45
variability coming from the forecast error. Upon adding the inventory of finished goods and
assemblies, postponement reduces the total inventory by 298,870 units. As before, the
differences observed between the distribution centers come from the lead times. Longer lead
times require more inventory for the same variability in demand as seen by Distribution Center 2.
Distribution Center 3 has the shortest lead time and therefore holds the least amount of
inventory.
There exists $5,200 in savings from transportation costs when switching to postponement, with a
one time savings opportunity while reducing the safety stocks to the new levels. The
transportation cost in this scenario is split between (1) the shipping of assemblies from the
manufacturing facility to the postponement center (this is a cost that did not previously exist as
there is no significant cost associated between moving assemblies from end of assembly line to
beginning of customization line) and (2) the transport of finished goods from the postponement
center to the distribution center. The savings then come from a decrease of $16,300 in the
shipment of finished goods, and an increase of $11,000 in shipping assemblies. The decrease is
due to an increased packing density of assemblies onto the pallets, as the packaging required for
finished goods will not be needed for the assemblies, which reduces the cost of shipping per unit.
3.5E+04
3.OE+04
2.5E+04
C 2.OE+04
0
N
1.5E+04
Current
MPostponement
r 1.OE+04
5.OE+03
O.OE+00
Assemblies
FG
Figure 8. Comparison of transportation costs between the current supply chain and the postponement
design.
46
The differences in the safety stock requirements for the two different designs seen in Figure 7
correspond to differences in costs of holding these inventories. These cost differences are seen in
Figure 9. The reduced finished goods inventory corresponds to a decrease of $29.18M, while the
additional inventory of assemblies leads to an increase of $15.42M. The overall savings in
holding inventory is then $13.76M if the postponement design were to be implemented.
3.5E+07
3.OE+07
2.5E+07
'A
0
U
2.OE+07
M Current
1.5E+07
*
Postponement
1.OE+07
5.OE+06
O.OE+00
FG
Assemblies
Figure 9. Comparison of safety stock costs between the current supply chain and the postponement design.
To operate the supply chain under postponement, the overall savings are $13.76M, not including
the cost of the implementation or operation of the postponement centers. If the implementation
of these postponement centers is less costly than the overall savings, then postponement can be
implemented to meet both of the requirements for the future supply chain of a 99% service level
and cost reduction; however, if the cost of is expected to be greater than the savings, then
implementing postponement will not meet the cost requirements.
Performing a quick sensitivity analysis shows the trade-off between a 98% and a 99% service
level. If the current supply chain were to perform at a 98% service level there would be a 12%
decrease in raw material inventory, 12% decrease in finished goods inventory as well as a 12%
decrease in assembly inventory. This 12% decrease then translates into 12% additional decreases
when looking at the benefits of postponement as well. For a 1% drop in service level, there exist
12% less units of inventory within the supply chain.
47
6.2.2
Mode of Transportation
To see the effects of changing mode of transportation from ocean to air, a few changes were
made to the initial assumptions. For the current supply chain lead times between customization
of assemblies and the distribution centers were decreased to 10 days, 7 days, and 5 days for DC 1,
DC2, and DC3, respectively to mimic the decreased lead time during air shipment as opposed to
ocean shipment. Transportation costs increased from $0.40 to $3 per unit. For the postponement
supply chain, the lead times between the manufacturing facility and the postponement centers
were decreased to the same number of days as above; however, transportation costs were
increased to $2.50 per unit (instead of $3 per unit) with the assumption that assemblies could be
packaged more densely than finished goods. Ground shipping from the postponement center to
the distribution center remains unchanged at $0.20 per unit. Figure 10 shows the expected
changes in inventory from shipping via air instead of ocean.
7.0E+05
6.OE+05
4.0E+05
4
I
I
S5.OE+05
* Ocean
3.OE+05
Lm
ii
2.OE+05
1.OE+05
DCI
DC2
FG
DC3
DC1
DC2
I DC3
Assemblies
DC1
DC2
DCI
DC3
DC2
FG
Overall
DC3
DC1
DC2
Assemblies
DC3
DC1
DC2
Air
DC3
Overall
Figure 10. Effects on inventory levels by changing the mode of transportation from ocean to air. The graph
on the left shows what the levels of inventory would be for the current supply chain. The graph on the right
shows what the levels of inventory would be for the postponement supply chain.
