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- 1. Developing Sustainable Manufacturing Practices forthe Pharmaceutical Industry (Green Manufacturing) ALP Final Check in | April 2024 Aspen Ascend 2023/2024 | Group 3 – The Green Pioneers
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- 3. • W EL C O M E • E M P A T H I S E P H A S E • D E F I N E P H A S E • I D E A T E P H A S E • P R O T O T Y P E P H A S E • T E S T P H A S E • C O N C L U S I O N • Q U E S T I O N S & A N S W E R S Contents • W E L C O M E • E M P A T H I S E P H A S E • D E F I N E P H A S E • I D E A T E P H A S E • P R O T O T Y P E P H A S E • T E S T P H A S E • C O N C L U S I O N • Q U E S T I O N S & A N S W E R S 3
- 4. 4 • W EL C O M E • E M P A T H I S E P H A S E • D E F I N E P H A S E • I D E A T E P H A S E • P R O T O T Y P E P H A S E • T E S T P H A S E • C O N C L U S I O N • Q U E S T I O N S & A N S W E R S Contents • E M P A T H I S E P H A S E
- 5. 5 Setting the context 23 FA C I L I T I E S 7 A P I 16 F D F Aspen Manufacturing Facilities
- 6. 6 Setting the context Pillarsof Aspen’s Sustainability Strategy O U R P E O P L E P A T I E N T S S O C I E T Y E N V I R O N M E N T • Inclusive, healthy, safe work environment • Treated with fairness and respect • Inspired to develop to full potential • Access to medicine • High quality, affordable products across geographies of our operations • Ethical and responsible business practice • Dignity, fundamental freedoms and human rights • Responsible environmental stewardship • Minimising negative impact • Compliance with applicable laws, regulations and other requirements
- 7. 7 Research methodology Q UA L I T A T I V E S T U D Y Secondary Data Academic Articles Interviews S O U R C E S O F D A T A Secondary Data Academic Articles Interviews S P O N S O R O P E R A T I O N A L S T R A T E G I C Jeanette Englund Group Executive: Risk & Sustainability, Aspen Holdings Fleur Wilson SHE Manager, Aspen SA Operations Leslie Wilson SHE Officer, Aspen SA Operations Dean Schmelzer Site Capex Manager, Aspen SA Operations Basil Mugwagwa Engineering Manager, Aspen SA Operations & ALP sponsor
- 8. 8 Research methodology W AS T E C A R B O N No-Go / Go Decision: 1. Existing projects underway 2. Feasibility of the project scope 3. Feasibility of the project timescale 4. Challenges
- 9. 9 Research methodology W AS T E C A R B O N F O C U S I D E N T I F I E D P R O B L E M S P O T E N T I A L S O L U T I O N S C O N C L U S I O N • Apply a lifecycle approach to resource-use & wate management • To achieve a near zero operational waste-to-landfill by 2030 • Glass vials are not recycled and moved to landfill • Labels on in-process sample plastic containers are challenging to remove disabling plastic recycling. Plastic containers are moved to landfill • Glass vials recycling • Alternative methods of labelling and/or label removal e.g. Water soluble labels, to enable plastic recycling • Projects underway: Yes • Anticipated feasibility of project scope: No • Anticipated feasibility of project timescale: Not achievable • Anticipated challenges: Regulatory & pharma recycling F O C U S I D E N T I F I E D P R O B L E M S P O T E N T I A L S O L U T I O N S C O N C L U S I O N • Aspen Goal: To reduce carbon emissions by 25-42% by 2030 • Energy consumption is one of the largest contributors to carbon emissions • Reduce energy consumption • Move to sustainable providers of energy • Move to renewable sources of energy • Energy recycling • Solar panels • LED Lighting • HVAC upgrades • Energy providers • Energy monitoring • Wind power • Automation • Incineration NO - GO GO • Projects underway: Yes; Energy monitoring & IOT is a potential solution • Anticipated feasibility of project scope: Yes • Anticipated feasibility of project timescale: Yes • Anticipated challenges: TBC USE OF IOT IN ENERGY MONITORING FOR IMPACT ON ENERGY CONSUMPTION W A S T E F O C U S I D E N T I F I E D P R O B L E M S P O T E N T I A L S O L U T I O N S C O N C L U S I O N
