I am pleased to announce our maiden sustainability report titled “Supply Chain Sustainability in Sub-Saharan Africa: Evidence from the Supply Chain of a Leading Nigerian Commodities Business” We are a leading sustainability research and consulting company providing insights and longitudinal case studies that make a business case for corporate sustainability.

1. Supply Chain Sustainability in Sub-Saharan Africa:
Evidence from the supply chain of a leading Nigerian commodities business
Strictly Private and Confidential

2. Author
2Content
PART I Overview
PART II Macro-economic Context: Sub-Saharan Africa
PART IV Supply Chain Sustainability
PART III A Global Perspective
PART V Case Study: Nigerian Commodities Business
PART VI Conclusion
PART VII Appendix
This research provides high-quality and rigorous insights into supply chain
sustainability practices in developing countries.
This paper is authored by Sustain Africa. Contents should not be quoted or
reproduced without the author’s permission.

3. Overview
I

4. 4Sustainable Development Goals (SDG)
• The SDGs were adopted by the 193 UNGA member
countries in September 2015.
• The SDGs succeeded the Millennium Development
Goals (MDGs).
• There are 17 SDGs, 169 targets, and 232 indicators.
• The aim of the SDGs is to “…end all forms of poverty,
fight inequalities and tackle climate change, while
ensuring that no one is left behind.” (UN, 2015).
• The SDGs have been integrated with the tripartite
model of sustainability (Rockstrom & Sukhdev, 2016).
Integrating the SDGs

5. 5Reporting and Rating Initiatives

6. 6
9
Measures of Corporate Sustainability
DJSI: Dow Jones Sustainability Index
MSCI: Morgan Stanley Capital International
GRI: Global Reporting Initiative

7. II
Macro-economic
Context: Sub-
Saharan Africa

8. 8Macro-economic Context: SSA
List of Regions
(African Union
Classification)
West Africa
Central Africa
Southern Africa
East Africa
Nigeria
23%
South Africa
22%
Angola
6%
Kenya
5%
Ethiopia
5%
Ghana
4%
Others
35%
1,739.51
1,699.46
NominalGDP (Billion US$)
2013 2018
943.04
1,078.31
Population (Millions)
2013 2018
Share of
Nominal GDP
(2018)
$30,503
$23,709
$23,473
$18,583
$17,912
Seychelles
Mauritius
Equatorial Guinea
Botswana
Gabon
7.7%
8.5%
CPI, Inflation
2010-2015 2018
CAGR: -0.6%
CAGR: 3.4%
Source: African Union (AU), World Bank, IMF
2018 Nominal GDP, Population, and Inflation Data
2018 GDP Per Capita, PPP (Top 5 Countries)

9. III
A Global
Perspective

10. 10CS From a Global Perspective
• The big 4 commodity traders, commonly referred to as the ‘ABCDs’ include: Archer Daniels Midland (ADM) | Bunge | Cargill | Louis Dreyfus
Company (LDC).
• They are responsible for 90% of global grain trades (OXFAM, 2012).
• Challengers of the ABCDs include Olam | Wilmar | COFCO group | Sinar Mas | Golden Agri-resources (GAR), among others.
• These challengers are commonly referred to as the ‘contenders’.
• These organizations use technology to incorporate the tenets of sustainability in their supply chains, albeit to varying degrees.
ACE Platform ©
Sustainability Platforms
ADM Bunge Cargill LDC Olam Wilmar COFCO GAR
Headquarters
(Country)
USA USA USA Netherlands Singapore Singapore Switzerland Singapore
Organization
Type
Public Limited,
Listed
Public Limited,
Listed
Private Private Public Limited, Listed Public Limited,
Listed
Private Public Limited,
Listed
Business
Segments
1. Origination,
2. Oilseeds,
3. Carbohydrate
Solutions,
4. Nutrition
1. Agribusiness,
2. Edible oil
products,
3. Milling
products,
4. Sugar &
bioenergy,
5. Fertilizer
1. Animal nutrition
& protein,
2. Food
ingredients &
applications,
3. Origination &
Processing,
4. Industrial &
financial services
1. Value Chain Segment
a. Grains,
b. Oilseeds,
c. Juice,
d. Freight,
e. Global Markets
2. Merchandising Segment
a. Coffee,
b. Cotton,
c. Rice,
d. Sugar,
e. Dairy
1. Edible nuts &
spices,
2. Confectionery &
beverage
ingredients,
3. Food staples &
packaged foods,
4. Industrial raw
materials,
Infrastructure and
logistics
1. Tropical oils,
2. Oilseeds and
grains,
3. Sugar,
4. Others
1. Sugar
2. Cotton
3. Grains &
Oilseeds
4. Coffee
5. Freight
1. Plantation & palm
oil mills
2. Palm, laurics &
others
Sustainability
Initiatives
Field to Market ACE Platform Grainbridge NA Atsource NA NA NA
Year Founded 1902 1818 1865 1851 1989 1991 1993 1987