The reduced lead time from shipping via air leads to significant changes in inventories held in
both supply chain designs. The left graph in Figure 10 shows the inventory levels for the current
supply chain design. The reduced lead time of shipping finished goods leads to a drop of 719,503
units (72%) when units shipped via air. There is no change in the inventory of assemblies to be
shipped to any of the distribution centers as the shipment of inventory occurs after the
customization step, once the assemblies are converted to finished goods. Similarly, right graph in
Figure 10 shows the changes in inventory for the postponement design supply chain. There is no
48
change in the finished goods inventory as the method of shipping from the postponement centers
to the distribution center remains unchanged - standard ground shipping, as these centers are
geographically close to each other. The change in lead time is between assembly and
customization for the postponement supply chain corresponding to a drop in assembly inventory
levels of 444,276 units (73%) when units are shipped via air.
To compare the inventory differences in the mode of shipping between designs the changes in
inventory levels between the current design and the postponement design are calculated for each
mode of transportation. Figure 11 shows the comparison of the inventory levels. The benefits of
postponement, in terms of levels inventory held, are greater when shipping via ocean. The
overall inventory savings when shipping via ocean in a postponement design are 298,870 units
less than in the current design. When the strategy is to air ship in both designs, the benefits of
postponement decrease to 23,643 units. The difference is due to the decreased lead time. As the
lead time for ocean shipping is so much larger, the benefits obtained through postponement for
safety stock levels increase.
8.OE+05
6.01E+05
2 4.01E+05
2.OE+05
-
0.0E+00
MOcean
MAir
-
C
-2.OE+05
DC1I DC2I DC3
FG
DC1 IDC2 IDC3
Assemblies
DC1
DC2
DC3
Overall
Figure 11. Difference in inventory between current supply chain design and postponement supply chain
design.
The tradeoff in these scenarios is on cost - air shipment is more costly than ocean shipping;
however, the faster transit times lead to implications in inventory reductions observed in Figure
10. Figure 12 illustrates these increases in transportation costs. In the current design, to fly
49
finished goods to the distribution center would correspond to an increase of $191,000 in
transportation costs while to fly assemblies in the postponement design, transportation costs
would increase $174,000.
2.5E+05
I-
2.0E+05
1.5E+05
" Ocean
1.OE+05
" Air
5.0E+04
E
-
0.OE+00
FG
FG
Assemblies
Assemblies
Postponement
Current
Figure 12. Comparison of transportation costs between ocean and air shipments.
The reduction in lead time when transporting inventory via air leads to less inventory and costs
as part of holding safety stock. These lower safety stock costs are $23.53M in finished goods for
the current supply chain, or $12.11 M in assemblies in the postponement supply chain. Figure 13
-
2.5E+07
-
3.0E+07
2.0E+07
-
0
I-I
3.5E+07
-
shows the costs of holding the safety stocks for both supply chain designs.
I
U
-
" Air
-
1.0E+07
* Ocean
5.0E+06
-
I
1.5E+07
0.0E+00
I
-
0
*'
FG
Assemblies
FG
Assemblies
Postponement
Current
50
Figure 13. Comparison of safety stock costs between ocean and air shipments.
In terms of total costs, for the current design ocean shipping is $23.34M more costly; while it is
$11.93M more costly for the postponement design. This implies that in both situations air
shipment is preferred on a financial basis, providing greater benefits for the current supply chain
than the postponement supply chain. This is because cost of holding inventory exceeds the cost
of transportation, as the reduction in lead time when shipping via air allows for a significant
decrease in safety stock levels. The decrease in safety stock levels is greater than the increase in
transportation costs.