- 10. 10 Ideate Graph showing Paretodiagram indicating estimated value of savings per energy project (in %). PE Sites (EMS) Facility LED lighting Change Unit 1 HVAC set-up to recirculation Electrical reheating coils - SVP 1 Solar panels in warehouses Solar Project Reduce refresh rates Johnson Controls chiller Heat Pumps for Hot Water Geysers Set points for chillers Energy-efficient dryers Johnson FX controllers 0% 5% 10% 15% 20% 25% 30% 35% 29% 19% 14% 9% 7% 5% 5% 3% 2% 2% 2% 1% Pareto Chart 102 ongoing site projects reviewed
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- 12. 12 Research methodology “Coal isby far the most common source for electricity production in South Africa. In 2021, the fossil fuel accounted for 84.4 percent of total electricity generation” (Cowling, 2023) www.ststista.com How can reducing energy (electricity) help to reduce carbon emissions ? Under IRP 2019, South Africa aims to have 24.7% of energy production coming from renewable sources by 2030. Reliability on non-renewable providers of energy is likely to be unavoidable for Aspen, not just in South Africa but globally, for many years to come. The energy sector contributes close to 80% towards South Africa’s total greenhouse gas emissions of which 50% are from electricity generation and liquid fuel production alone. IRP 2019. Furnace Boiler Turbine Coal Water Electricity
- 13. Research methodology INTERNET OF THINGS(IOT) ENERGY MONITORING SYSTEM (EMS) ENERGY REDUCTION REDUCED CO2 EMISSIONS Sensors attached to assets interconnect devices at the manufacturing site to feedback real time data Visibility of real time data and monitoring of energy at an asset becomes possible. Data driven decisions can both effectively manage and reduce electricity consumption A reduction in electricity usage contributes directly to a reduction in CO2 emissions What is an Energy Management System (EMS) and how can it help to reduce Co2? Meters / Sensors EMS Actionable Data Reduced Energy & Co2
- 14. 14 Research methodology Benefits ofIOT in reducing energy and Carbon (CO2) Direct benefits Energy-aware process design Predictive maintenance Configurability Reducing energy purchasing costs Improving economics of self- generated / renewable power Cost Management Benchmarking Other benefits ISO5001 Carbon pricing mechanisms Human Resources Savings Environmental / Competitive advantage Scalable
- 15. 15 • W EL C O M E • E M P A T H I S E P H A S E • D E F I N E P H A S E • I D E A T E P H A S E • P R O T O T Y P E P H A S E • T E S T P H A S E • C O N C L U S I O N • Q U E S T I O N S & A N S W E R S Contents • D E F I N E P H A S E
- 16. 16 ALP Project Design Todetermine the optimum Energy Monitoring solution for Aspen to deploy within SA Operations, that will monitor energy and in turn reduce energy consumption and thereby contribute to a reduction of carbon emissions.
- 17. 17 • W EL C O M E • E M P A T H I S E P H A S E • D E F I N E P H A S E • I D E A T E P H A S E • P R O T O T Y P E P H A S E • T E S T P H A S E • C O N C L U S I O N • Q U E S T I O N S & A N S W E R S Contents • I D E A T E P H A S E
- 18. 18 Ideate Table 1 showingAspen SA Operations budget energy usage 2023, per operational area. Budget 23 Usage (KWH) KWH + kVA Rand Value R per kwh OSD 44,062,000 67,893,515 R 1.54 R SVP1 17,468,000 26,915,799 R 1.54 R SVP2 14,502,000 22,345,598 R 1.54 R TOTAL 76,032,000 117,154,912 R 1.54 R OSD + SVP1 & 2 (Actual Energy Usage) FY23 KWH + kVA Rand Value R per kwh Jul-22 5,567,349 11,309,503 R 2.03 R Aug-22 5,893,122 12,303,125 R 2.09 R Sep-22 5,544,077 7,712,817 R 1.39 R Oct-22 6,342,761 8,588,744 R 1.35 R Nov-22 6,018,017 8,308,994 R 1.38 R Dec-22 5,305,369 7,417,170 R 1.40 R Jan-23 6,164,704 8,562,364 R 1.39 R Feb-23 5,638,013 7,370,083 R 1.31 R Mar-23 6,332,575 8,644,058 R 1.37 R Apr-23 5,382,046 7,283,994 R 1.35 R May-23 5,028,124 7,047,935 R 1.40 R Jun-23 5,498,001 11,330,329 R 2.06 R Total 68,714,157 105,879,116 R 1.54 R Table 2 showing Aspen SA Operations actual energy usage 2023, per month overall.