11. 11Materiality
• Both ABCDs and contenders use the tripartite model to ascertain material areas of sustainability.
• The organizations also cite relevant SDGs in their sustainability reports.
• The top 5 environmental, social, and economic material areas for the 8 organizations were identified by thematic analysis.
• SDG 15: Life on land appeared for all organizations under consideration.
• SDGs 2, 6, 8, 12, 13, and 17 were also well represented.
• The ABCDs largely faired better than the contenders in financial performance and size, while sustainability performance is even.
38%
88%
38%
63%
38%
75%
50%
88%
38%
25%
13%
88%
75%
25%
100%
38%
75%
SDG1
SDG2
SDG3
SDG4
SDG5
SDG6
SDG7
SDG8
SDG9
SDG10
SDG11
SDG12
SDG13
SDG14
SDG15
SDG16
SDG17
Material Areas of SustainabilityOccurrence of SDGs

12. IV
Supply Chain
Sustainability

13. 13
• Typical supply chain risks differ from sustainability-
related supply chain risks (SRSCR).
• (Giannakis & Papadopoulos, 2016) provided a
framework to help differentiate the two.
• These risks can be classified into endogenous
(originated from within), and exogenous (originated
from outside) groups.
• SRSCRs can be managed through existing risk
management strategies, i.e. ISO 31000.
Typical supply chain risks Sustainability-related risks
Risk identification
Supply chain disruptions i.e. delays,
forecast errors, inventories, etc.
Deterioration of ecosystems, effect
on societal values and responsible
management.
Risk assessment
Done using operational or financial
methods.
Inductive studies
Risk treatment
• Organization-wide understanding of
supply chain risks through stress
testing and tailoring.
• Management assessment of risks
and proper business planning used
as a risk treatment method.
• Risk treatment opportunities of
business improvement and market
share gain.
• Strategies for managing all three
sustainability dimensions.
• The use of scenario planning and
simulation, automatic fault
detection, automatic recovery.
• The opportunity for competitive
advantage and business
excellence.
Sustainability-Related Supply Chain Risks
ISO 31000: Risk Management
• The ISO 31000 risk management process is a non-
sequential but iterative process that involves 6 steps.
• Communication and consultation
• Scope, context and criteria
• Risk assessment involves three processes:
• Risk identification,
• Risk analysis, and
• Risk evaluation
• Risk treatment
• Monitoring and review
• Recording and reporting