6.2.3
Forecast Accuracy
The parameters for the power model were calculated as inputs to mimic varying levels of
forecast accuracy as described in Section 4.3.2. The parameters for the various levels of forecast
accuracy as well as the resulting RMSE are listed Table 1. The difference between forecast
accuracy RMSE and power model RMSE is that the forecast accuracy RMSE validates the
increase or decrease in forecast accuracy through changing the forecast itself and comparing that
error to actual demand. The power model RMSE comes from the log(RMSE) vs. log(demand)
regression in equation (4). The parameters extracted from the regression are
constant multiplier in the power model is K which is
10k
P and k. The
and this value scales with the increase
.
and decrease of forecast accuracy. The fit of the regression is captured in R2
Table 1. Parameters used in model to simulate increases and decreases in power model estimated RMSE.
Forecast
Accuracy
10%
25%
Increase
50%
Base Case
10%
25%
Decrease
50%
Forecast
Accuracy
RMSE
1971
1642
1095
2190
2409
2737
3285
B
k
0.7831
0.7830
0.7831
0.7829
0.7828
0.7830
0.7830
51
0.6471
0.5685
0.3919
0.6935
0.7352
0.7900
0.8694
K
4.4371
3.7025
2.4655
4.9374
5.4350
6.1660
7.4029
R
0.7074
0.7073
0.7073
0.7073
0.7073
0.7073
0.7073
Power
Model
RMSE
1734
1446
964
1927
2118
2408
2892
The parameters exhibit some notable trends. As expected as forecast accuracy increases, RMSE
decreases demonstrating that the forecasts are in fact getting closer to the actual demand.
Similarly, as forecast accuracy decreases, RMSE increases. Based on R2 , the relationship
between demand and RMSE remained exactly the same. The P also remained constant while k
varied as a function of forecast accuracy; increased accuracy leading to a lower value of k.
6.2.3.1
Increase in Forecast Accuracy
An increase in forecast accuracy directly leads to a reduction of safety stock required for both
supply chain designs. As the accuracy of the forecast increases, so does the reduction in safety
stock. As an example, we will look at a 50% improvement in forecast accuracy. In current supply
chain, finished goods is reduced by 497,697 units (50%), assemblies reduced by 25,594 units
(50%), leading to an overall reduction of inventory of 523,291 units (50%). In the postponement
supply chain, finished goods is reduced by 68,566 units (50%), assemblies reduced by 305,271
units (50%), leading to an overall reduction of inventory of 373,837 units (50%). The number of
units decreased is larger for the current supply chain; however in both designs inventory levels
were improved by 50%.
1.OE+06
9.0E+05
8.0E+05
E 7.OE+05
0 Base Case
M 6.0E+05
5.E+05C 4.0t+05
0 10% increase FA
a 25% Increase FA
IL
E3.OE+O5
2.OE+05
0 50% Increase FA
1.OE+05
DC1
DC2
FG
DC3
DC1I DC2 IDC3
DC1 IDC2I DC3
Assemblies
Overall
DC1
DC2
FG
DC3
DC1
DC2
DC3
DC1
Assemblies
DC2
DC3
Overall
Figure 14. Effects on inventory levels of a increase in forecast accuracy. The graph on the left shows what the
levels of inventory would be for the current supply chain design. The graph on the right shows what the levels
of inventory would be for the postponement supply chain.
In terms of costs, there is no change in transportation costs from those presented in Section 6.2.1
due to changes in forecast accuracy. In this model transportation costs are derived from demand,
and are therefore not affected by variability in the forecast. What the improvement does do is a
52
one-time decrease of the safety stocks, which allows for a temporary reduction of shipments.
Following the example above with a forecast accuracy increase of 50%, Figure 15 shows how
safety stock costs change as a function of increasing forecast accuracy. For the current supply
chain, finished goods costs decrease $16.27M and costs of assemblies decrease $0.6 1M. For the
postponement supply chain, finished goods costs decrease $1.69M and costs of assemblies
decrease $8.32M.
3.5E+07
3.OE+07-
2.5E+07
1W
n Base Case
U 2.0E+07
N 10% Increase FA
1.5E+07
25% Increase FA
1.0E1+07 -K
50% Increase FA
5.OE+06 0.0E+00
FG
lAssemblies
Current
FG
lAssemblies
Postponement
Figure 15. Changes in safety stock costs as a function of increasing forecast accuracy.
In looking at overall supply chain costs, the difference between the base case and a 50%
improvement in forecast accuracy is a decrease $16.89M for the current design and a decrease of
$10.01 M for the postponement design. If the current design is maintained, then there would be
greater benefits to improving the forecasting method if at all possible.
