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Logistics and Supply chain Risk Management 46 Case study Telefon AB LM Ericsson On 17 March 2000 there were thunderstorms in New Mexico, and lighting hit an electronic power line This caused a surge i[.]

Case study: Telefon AB LM Ericsson

On 17 March 2000 there were thunderstorms in New Mexico, and lighting hit an electronic power line This caused a surge in power, which started a small fire in Philips chip-making factory in Albuquerque The automatic spinkler system put this out within 10 minutes, and fire damage to the building was slight Unfortunately, thousands of chips that were being processed were destroyed But more importantly, the spinklers caused water damage thoughout the factory and smoke particles got into the sterile area, contaminating millions of chips held in stock

Four thousand miles away, Ericsson was Sweden’s largest company with

an annual revenue of $30 billion, 30 per cent of which came from mobile telephones For many years, Ericsson moved towards single sourcing as a way

of lowering costs and speeding deliveries Now the Philips plant was its sole source of many radio frequency chips, including those used in an important new product

At first, Philips thought that the plant would return to normal working within a week, so Ericsson was not too concerned when it heard about the fire However, it soon became clear that there was more extensive damage Philips actually shut the factory completely for three weeks, it took six months for production to return to half the previous level, and some equipment took years

to replace Ericsson had no alternative suppliers, an at a time of booming sales it was short of million of chips

In 2001 Ericsson said production lost due to the fire cost it more than $400 million When this figure was published, its share price fell by 14 per cent in a few hours For a variety of reasons, including problems with component supply, marketing mix, design, and the consequences of the fire, Ericsson’s mobile phone division lost $1.7 billion that year It decided to withdraw from handset production and outsource manufacturing to Flextronics International It also changed its approach to procurement, moving away from single sourcing and ensuring that there were always backup suppliers And it also introduced systems for risk management to avoid similar problems in the in the future

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Case study: Nokia

At the time of the 2000 fire in Albuquerque, Nokia was another leader in the communications insdustry, with revenues of $20 billion, more than 70 per cent of which came from mobile telephones It also used the Philips factory as a source of chips, and between them Ericsson and Nokia bought 40 per cent of its production

But Nokia’s reaction to the problem was mucch faster and more possitive than Ericsson’s In the 1990s Nokia had suffered from shortages of components that limited production and cost millions of dollars in lost sales It took various measures to stop this happening again, including the appointment of a “supply chain troubleshooter” who indentified problems and sorted them out as quickly as possible And it carefully avoided single – sourcing key components

Its proactive risk management meant that Nokia did not have to wait until Philips told it about the fire, but its “events management system” quickly noticed a hipccup The company immediately contacted Philips, and within hours of hearing about the fire had assembled a team to assess problems, find ways around them, monitor conditions and offer technical support More directly, it put pressure on Philips to divert capacity

in other plants to maintain its supplies – and it negotiated with other suppliers, redesigned chips so that other companies could make them, and redesigned products to use slightly different chips Nokia used its considerable influence to get everyone’s cooperation Alternative Japanese and US suppliers were delivering new chips within five days, and 10 million chips were supplied by other Philips factories in Eindhoven, the Netherlands and Shanhai, China Asa result of its actions, Nokia’s production was hardly affected by the fire

Case study: World Economic Forum

The World Economic Forum (WEF, 2010) publishes an extensive list os risks to the global economic These can be viewed as external risks for supply chain

management, and are broadly classified as economic, geopolitical, environmental,

societal and technological These major categories are broken down in to 36 more

specific types of risk, such as ‘food price volatility’ and ‘underinvestment in

infastructure’ (economic), ‘international terrorism’ and ‘transnational crime and

corruption’ (geopolitical), ‘extreme weather’ and ‘water scarcity’ (environmental),

‘pandemic’ and ‘migration’ (societal) and ‘critical information infrastructure

breakdown’ and ‘data fraud’(technological)

At a time of financial uncertainty, it is not surprising that economic risks cause most concern These are led by ‘asset price collapse’, ‘fiscal crises’ and ‘slowing

Chinese economy’ The leading non – economic risks are felt to be ‘chronic diseases’

and ‘global governance gaps’ Estimates for these risks suggest a chance of occurring

somewhere around 20 per cent, with consequences possibly as high as $1 trillion

An important point about the 36 risks is that they are all to some extent interconnected,

and when one risky event occurs it can lead to increasing risk in related ereas The

effects of each risk cannot be isolated in distinct units, but can expand to cause loss and

damage to whole systems It follows that an aim of the World Economic Forum is to

develop a global view of risks and ways to tackling them

Figure: Illustration of a basic register

Identification

number

Date recognized

Owner stakeholders

Description

of risk

Description

of impact

Evaluation Actions Responsibility Improvements

1

2

3

4

5

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Case study: identified risks

There is considerable variation – and disagreement – about the most significant risks

facing supply chains A survey (2006) for Accenture conducted by S Radoff Associates found that leading risks to supply chains are:

Raw material supply disruptions (………… per cent of respondents)

Import operations and customs delays (………… per cent)

Longer supply lines and lead times (……… per cent)

Geopolitical instability (……… per cent)

Shortage of skilled labour (……… per cent)

Terrorist infiltration of cargo (……… per cent)

A similar survey by AMR Research (2006) found that the main risks are:

Supplier failure (……… per cent)

Strategic risk (……… per cent)

Natural disaster (……… per cent)

