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MGMT2001 Case Study
Creation date：2024-06-14 16:49:26
Belonging category：市场营销Marketing
Case Study

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MGMT2001

Case Study
Case/Background
Dyson (dyson.co.uk), a company that designs and manufactures household appliances, is
currently being sued by workers employed by a third party in a Malaysian factory where Dyson
products were being manufactured. The claim alleges that workers were subject to unlawful and
exploitative working conditions and that Dyson is liable because it knew, or ought to have
known, of the alleged illegal practices. They point to Dyson’s previous public statements about
their procedures for detecting and preventing forced labour in their supply chains.
Driven by environmental, social, and governance (ESG) principles, governments and regulators
are increasingly obliging businesses to identify and address human rights and environmental
risks along their supply chains. A transition from previous ‘soft law’ standards to “hard law”
obligations has the potential to impact businesses outside the territories in which regulations are
imposed, including businesses based in the UK. Businesses will need to monitor new legislation
as well as their own supply chains in preparation for greater scrutiny by regulators and by
contracting parties who are directly bound by the new rules.
The EU is bringing forward a series of mandatory supply chain due diligence proposals over the
next two years under its “Green Deal”, a programme of policy initiatives with the overarching
aim of making the EU climate neutral by 2050. The EU's measures are designed to have extra-
territorial reach with the aim that commodities traded through the EU and products placed on the
EU market are sourced / manufactured responsibly.
UK companies with an EU presence will be directly affected but any UK companies supplying
in-scope EU companies will be indirectly affected. The regulatory scrutiny is wide-ranging. For
example, the Deforestation Regulation will require companies trading commodities with the EU
(including cattle, cocoa and wood, as well as products made from those commodities such as
leather, chocolate and books) to ensure their supply chains are not linked to illegal deforestation.
According to the Food & Drink Federation, EU trade made up 69 per cent of the UK’s total food
and drink imports and 55 per cent of exports in 2022. All of this trade will be covered by these
regulations. Those companies found to be non-compliant can have products confiscated, be
denied access to the EU market or, in the worst cases, potentially be fined up to 4 per cent of
their annual turnover in the EU.
Germany has already implemented its own Corporate Due Diligence Obligations in Supply
Chains Act (Lieferkettensorgfaltspflichtengesetz - LkSG) (the German Act) in January 2023. As
with a similar EU directive, it requires companies to have a compliance management system to
assess and report on the risk of ESG abuses within their own companies, with direct suppliers
and, where they are deemed to have “substantial knowledge”, indirect suppliers. Any UK
businesses which trade directly or indirectly with German companies will therefore be subject to
scrutiny via their German counterparts and will need to be able to account for their own onward
supply chain, or risk losing business. Furthermore, UK companies with branches in Germany
employing more than 3,000 employees are already bound. This threshold will fall to 1,000
employees from next year.
Regulations relating to supply chain due diligence have also been introduced outside the EU in
recent years, including the Business Supply Chain Transparency on Trafficking and Slavery Act
in the US (2020), the Transparency Act in Norway (2022) and the Corporate Responsibility on
Human Rights (C-262) statute in Canada (2022). This is a developing area, but the direction of
travel globally is clear.
Risk
There is an obvious risk to any UK businesses which are directly bound by local laws in other
jurisdictions, including supply chain due diligence regulations, of being subject to legal claims or
regulatory action in those jurisdictions.
There is also a risk of disputes between contracting parties where one has allegedly failed to
comply with its ESG-related obligations. For example, a German company seeking to ensure its
own compliance with the German Act may request a contractual warranty from a supplier that it
has complied with the relevant statutory codes of conduct. Claims could arise where any such
warranty is allegedly breached.
Question
Dyson has been growing through its innovation based on R&D investments, which require
continued investment. However, as a global manufacturing company, managers must
navigate the complexities of managing the supply chain across countries, which now poses
greater supply chain-related risks.
[Body] Against this backdrop, write a report (case study) on Dyson, focusing on
organizational change. Should Dyson change its manufacturing strategy in terms of off-
shore manufacturing?
[Recommendations & Conclusion] What strategy would you recommend to Dyson to deal
with the current problem. Detail your approach to resolve the issue in 1-3 years (short-
term) and 5-10 years (long-term).
Case category
Tag cloud
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EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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