14. 14Sustainability-Related Supply Chain Risks in Nigeria
• Risk agents and risk events were identified through
extensive literature review.
• Access to the members database of a prominent
supply chain institute was granted.
• Stratified sampling was used to select respondents.
• Sample survey consisted of 40 organizations across
several industries and 249 supply chain
professionals.
• Input from 105 supply chain managers (42%
sample size) was used to refine the lists.
• Represented industries include:
• Animal feed manufacturing,
• Beverages and brewing,
• Flour milling,
• Pharmaceutical,
• Fast moving consumer goods (FMCG),
• Sugar refining, and
• Cement manufacturing.
• A final list of 28 risk events and 43 risk agents were
identified.
Sustainability
dimension
Class of
Dimension
Risk Events Risk Agents
Environmental
Endogenous
1. Increased material wastage 1. Pollution
2. Inadequate crop
rotation/biodiversity
3. High GHG emissions
4. High packaging cost
5. Farmer Disloyalty
6. Low energy efficiency
7. Smuggling across porous
borders
8. High logistics costs
9. Pests and diseases
10. Water scarcity
11. Natural disasters
12. Heatwave, drought
13. Forced labour/slavery
14. Child labour
15. Fair wages and benefits
16. Harassment
17. Unethical genetic modification
18. Workplace health & safety
19. Human and labour rights
20. Inadequate diversity and
inclusion
21. Inadequate community
engagement
22. Social unrest
23. Terrorism
24. Unionization of employees
25. Insecurity
26. Political unrest
27. Demographic changes
28. Extinction of species
29. Pests and diseases
30. Rising unemployment
31. Transparency and traceability
32. Corporate governance
33. innovation
34. Tax avoidance and evasion
35. Price fixing (by partners)
36. Intellectual property theft
37. Bribery and corruption
38. Dishonesty and false claims (by
partners)
39. Seasonality
40. Financial crises
41. Boycotts
42. Energy cost volatility
43. Logistics cost volatility
2. Damage of ecosystem
Exogenous
3. Depletion of natural
resources
4. Depletion of ozone layer
Social
Endogenous
5. Reputational damage/loss of
trust
6. Adverse product quality and
safety
7. Loss of talented employees
8. Deterioration of community
relations
Exogenous
9. Lack of access to workplace
10. Loss of lives and property
11. Low employee motivation
12. Reduction in spending power
Economic
Endogenous
13. Significant loss of
customers/patronage
14. Loss of productivity
15. Loss of value from
inefficiencies
16. Market share loss
17. Bankruptcy and insolvency
18. Threat to going concern
status
19. Loss of access to credit
20. Inability to fulfil delivery
requests
Exogenous
21. Supply chain cost increases
22. Supply chain interruptions
23. Fines and penalties
24. Breach of contract and
agreements by suppliers
25. Loss of corporate value
26. Increased overhead cost
27. Decline in share value
28. Large order cancellations (By
Customer)

15. 15House of Risk (HOR) Model
• The house of quality (HOQ) model is an innovative model to manage the risk agents that could lead to one or more risk events.
• The HOR model is a modification between the well-known Failure Mode and Effect Analysis (FMEA) and HOQ models.
• The HOR model is usually deployed in 2 phases:
• HOR1: To prioritize risk agents for proactive actions.
• HOR2: To prioritize the proactive actions considering their effectiveness to difficulty (ETD) ratios.
Sustainability dimension
Class of
Dimension
Risk
Events
(Ei)
Risk Agents (Aj) Severity
of risk
event (Si)
A1 A2 A3 A4
Environmental
Endogenous E1 R11 R12 R13 R14 S1
Exogenous E2 R21 R22 R23 R24 S2
Social
Endogenous E3 R31 R32 R33 R34 S3
Exogenous E4 R41 R42 R43 R44 S4
Economic
Endogenous E5 R51 R52 R53 R54 S5
Exogenous E6 R61 R62 R63 R64 S6
Occurrence of risk agent j O1 O2 O3 O4
Aggregate risk potential of j ARP1 ARP2 ARP3 ARP4
Priority rank of risk agent j
𝑨𝑹𝑷𝒋 = 𝑶 𝒋 '
𝒊
𝑺𝒊 𝑹𝒊𝒋
Shortlisted Risk Agent (Aj)
Proactive action (Pk)
ARPj
P1 P2 P3
A1 E11 E12 E13 ARP1
A2 E21 E22 E23 ARP2
A3 E31 E32 E33 ARP3
Total effectiveness of action k TE1 TE2 TE3
Degree of difficulty of action k D1 D2 D3
Effectiveness to difficulty (ETD) ratio ETD1 ETD2 ETD3
Priority rank of action k (Rk)
𝑻𝑬 𝒌 = '
𝒋
𝑨𝑹𝑷𝒋 𝑬𝒋𝒌∀ 𝒌
𝑬𝑻𝑫 𝑲 =
𝑻𝑬 𝒌
𝑫 𝒌
HOR1 Model
HOR2 Model
Rij {0,1,3,9}
Ejk {0,1,3,9}

16. V
Case Study:
Nigerian
Commodities
Business

17. 17Overview
• The company is a subsidiary of a leading food and agri-business multinational company operating in 60+ countries, with approximately $24 billion in
2018 revenue.
• The subsidiary is primarily involved in the manufacturing and sale of animal feeds, and the occasional maize and soybeans trading.
• The subsidiary, a market leader with approx. 30% market share, has two animal feed manufacturing plants in Nigeria with combined production
capacity of 600,000 MT of feed per annum. In addition to combined silo storage capacity of 80,000 MT.
• For the purpose of this research, we focus solely on the company’s maize supply chain.
The Supply Chain Diagram