- 19. 19 Ideate Graph showing AspenSA Operations actual energy usage 2023.
- 20. 20 Ideate Table showing estimatedIOT Annual Savings (in Rm).
- 21. 21 • W EL C O M E • E M P A T H I S E P H A S E • D E F I N E P H A S E • I D E A T E P H A S E • P R O T O T Y P E P H A S E • T E S T P H A S E • C O N C L U S I O N • Q U E S T I O N S & A N S W E R S Contents • P R O T O T Y P E P H A S E
- 22. 22 Comparison of solutions Criteriafor comparison of solutions: 1. Integration to existing hardware 2. Integration to ERP 3. ISO compliance 4. Installation time 5. Power requirement 6. Software offerings and reporting 7. Data to information conversion 8. Cloud-based 9. Support of Local resources in SA 10. Scalability
- 23. 23 • Workshops organizedwith Siemens to finalise the User Requirements Specification (URS) • This URS can now be used by all Aspen facilities.
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- 26. 26 Business Case –SVP2 Scenario Assumptions Scenarios Worst Base Best None 0 0 2 3 Low 1 1 2 3 Medium 2 1 2 3 High 3 1 2 3 1 2 3 0 2 3 1 2 3 1 2 3 2 3 3 0 1 2 1 3 3 Marketed Launch Centralised approach: Management buy-in Resourcing (skilled personnel) Change Management Plan Training Energy Management Plan Employee Engagement Communication and Feedback Interface with ERP Systems Advanced Statistical Analytics and AI Energy Management Culture Energy Saving Assumptions by Year: Year 0 1 2 3 4 5 Worst Case 1% 3% 6% 9% 10% Base Case 2% 6% 12% 17% 20% Best Case 3% 8% 15% 22% 25% Base case assumptions supported by sponsor and literature Cost assumptions: • DEG grant 50% of the cost of the solution for SVP2 (1.91m ZAR) • Model just focuses on electricity reduction • WACC 13% • Tax Credits 0.95c per KwH - Energy Saved • Siemens Costings: (Hardware, Software, Training, Licensing, workshops & labour) • 0.9 Kg Co2 per KwH - official efficiency factor Cost benefit model – Base Case Year 0 1 2 3 4 5 SVP2 (Kwh saving p.a) 2% 6% 12% 17% 20% Total KwH 13,136 13,136 12,611 11,350 9,647 Saving KwH 263 788 1,513 1,929 1,929 Saving (Elec - Zar) 407 1,222 2,346 2,991 2,991 Tax (110) (330) (633) (807) (807) Annual Costs (170) (170) (170) (170) (170) Tax Credit 67 202 388 495 495 Cashflows (1,911) 195 924 1,930 2,508 2,508 Present Value (1,911) 172 724 1,338 1,538 1,361 NPV (1,911) (1,739) (1,015) 323 1,861 3,222 Tonnes Co2 236 709 1,362 1,736 1,736 HardBenefits: % KwH Tonnes Years Reduction Co2 Payback Worst 1.91 10% 0.6 21% 1,066 4.15 Base 1.91 20% 3.2 51% 1,736 2.53 Best 1.91 25% 4.5 64% 1,914 2.24 NPV m's zar IRR SVP2 (5-Year Returns) Capex m's zar Soft Benefits: • Compliance with ISO50001 - Energy reporting standard which is likely to become mandated • Carbon Pricing (Scope 1 - 3). Necessary for carbon reporting and carbon taxes • Energy Reduction = Co2 Reduction • Human Resources - Upskill employees • Tender authorities including proof of carbon reduction in tender criteria. • Scalable