Geopolitical events (……… per cent)

Regulatory risk (……… per cent)

Logistics failure (……… per cent)

Intellectual property infringement (……… per cent)

A survey by Richmond and Associates (2010) found that the risks most

commonly identified by European supply chain managers were:

Loss of the information system

Loss of the site

Government regulations

Currency fluctuation

Fire

Extreme weather

Floods and other natural disaster

Industrial action

Pressure group protest

Product safety

Health and safety issues

Loss of suppliers

Single sourcing

Supplier reliability

Poor forecasting

Shortage of key material

Lean operation

Long supply chains

Delays at frontiers

Lack of flexibility

Capacity problems

Traffic congestion

Shortage of key employees

Equipment failure

Political unrest or warfare

Terrorism

Case study: IBM

IBM is the world’s largest IT and consulting services company, with operations in 170 countries and revenue opproaching $100 billion Its complex supply chains move materials around intergrated global operations In 2008 it reviewed the supply chain for its System X server product, including identification of risks The company used a systematic approach to idetify risks based on the following steps:

 Map business processes for procurement, assembly and delivery

 Map the human, capital and informational sources that support these processes

 Hold interviews with key managers and engineers to idetify key risk factors and causes (ranging from catastrophic to everyday)

 Draw cause and effect diagrams – or influence diagrams – to assess the effects

of failures and disruptions

 Intergrate the risk analyses to pinpoint precisely potential problems and the way that effects are propagated through suply chains

Case study: Waikiki Wholesalers

Waikiki Wholesalers is a well-established business distributing food items to supermarkets around Hawaii Jose Samson is the owner of Waikiki and oversees all the adminitration, including records of the small number of customer complaints Over the past three years he has collected the figures shown in Table below

From these data, Jose drew the Pareto chart shown in Figure, which highlights the main areas for concern There were almost no complaints about the materials delivered, so customers were clearly pleased with these Half the complaints came from faults in the bill, and Jose reduced these by installing a new billing system Sometimes deliveries were slow ordelayed, and Jose worked out ways of reducing this problem with his transport managers Tackling these three problems dealt with 80 per cent of complaints, significantly reducing the risk of them reoccurring in the future

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Table: Waikiki customer complaints

complaints

Percentage of complaints

Mixture of food and non-food items 2 3

Figure: Pareto chart for Waikiki Wholesalers

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20

11

8

0

10

20

30

40

50

60

Faults in

the bill

Long lead

time

Late delivery

Wrong goods delivered

Wrong amounts delivered

Range of products

Mixture of food and non-food items

Condition

of fresh food

Condition

of wine

Rudeness

of staff

Percentage of complaints

percentage of complaints

Case study: James Bulwark & Sons

James Bulwark runs a UK warehouse for fruit and vegetables on the outskirts of Birmingham Deliveries come from over 100 suppliers within the European Union James Bulwark notices that suppliers usually quote performance in terms of lead time, but he finds that the greatest risk comes from those with most variability rather than the longest time He monitors the lead time of each delivery and builds up a picture for each supplier There is inevitably some variation about the mean, but provided the actual lead time is not too far from the target he assumes that operations are working normally and there is little risk But if there is a sudden jump in the lead time, or a trend away from the mean, he assumes that the process is out of control, risk is increasing and some remedial action is needed To see when this happens, he defines two limits – an upper control limit and a lower control limit, each about two standard deviations from the mean When the lead time moves outside these control limits it is time for remedial action

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Case Study: Jim’s Drive Through Bottle Shop

Jim’s Drive Through Bottle Shop sells alcoholic drinks in Brisbane, Australia The prices are low and the shop is busy, with most customers apparently to accept some delays at checkouts to get cheap drinks But a busy times the manager feels that he is losing customers

The shop has a single line of cars driving past a service window The obvious way of reducing delays is to have more service windows working parallel, but the site

is rather long and narrow, so this would be difficult

The manager decided to try a number of improvements, such as dividing the service into a series of stages He found the distributions of times for various operations, and then used a spreadsheet to simulate different options

He put three serves in a series The first serve, A, took the customer’s order, the second, B, looked after the bill and payment, and the third, C, delivered the goods The speadsheet follows 10 customers through the process It generates time for each activit and show how the process performs during a typical short period By following longer periods and more variations, the manager can look at different aspects of operations and see which configuration gives the best performance

Join queue

Start service

Leave Join

queue

Start service

Leave Join

queue

Start service

Leave

1 8.45 8.47 8.51 8.52 8.55 9.00 9.01 9.02 9.04

2 8.45 8.51 8.53 8.53 9.00 9.01 9.01 9.04 9.07

3 8.58 8.58 9.01 9.02 9.07 9.09 9.10 9.10 9.13

4 9.00 9.01 9.04 9.05 9.09 9.10 9.11 9.13 9.16

5 9.05 9.05 9.06 9.06 9.10 9.13 9.13 9.16 9.18

6 9.20 9.20 9.21 9.21 9.21 9.23 9.23 9.23 9.25

7 9.20 9.21 9.24 9.25 9.25 9.28 9.29 9.29 9.33

8 9.22 9.24 9.26 9.27 9.28 9.30 9.30 9.33 9.35

9 9.25 9.26 9.29 9.30 9.30 9.34 9.35 9.35 9.38

10 9.25 9.29 9.32 9.33 9.35 9.38 9.39 9.39 9.44

Analysis

Number of customers

Time in system