18. 18Methodology
• The risk management process was used to guide the structure of the longitudinal case study.
• The HOR models were used in the identification, analysis & evaluation, and treatment of sustainability-related supply chain risks.
Scale 1 2 3 4 5 6 7
Severity No impact
Negligible
impact
Minor
impact
Moderate
impact
Major
impact
Critical
impact
Catastrophic
impact
Degree of
occurrence
Almost
never
Rarely Infrequently Occasionally Frequently Usually
Almost
Always
Degree of
difficulty
Extremely
easy
Very easy Slightly easy Moderate
Slightly
difficult
Very
difficult
Extremely
difficult
• Linkert type scales were used to assess degree of severity, likelihood of occurrence, and degree of difficulty.
Research Methodology
Risk Scale
Analytical Step Data Collection Data Analysis
Identify
• Risk events
• Risk agents
• Literature review
• Surveys
• Interviews
• Sessions
• Content
analysis
Assess
• Severity of risk events
• Occurrence of risk agents
• Correlation between risk agents
& risk events
Calculate
• ARP’s
Prioritize
Using pareto analysis
• Questionnaire
• Interviews
• Sessions
• HOR1
• Pareto
analysis
Identify
• Proactive actions
Assess
• Difficulty of proactive actions
• Correlation between proactive
actions & risk agents
Calculate
• ETD ratio
Prioritize
• Proactive actions
• Questionnaire
• Interviews
• Sessions
• HOR2
• Assess economic impact of
proactive actions
• Financial reports
• Interviews
• Sessions
• Profitability
analysis

19. 19Risk Identification
• The risk events and risk agents were refined by interviewing internal and external stakeholders relevant to the company’s maize supply chain.
• The associated degree of severity, and likelihood of occurrence were identified by administering questionnaires to relevant supply chain
managers.
• Risk events have been coded E1 to E22, while risk agents have been coded A1 to A20.
Sustainability
dimension
Class of
Dimension
Risk Events Code Severity
Environmental
Endogenous
Increased material wastage E1 7
Damage of ecosystem E2 6
Exogenous
Depletion of natural resources E3 6
Depletion of ozone layer E4 4
Social
Endogenous
Reputational damage/loss of trust E5 5
Adverse product quality and safety E6 6
Loss of talented employees E7 6
Deterioration of community relations E8 5
Exogenous
Lack of access to workplace E9 2
Loss of lives and property E10 5
Economic
Endogenous
Significant loss of customers/patronage E11 6
Loss of productivity E12 6
Loss of value from inefficiencies E13 7
Market share loss E14 2
Threat to going concern status E15 6
Loss of access to credit E16 6
Exogenous
Supply chain cost increases E17 7
Supply chain interruptions E18 7
Fines and penalties E19 5
Breach of contract and agreements by
suppliers
E20 5
Loss of corporate value E21 4
Increased overhead cost E22 5
Risk Agent Code Occurrence
High GHG emissions A1 5
High packaging costs A2 7
Farmer Disloyalty A3 5
Low energy efficiency A4 4
Smuggling across porous borders A5 6
High logistics costs A6 6
Rising Unemployment A7 7
Water scarcity A8 5
Natural disasters A9 4
Unionization of employees A10 4
Inadequate diversity and inclusion A11 4
Inadequate community engagement A12 6
Insecurity A13 6
Political unrest A14 7
Transparency and traceability A15 5
Price fixing (by partners) A16 5
Bribery and corruption A17 5
Dishonesty and false claims (by partners) A18 6
Seasonality A19 3
Energy cost volatility A20 2

20. 20Risk Analysis & Evaluation – HOR1
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
-
500
1,000
1,500
2,000
2,500
3,000
A13
A3
A9
A8
A14
A4
A1
A18
A16
A6
A15
A12
A17
A2
A19
A7
A20
A11
A5
A10
Cumulative%
ARPScore
Risk Agents
• Rij {0,1,3,9} was used to assess the relationship between risk
agents and risk events. 1 = low correlation; 9 = high correlation.
• The coefficient of 9 between A1 and E2 means that high GHG
emissions would certainly damage the ecosystem.
• The severities, correlation coefficients, and occurrences were
used to calculate the respective average risk potentials (ARPj).
• The ARPswere ranked and a pareto diagram used to prioritize.
• A clear pareto rule does not apply as the first 12 risk agents
contributed about 80% to the total ARP.
• The first 11 risk agents (75% of total ARP) were selected for the
HOR2 model.
Pareto Diagram

