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Summary Akkermans - Supply chain dynamics chapter 4 & 9
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The differences in governance adjustments drove the different project outcomes, showcasing thateven with an identical model, changing parameters determining governance adjustments can lead to
dramatically different project outcomes.
Two history-friendly simulations are presented in Figure 2, with blue lines representing adjustments
to contractual governance and red lines representing adjustments to relational governance, showing
how project performance differs under each type of adjustment.
Conclusion
The main contribution is the development of a dynamic theory focusing on large public-private
projects and how governance adjustments play a crucial role when setbacks occur.
Setbacks triggered by events within the public-private collaborations are almost inevitable but can
be effectively addressed through adjustments to both contractual and relational governance.
Interplay of Governance Adjustments:
The dynamic theory sheds light on the types of governance adjustments made in response to project
setbacks and their interplay.
It highlights unintended side effects where governance adjustments that help address the setback
may inadvertently worsen the situation through another loop.
Critical Balancing of Governance Adjustments:
The careful balancing of governance adjustments is emphasized as critical to effectively addressing
project setbacks and achieving desired project outcomes.
Longitudinal case research and simulation modeling were used to move beyond theory generation,
perform robustness checks, and identify boundary conditions for a dynamic perspective on
governance adjustments.
(2014) Akkermans – Supply chain dynamics
chapter 4 – The curse of cyclicality
Case introduction
The VP of Supply Chain Management at Philips Semiconductors is experiencing a great year due to
high demand for products. Despite knowing the cyclical nature of the industry, the current situation
seems different with the rise of broadband Internet, mobile telephony, and digital storage media.
However, a request from a production planner to cancel capacity slots due to sufficient work in
progress stock raises concerns about the future direction of demand, especially with the book-to-bill
ratio dipping below 1.0. The VP is faced with the dilemma of whether to freeze capacity investments
based on these signals or risk excess inventory and costly capacity in case of demand drops.
§4.1 Introduction: A thoroughly cyclical industry
The text discusses the issue of semiconductor supply chains being late in responding to market
demand changes. It highlights how capacity investments in new factories often start at market
peaks, leading to excess inventory after demand downturns. The research builds on previous studies,
including one by Joan van Aken in the 1970s. It also mentions Jan Jaap Bezemer's work in 2002,
analyzing the semiconductor industry's delayed response during the dot-com bubble. The text
emphasizes the cyclical nature of the industry and the challenges faced by multiple generations of
researchers studying this phenomenon.
§4.2 The problem: Why are we always late?
, Jan Jaap's thesis work provided a four-year time series of key performance indicators of PSC's supply
chain from 1999 to Q4 2002. The historical sales rate at PSC showed a significant increase, peaking at
+125% on a weekly basis. Despite the subsequent decline, sales remained around +40% higher than
the beginning of 1999. This impacted delivery performance, with RLIP and CLIP indicators showing a
decline in 2000 due to the company's inability to meet market demand. However, the recovery of
CLIP and RLIP was swift compared to other key performance indicators like inventory, which took
longer to return to acceptable levels.
The semiconductor supply chain is late in responding to market demand changes due to
capacity investments starting at the peak of the market, leading to excess production
capacity after demand decreases.
Additionally, there is a delay in reducing utilization of existing capacity when the market
demand drops, resulting in excess inventories for up to two years after the downturn.
The supply chain's inherent instability and locally rational management policies contribute to
the persistent oscillatory behavior and late responses to market demand fluctuations.
RLIP = Requested line item performance
CLIP = Confirmed line item performance
§4.3 A model of an IC manufacturing supply chain
In this section, the focus is on developing a dynamic theory of the Philips Semiconductors supply
chain. This involves translating the theory into equations, adding numerical values to the
parameters, and creating a simulation model to replicate historical behaviors. The supply chain at
PSC is broken down into three main flows: orders, products, and capacity management. Each of
these flows plays a crucial role in understanding and managing the supply chain dynamics effectively.
4.3.1 The flow of orders
Explanation of the Flow of Orders Process:
The flow of orders at PSC involves entering customer orders into the IT system, which then move
into backlog until products are shipped. Once products are shipped, the orders are fulfilled,
depleting the backlog accordingly. An administrative limit called Min fulfillment delay is set at 1.0
week to control the outflow from the order backlog. The equation for order fulfillment rate
considers the backlog and shipments to determine the rate.
Key Performance Indicator - Delivery Performance:
One key performance indicator derived from the flow rate of orders is delivery performance. This
indicator is crucial for monitoring the efficiency of the order fulfillment process. It serves as a proxy
for various common indicators related to order backlog, new orders, fulfillment rate, delivery delay,
workload, cumulative sales, historical sales rate, company target delivery delay, max workload, SW
Oscillation test, and order handling.
4.3.2 The flow of products
Flow of Products in PSC Supply Chain:
The flow of products in the PSC supply chain is depicted in Figure 8 of the document. It starts with
the production starts, also known as wafer starts in PSC terminology. The amount of production
started is determined by the minimum of desired production starts and the available capacity. Once
production starts, the products become work in progress (WIP) with a production cycle time of 10-12
weeks. The production output is calculated as the WIP divided by the production cycle time.
Inventory and Capacity Utilization KPIs:
Two key performance indicators (KPIs) are specified based on the flow of products: inventory and
capacity utilization. Inventory is calculated by combining the value of WIP with finished products. On
the other hand, capacity utilization ratio is determined by multiplying the production output rate by
100 and dividing it by the available capacity. These KPIs provide insights into the efficiency and
performance of the supply chain at PSC.
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