6.2.3.2
Decrease in Forecast Accuracy
The model was run to look at changes in inventory levels for decreasing forecast accuracy.
Figure 16 shows how decreasing forecast accuracy increases the levels on inventory for both the
current supply chain and the postponement supply chain. Again, we will take the 50%
improvement in forecast accuracy as an example. In the current supply chain, finished goods
inventory is increased by 498,476 units (50%), assemblies inventory is increased by 25,659 units
53
(50%), leading to an overall increase of inventory of 524,135 units (50%). In the postponement
supply chain, finished goods is increased by 68,644 units (50%), assemblies increased by
306,060 units (50%), leading to an overall increase of inventory of 374,704 units (50%).
1.OE+06
9.OE+05
E
5
8.OE+05
7.OE+05
Base Case
6.OE+05
0
5.OE+05
M 10% Decrease FA
N 25% Decrease FA
4.0E+05
2.OE+05
LOF -'05
a 50% Decrease FA
Wd
DC1I DC2 IDC3
FG
DC1I DC2I DC3
Assemblies
DC1I DC2
DC3
Overall
DC1
DC2
FG
I
DC3
DC1
DC2
DC3
DC1
Assemblies
DC2
DC3
Overall
Figure 16. Effects on inventory levels of a decrease in forecast accuracy. The graph on the left shows what the
levels of inventory would be for the current supply chain design. The graph on the right shows what the levels
of inventory would be for the postponement supply chain.
Just as there are no changes in transportation costs in the scenario looking at an increase in
forecast accuracy in Section 6.2.3.1, there are no changes in transportation costs, when looking at
a decrease in forecast accuracy. Instead of a one-time cost savings, however, there is an initial
cost increase to transporting the additional inventory required as safety stock.
In looking at the additional safety stocks required and following the example above with the
decrease in forecast accuracy of 50%, Figure 16 shows how safety stock costs change as a
function of decreasing forecast accuracy. The current supply chain shows an increase in finished
goods costs of $16.30M and an increase if costs of assemblies of $0.62M; while the
postponement supply chain showed an increase of finished goods costs of $1.69M and an
increase in costs of assemblies of $8.34M.
54
6.OE+07
5.0E+07
4.OE+07
U
U Base Case
3.0E+07
10% Decrease FA
2.0E+07 -
25% Decrease FA
1.OE+07 -
*50% Decrease FA
O.OE+00
FG
Assernbfies
Current
FG
lAssemblies
Postponement
Figure 17. Changes in safety stock costs as a function of decreasing forecast accuracy.
In looking at overall supply chain costs, the difference between the base case and a 50% decrease
in forecast accuracy is an increase of $16.92M for the current design and an increase of $10.03M
for the postponement design. This is the opposite of what was observed with increasing forecast
accuracy. As the model drives costs from inventory levels, and inventory increased/decreased by
similar amounts with changing forecast accuracy then this result was expected.
6.2.3.3
Summary
Forecast accuracy has an effect on the safety stocks at the finished goods and assembly level of
the supply chain. Figure 18 shows the change in inventory units and total costs between the
different forecast accuracy scenarios presented in Section 6.2.3.1 and Section 6.2.3.2. In all
cases, the postponement supply chain has lower inventory units and total costs as compared to
the current supply chain design. Additionally, better forecasts favor maintaining the current
supply chain design as fewer benefits are achieved through postponement (smaller difference in
costs and inventory units). In improving forecasts, total costs decrease for both designs, as does
inventory. As expected worsening forecast accuracy favors moving towards a postponement
design due to the greater benefits (greater difference in costs and inventory units) as costs and
inventory units increase.
55
p
0.37M
units
$10.05M
50%
C
0.52M units
$16.94M
p
0.56M units
$15.07M
C
0.79M
p
0.67M units
$18.07M
C
0.94M units
$30.45M
p
0.75M units
10%
units
$25.40M
$20.06M
Current Stnle
Nn
Chrne in Fnrercnt Arrlracv
C
1.05M units
$33.83M
P
0.82M units
$22.06M
10%
1. 1._IVI UILUSN
7
$37.21M
P
$25.07M
25%
C
1.31M units
$42.27M
p
1.12M units
$30.1OM
50%
Note:
P= Postponement Design
C= Current Design
0.94M units
C
1.57M units
$50-74M
Figure 18. Diagram showing the total inventory units and costs of each of the supply chain options.