21. 21Risk Treatment – HOR2
• 15 proactive actions were identified to manage the prioritized risk
agents from the HOR1 model.
• The actions were classified into four risk treatment categories of
avoid, retain/accept, mitigate/control, and transfer/share.
• Questionnaires were administered to supply chain managers to
assess the degree of difficulty of each proactive action.
• The degree of difficulty (Dk) reflects the expected costs of the
proactive actions.
• Ejk {0,1,3,9} was used to assess the relationship between proactive
actions and risk agents.
• The ARPs, and correlation coefficients were used to calculate the
effectiveness (TEk) of the proactive actions.
• The effectiveness to difficulty ratios (ETDk) were calculated by
dividing the TEk by the Dk.
• The proactive actions were then ranked for prioritization.
Proactive actions Code Difficulty
Insurance of property against disaster and catastrophe P1 4
Backward integration P2 7
Farmer partnerships - implement farmer outgrower schemes P3 5
Use clean energy sources P4 5
Invest in energy efficient technology P5 6
Monitor and record carbon footprint P6 2
Drop logistics partners with high emissions P7 2
Invest in water recycling technology P8 6
Contingency plan for remote work P9 2
Monitor supplies from low trust partners P10 2
Engage stakeholders routinely P11 1
Use of technology to aid traceability P12 3
Use reverse haulage schemes P13 1
Continuously vet and choose suitable suppliers P14 1
Have a dedicated sustainability function P15 2

22. 22Risk Monitoring and Review
• 6 proactive actions not limited to the HOR2 model were
selected for implementation:
• Carbon Emissions Reduction (CER): Selecting logistics
partners with average emissions factor of 170 gCO2 per MT-
Mile or less.
• Farmer Yield Improvement (FYI): Out-grower scheme to
provide seeds, fertilizer, and extension services to farmers
thereby guaranteeing a forward price. Committing $2
million to R&D of Non-GMO, hybrid maize seeds.
• Maize Trading (MT): Offsetting the maize procurement cost
of debt (KD) by trading at net margins (NM) where NM > KD.
• Packaging Material Re-usability (PMR): Using larger, and
re-usable packaging materials.
• Property Insurance (PI): Improving insurance policy to
include cover for natural disasters and catastrophes.
• Transparency & Traceability (T&T): Selecting only verifiable
suppliers, enrolling them into an internal database, and
certifying them – using Gold, Silver & bronze categories.
Plant 1 Plant 2 Total
Key Data
Total Maize Supply 145,000 105,000
Avg. Maize Value (Per MT) $198.69 $208.34
Carbon Emissions Reduction (CER)
Avg. Round-trip Distances (Miles) 176 563
Previous Emissions Factor (gCO2) 228 251
Current Emissions Factor (gCO2) 173 169
Total Previous Emissions (MTCO2) 5,812 14,838 20,650
Total Current Emissions (MTCO2) 4,410 9,990 14,400
Previous Emission Intensity (MTCO2e/MT) 0.48 1.70
Current Emission Intensity (MTCO2e/MT) 0.36 1.14
Carbon Pricing (Per MT) $8.0 $8.0
Total Annual Savings $11,216 $38,779 $49,995
Farmer Yield Improvement (FYI)
Previous Yield - MT/ha 1.9 1.9
Current Yield - MT/ha 2.5 2.6
Discounts 7% 7%
Farmer Gain (Per Ha) $112.71 $145.32
Partnership Volume Split 20% 20%
Partnership Volume (MT) 29,000 21,000 50,000
Maize Trading (MT)
Weighted Avg. Cost of Debt 10.6% 10.6%
Cost of Debt $3,039,406 $2,307,940 $5,347,346
Trading Margin (Per MT) $63.47 $53.81
Annual Savings/Trading Gain $374,300 $109,172 $483,472
Packaging Materials Re-usability (PMR)
Previous SKU (KG) 50 50
Previous Bag Cost (Per Bag) $0.18 $0.18
Current SKU (KG) 800 800
Current Bag Cost (Per Bag) $11.73 $11.73
Re-usability (X) 5 5
Structural Adjustments $58,000 $42,000 $100,000
Transparency & Traceability (T&T)
Previous Number of Suppliers 135 141
Current Number of Suppliers 55 64
Wastage Reduction (Basis Points) 22 13

23. 23Financial Implications
• Financial implications of the proactive actions were monitored over the 2018 FY.
• The CER action led to earnings gain of approx. $50k.
• The FYI action reduced maize weighted average cost by 140 bps, and increased earnings by approx. $360k.
• The MT action helped defray maize cost of debt by approx. $480k.
• The PMR action helped reduce packaging material cost by 18%, and increased earnings by approx. $120k.
• Additional insurance expense of $150,000 was incurred from the PI action, this decreased earnings by approx. $170k.
• The T&T action helped reduce wastage in Plant 1, and Plant 2 by 22 bps and 13 bps respectively, and improved earnings by $100k.
• Results show that the earnings margin improved by 43 bps as a result of the sustainability initiatives.
• Consequently, profitability ratios – ROE, ROIC, and EBITDA/IC also improved by 155 bps, 77 bps, and 138 bps respectively.
in million $ except for the margins 2018A CER FYI MT PMR PI T&T
2018A
(Without
Initiatives)
Revenue 170.21 (6.38) 163.83
Cost of Sales (143.53) (0.80) 4.92 (0.13) (0.10) (139.65)
Margin 84.3% 85.2%
Gross Profit 26.68 23.18
Margin 15.7% 14.8%
Net Operating Expenses (13.07) (0.05) 0.24 0.46 0.15 (12.27)
Margin 7.7% 7.5%
Operating Profit (EBIT) 13.61 11.91
Margin 8.0% 7.3%
Net Interest Cost (6.96) 0.21 0.52 0.01 0.02 (6.20)
Profit Before `Tax 6.65 5.70
Margin 3.9% 3.5%
Taxation
Profit After Tax 6.65 (0.05) (0.36) (0.48) (0.12) 0.17 (0.10) 5.70
Margin 3.9% 3.5%
Depreciation & Amortisation 3.90 3.90
EBITDA 17.51 15.81
Margin 10.3% 9.6%
Invested Capital (IC) 123.70 123.70
LT Debt 39.58 39.58
Notional Equity 61.10 61.10
ROE 10.9% 9.3%
ROIC 5.4% 4.6%
EBITDA/IC 14.2% 12.8%
Earnings Waterfall Chart

24. 24Highlight – Farmer Yield Improvement (FYI)
• An out-grower scheme was implemented by partnering with farmer co-operatives, and large independent farms mainly across Northern Nigeria.
• The partners were provided inputs – fertilizer, seeds, and extension services to help boost their yields, thereby putting more income in their pockets.
• Seeds were hybrid, disease-resistant, Non-GMO maize seeds.
• Increased yield at little to no working capital requirement meant that the partners were more than willing to participate.
• Furthermore, farmers could guarantee a forward price at a 7% discount to market prices due to improved earnings.
• Weighted average cost of maize dropped by an impressive 140 bps, thereby helping to improve margins.
• The organization has committed $2 million to improving the out-grower scheme by adding more partners, funding more R&D, and shifting total maize
demand to the scheme to aid traceability.