56
6.2.4
Changes in Demand
Over time, it is possible to experience growth or decline in market demand. Growth can be due
to an increase in the customer base through either an increase in market share or an overall
increase in the size of the market. Decline is due to a decrease in market share through the
increase of competition or through a change in the competitive landscape which renders the
product obsolete. It is therefore important to see if a decision might be affected by conditions
that cannot be controlled as well as the magnitude of that impact.
This section looks at the effects of a change in demand of both a 25% increase and a 25%
decrease for both supply chain designs. Figure 19 shows what the inventory levels are expected
to be based on these new demand assumptions. When demand increases, there is an increase in
safety stocks of finished goods of 190,319 units (19%), increase in assemblies of 9,796 units
(1%), for an overall increase of 200,115 units (20%) in the current design. Similarly, the
postponement design exhibits an increase in finished goods of 26,215 units (3%), increase in
assemblies of 116,761 units (11%), for an overall increase of 142,976 units (14%). As expected
for that same decrease in demand, there is a decrease in finished goods of 200,556 units (20%),
decrease in assemblies of 10,318 units (1%), for an overall decrease of 210,874 units (21%) in
the current supply chain; and a decrease in finished goods of 27,627 units (3%), decrease in
assemblies of 123,106 units (12%), for an overall decrease of 150,773 units (15%) for the
postponement supply chain.
8.OE+05
7.OE+05
6.0E+05
5.0E+05
4.OE+05
n Base
3.0E+05 2.OE+05
a Decrease
increase
1.0E+05
o.OE+00
DC1
DC2
FG
DC3
DC1
DC2
DC3
Assemblies
DC1
DC2
DC3
DC1
DC2 DC3IDC1IDC2IDC3
FG
Overall
Assemblies
DC1
DC2
DC3
Overall
Figure 19. Effects on inventory levels by changing the demand. The graph on the left shows what the levels of
inventory would be for the current supply chain. The graph on the left shows what the levels of inventory
would be for the postponement supply chain.
57
As expected, based on the inventory level, if a postponement strategy is implemented there will
be a more tangible benefit in terms of inventory savings if demand increases than if demand
decreases. An increase in demand requires safety stock inventory increases, just as a decrease in
demand requires a decrease in safety stock inventory. Figure 20 compares the difference in
inventory levels between the current design and the postponement design. From the figure it can
be seen that for these lead times, postponement is still the favored design to acquire benefits
through reductions in inventory levels. A positive change in inventory favors the postponement
supply chain as that design requires fewer inventories than the current one. On the other hand, a
negative change in inventory units favors the current supply chain as the postponement design
requires more inventories. If demand continues to drop, then the benefits of postponement will
continue to drop, until there is a net zero change in overall inventory similar to that seen for
DC3.
8.OE+05
.
C
6.OE+05
4.OE+05
C
2.OE+05
OE
.OE+OO
.
M Base
Increase
-N
C-2.E+05
N Decrease
-4.OE+05
-6.OE+05
DC 1 DC2
FG
1
DC3 DC1I DC2 1 DC3
DC1I DC2 1 DC3
Assemblies
Overall
Figure 20. Graph shows the changes in inventory between current design and postponement design.
Costs follow the same pattern observed for safety stock levels. A higher demand corresponds to
higher supply chain costs. Figure 21 shows the transportation costs for both supply chain
designs. For the current design, an increase in demand corresponds to an increase of $7,767 in
transportation of finished goods while a decrease in demand corresponds to a decrease of $7,752.
As in all other scenarios, there is no shipment of assemblies so there is no associated
transportation cost. In the postponement design, an increase in demand leads to a $3,699 increase
58
in shipping of finished goods, and $2,774 increase in shipping of assemblies; a decrease in
demand leads to a $3,691 decrease in shipping of finished goods and $2,769 decrease in shipping
of assemblies.
4.5E+04
4.OE+04
FA
3.5E+04
3.OE+04
c0 2.5E +04
2.OE+04
* Base
MIncrease
1.5E+04
C
i Decrease
1.OE+04
5.OE+03
O.OE+00
FG
Assemblies
FG
Current
Assemblies
Postponement
Figure 21. Comparison of transportation costs for increases and decreases in demand.
As expected from the changes in inventory, an increase in demand corresponds to an increase in
the costs associated with safety stocks. Figure 22 shows the safety stock costs for both supply
chain designs. For the current design there is an increase in $6.22M of finished goods and
increase of $0.23M of assemblies. In the postponement design, there is a smaller increase of
$0.65M in finished goods, but a larger increase of $3.18M of assemblies. Also, as expected a
decrease in demand corresponds to a decrease in safety stock costs. For the current supply chain
these were a decrease of $6.56M and $0.25M of finished goods and assemblies, respectively;
while for the postponement design these decreasing costs are $0.68M of finished goods and
$3.35M of assemblies.
59
4.5E+07
4.OE+07
3.5E+07
0
3.OE+07
E7I.
2.5E+07
a Base
a' 2.0E+07
N Increase
1.5E+07
MDecrease
1.OE+07
5.OE+06
0.OE+00
FG
Assemblies
FG
I Assemblies
Postponement
Current
Figure 22. Comparison of costs associated with safety stock for increases and decreases in
emand.
The total costs for 25% increase in demand increase by $6.47M for current design and $3.83M
for postponement design; while for the same decrease in demand, the costs decrease $6.81M for
the current design and $4.04M for postponement design as compared to the base case.
It is important to note that this analysis was performed assuming steady state operations
disregarding the costs of the postponement centers and the costs/savings in transportation to
either build up or decrease the safety stock levels for the changes in demand. Again, having an
understanding of the cost of setting up and running a postponement center needs to be considered
before making the decision to take that path, as the cost of that will likely overshadow the one
time changes in transportation costs. Understanding these costs, of starting up and running
postponement centers, is one of the suggestions for future work in Section 7.2 for these studies.
60
7
Recommendations and Conclusions
This last chapter will summarize the findings of this research as well as propose directions for
future work.
7.1
Conclusions
This thesis was able to analyze the current supply chain and generate options with the goals of
providing a target service level of 99% at the distribution centers and maintaining or decreasing
costs. To achieve these goals the idea of supply chain agility was explored with a focus on
postponement to meet varying customer requirements. Demand and forecast data was analyzed,
and a model was built on the basis of a power law model that studied the effects of aggregating
demand. Scenarios were generated to determine the effects of mode of transportation, degree of
forecast accuracy, and changes in market size on both supply chain designs. Understanding the
effects of choosing to re-design a supply chain with factors out of Company B's direct control (to
some degree), such as forecast accuracy and market size, is important to making the decision
with which to move forward. Figure 23 presents the total inventory levels, and Figure 24
presents the total supply chain costs for all of the scenarios presented.
1.
1.
8
6
4
I
1
U
U
current
Postponement
U-
0
0.8
0 .6
4.
0 .4
a, 0 .2
0.
.0
0
9,e
t
~
'0
q t~
4~
'0
Scenario
Figure 23. Summary of inventory levels for all scenarios presented.
61
G
60
VA
" Current
50
" Postponement
'S4 0
30
20
0
0
~
,~
$
c
G~c.
0<-
4!
ez;
e~
\A
eve
,;;a
'z,
0
'
,e;
Scenario
Figure 24. Summary of total costs for all scenarios presented.
From Figure 23 and Figure 24 it is clear that the model showed that postponement leads to lower
levels of inventory and lower costs for all scenarios. The option of moving from ocean shipping
to air shipping led to significantly reduced inventory levels (as expected due to the large decrease
in lead time between the manufacturing site and distribution center), and reduced costs. The
degree of cost reduction was not as expected. Transportation costs for air shipment were higher
than for ocean transport, however, the increase in transportation costs was covered by the
decrease in inventory holding costs; leading to overall decrease in supply chain costs. Increasing
forecast accuracy led to lower levels of inventory as well as lower costs, while decreasing
forecast accuracy led to increasing inventory levels and rising costs. Finally, if market size
increases, inventory levels will have to increase to meet that demand (costs increase as well)
while a decrease in market size requires inventory levels to shrink and costs to decrease.