25. 25Further Discussions
• Data was obtained over an 18 month period (January 2018 to June 2019).
• All prices are in NGN, while all volumes are in tonnes.
• Chart A shows that the relationship between average monthly procurement prices and weighted average prices is a positively correlated one.
• Procurement prices and volumes are also influenced by seasonality as witnessed in the swings in both chart A and B.
• Chart C shows that total maize inclusion hovers between 43 to 49% in total raw material mix. Yellow maize is sourced from imports and subject to availability.
• Chart D shows that Kaduna state contributed 82% to total maize procurement volume over the 18 month period. However, the company has a fairly sizable maize
procurement outreach.
-
20,000
40,000
60,000
80,000
100,000
120,000 Jan.2018
Feb.2018
Mar.2018
Apr.2018
May.2018
Jun.2018
Jul.2018
Aug.2018
Sep.2018
Oct.2018
Nov.2018
Dec.2018
Jan.2019
Feb.2019
Mar.2019
Apr.2019
May.2019
Jun.2019
Procurement Price Weighted Average Price
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
-
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
Jan.2018
Feb.2018
Mar.2018
Apr.2018
May.2018
Jun.2018
Jul.2018
Aug.2018
Sep.2018
Oct.2018
Nov.2018
Dec.2018
Jan.2019
Feb.2019
Mar.2019
Apr.2019
May.2019
Jun.2019
Consumption
Procurement
Procurement Volume Consumption Volume
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Jan.2018Feb.2018M
ar.2018Apr.2018M
ay.2018Jun.2018Jul.2018Aug.2018Sep.2018Oct.2018Nov.2018Dec.2018Jan.2019Feb.2019M
ar.2019Apr.2019M
ay.2019Jun.2019
White Maize Yellow Maize Total Maize
Procurement Vs Weighted Average Prices Procurement Vs Consumption Volume
Maize Inclusion in Raw Material Mix Maize Procurement Geographic Mapping

26. VI
Conclusion

27. 27
Purchase Complete Report
• The methodology used in the research can be replicated across all organization types irrespective of industry.
• A more comprehensive sustainability report is available for sale. It contains:
• Questionnaire structure to ascertain company-specific risk events, and risk agents,
• Detailed methodology on measuring, and reporting carbon emissions,
• Detailed methodology for improving traceability using technology
• Impact of renewable energy use, water management, women involvement across the supply chain, freight emissions,
traceability, and biodiversity.
• Pricing:
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28. VII
Appendix

29. 29
Part IV: The Global Supply Chain
• The global supply chain is relevant to organizations whose supply chains spans across multiple countries.
• (Manuj & Mentzer, 2008) identified four broad categories of supply chain risks in the global supply chain:
• Supply Risk: Risk events in inbound supply that could affect meeting customer demands, i.e. raw material quality.
• Operational Risk: Events that could hinder the production of goods & services, i.e. breakdown of machinery.
• Demand Risk: Events that affect the likelihood of getting customers’ orders, i.e. increased competition.
• Security risk: Events relating to HR, operations integrity, and information systems, i.e. IT breaches, and sabotage.
The Global Supply Chain
Sustainable Innovation Context (SIC)
• Innovation context is critical to the innovation process.
• (Adams, et al., 2015) identified 3 dimensions and 3 sustainable
innovation (SI) context levels.
• SI level 1: Organizations innovate towards operational
efficiency, i.e. improving energy efficiency.
• SI level 2: Beyond operational efficiency to organizational
transformation, i.e. innovation across the supply chain.
• SI level 3: Full radical innovation that affects the organization,
supply chain, stakeholders, and market.

30. 30Part III: Statistical Analysis: ABCDs Vs Contenders
Mean -
ABCDs
Mean -
Contenders
t df
Sig. (2-
tailed)Description
Age 160 29 7.532 3 0.005
Employees (Thousands) 62,453 52,118 0.298 3 0.785
Core Countries of Operations 52 32 4.521 3 0.020
Revenue - $'Mn 65,010 26,198 2.261 3 0.109
Size (Total Assets) - $'Mn 35,094 23,749 1.106 3 0.349
Shareholders Equity - $'Mn 15,885 8,590 1.113 3 0.347
Net Profit - $'Mn 1,256 487 1.372 3 0.264
Net Profit Margin (NPM) 1.7% 1.3% 0.757 3 0.504
Return on Assets 3.0% 1.4% 2.177 3 0.118
Return on Equity 7.2% 4.1% 2.970 3 0.059
Return on invested capital 6.7% 3.0% 3.394 3 0.043
Debt to Assets 19.8% 44.8% -8.718 3 0.003
Revenue CAGR - 5 years -6.5% 3.7% -4.112 3 0.026
Net Profit CAGR - 5 years -5.3% -26.0% 3.212 3 0.049
Energy Intensity Ratio (MWh/MT) 0.44 0.23 0.747 2 0.533
Emission Intensity - Scope 1&2 (MT CO2e/MT) 0.12 1.59 -18.313 2 0.003
Number of Material issues 10 15 -3.422 3 0.042
Number of SDGs 11 8 1.192 3 0.319

31. The End