As supply chain costs increase, the advantages of re-designing the supply chain increase. This is
because of the increasing difference between the costs of the two designs. As that difference
increases, the benefits of postponement become greater and therefore an investment should be
made to implement postponement. The cost of implementing and then operating the
postponement centers should not be greater than the savings from implementation as then those
62
savings will not be realized. The cost of this implementation was not discussed in this thesis and
is listed as part of the future work recommendations in Section 7.2.
7.2
Recommendations for Future Work
The recommendations for future work are to delve deeper into (1) supply chain between
distribution center and end user, (2) the operations of the distribution centers, (3) the costs of
postponement centers whether they are individual entities or part of the distribution center, (4)
the terms of the service contracts with raw material suppliers, and (5) the process by which large
orders are placed and how these orders move through the supply chain.
(1) At the moment, the bulk of understanding of the supply chain is between the receipt of raw
materials and the storage of finished goods at the distribution center. However, local
distribution centers exist and the retail customers receive their products at a retailer - not
directly from the local distribution center. This means that there are additional processes
between the distribution center and the customer which may affect the customer's experience
and the availability of the device at a given time. Improving the operations of the distribution
centers to operate at a 99% service level does not imply that the customer will experience
that same level of service due to the existence of downstream steps that may not yet be
understood. Therefore, expanding the scope of the project in this thesis to include these
additional steps will help ensure that there are less/no stock outs and that the customers have
a positive experience. Additionally, the cross-functional nature of this process will allow the
various functions (and business partners if some of these functions are external to Company
B) to discover assumptions and processes that are inhibiting optimal performance.
(2) Although the various distribution centers have the same target service level, they all perform
differently. Is the difference in performance due to the variance in customer demand in each
of the regions or to operational business practices at each of the distribution centers?
Building an understanding of how each of the distribution centers operate and how they each
respond to customer demand can help define best practices. Furthermore, understanding the
causes to poor performance can lead to ways to improve processes. Another aspect to
consider with regards to the distribution centers is developing the capability for them to
perform the customization step which includes holding inventory of assemblies. The
63
advantage of this would be to further reduce the lead time between customization and
fulfilling the order to the customer which would reduce finished goods inventory and move
the push-pull boundary closer to the customer decreasing the reliance on forecasts.
(3) This work focused on the impact of the implementation of postponement on inventory and
finances without considering the costs of start-up and operations. To make the decision of
whether or not postponement should be implemented, it is necessary to analyze the decision
with knowledge of these costs. As part of this process to develop postponement centers, it is
also recommended that ways to simplify customization be brainstormed. This may lead to
higher up-front engineering and development costs but lead to potential long-term benefits.
(4) Contracts with raw material suppliers list standard delivery times of six months. On the side
of the supplier this is done to level out demand to streamline their manufacturing operations;
however, for Company B this means that any change to their products requires at least a six
month lead time to change. Company B should consider creating mutually beneficial
relationships with suppliers that are built on understanding (and possibly integrating)
processes. The supplier will then have insight into the actual demand of the product in order
to plan production while the actual lead time for Company B to receive materials can be
reduced to the actual manufacturing lead time instead of an arbitrary 6 month timeframe.
This affects the stocks of raw material inventory held at Company B's manufacturing site as
well as how quickly Company B can introduce new products to the market or implement a
change on a currently marketed product.
(5) Finally, understanding the demand chain (the process by which orders are placed, how
forecasts are created, and the contracts by which large orders are placed) can provide a way
to control or level out the demand experienced by the manufacturing site. It is possible that
communication between functions can be improved and that lead time requirements for the
large entity customers be set on a case by case basis to allow for a constant and predictable
utilization at the manufacturing site. Part of this task involves understanding the forecasting
methodology and looking at what causes the variability in demand. For instance: Are the
orders placed by retailers fairly constant and predictable? Is there sufficient lead time to
fulfill large orders without causing a drop in service level? How much visibility is there at the
manufacturing site into actual demand of the product (not the forecast)?
64
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