- 27. 27 Business Case –SVP2 1 2 3 4 5 0 500 1,000 1,500 2,000 2,500 Co2 Reduction - (Tonnes) Worst Base Monte Carlo Best 1321.48455426741 1593.91843116105 1866.35230805468 2138.78618494832 2411.22006184195 2683.65393873558 2956.08781562922 3228.52169252285 3500.95556941649 3773.38944631012 01/09/1900 01/19/1900 01/29/1900 0 20 40 60 80 100 120 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% MONTE CARLO - NPV Frequency Cumulative Probability 0.301126303530057 0.33022623846309 0.359326173396123 0.388426108329157 0.41752604326219 0.446625978195223 0.475725913128256 0.50482584806129 0.533925782994323 0.563025717927356 01/09/1900 01/19/1900 01/29/1900 0 20 40 60 80 100 120 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% MONTE CARLO – IRR Frequency Cumulative Probability Monte Carlo Analysis: The Monte Carlo (MC) Analysis uses random sampling to analyse and simulate the possible outcomes of our model. Most of the inputs into the model were added to the MC analysis including the yearly savings in each year, rand per KwH, operational growth rates and annual KwHs used at site. Based on our 3 scenarios the MC analysis shows the highest probability results as: • NPV: 83% probability of an NPV of 3.047m ZAR in Year 5 • IRR: 76% probability of an IRR of 47.5% by Year 5 • Co2: 1,833-ton reduction of Co2 by Year 5
- 28. 28 Business Case –All PE Sites Cost benefit model – Base Case 1 2 3 4 5 0 2,000 4,000 6,000 8,000 10,000 12,000 Co2 Reduction - PE Sites (Tonnes) Worst Base Monter Carlo Best Hard Benefits: % KwH Tonnes Years Reduction Co2 Payback Worst 13.21 10% -2 8% 5,071 5.28 Base 13.21 20% 9 30% 9,083 3.38 Best 13.21 25% 13 37% 11,015 3.17 NPV m's zar IRR Capex m's zar All PE (5-Year Returns) Cost assumptions: • No DEG Grant • WACC 13% • Tax Credits 0.95c per KwH - Energy Saved • Siemens Costings: (Software & Licensing quoted) Hardware estimated based on technical advancement of site • 0.9 Kg Co2 per KwH - official efficiency factor • Additional Technical Engineers (1 x Worst, 2 x Base, 3 x Best) Based on our 3 scenarios the MC analysis shows the highest probability results as: • NPV: 81% probability of an NPV of 5.691m ZAR in Year 5 • IRR: 70% probability of an IRR of 22% by Year 5 • Co2: 8,893-ton reduction of Co2 by Year 5 -3137.78038958114 -2208.44849978953 -1279.11660999792 -349.784720206314 579.547169585295 1508.8790593769 2438.21094916851 3367.54283896012 4296.87472875173 5226.20661854334 6155.53850833495 7084.87039812656 8014.20228791816 8943.53417770977 9872.86606750138 01/09/1900 01/19/1900 01/29/1900 0 10 20 30 40 50 60 70 80 90 100 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NPV - All PE Sites Frequency Cumulative Probability 0.0584196595424629 0.0756199486580877 0.0928202377737125 0.110020526889337 0.127220816004962 0.144421105120587 0.161621394236212 0.178821683351836 0.196021972467461 0.213222261583086 0.230422550698711 0.247622839814336 0.26482312892996 0.282023418045585 0.29922370716121 01/09/1900 01/19/1900 01/29/1900 0 10 20 30 40 50 60 70 80 90 100 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% IRR - All PE Sites Frequency Cumulative Probability Year 0 1 2 3 4 5 PE Sites (Kwh saving p.a) 2% 6% 12% 17% 20% Total KwH 68,714 68,714 65,966 59,369 50,464 Saving KwH 1,374 4,123 7,916 10,093 10,093 Saving (Elec - Zar) 2,130 6,390 12,270 15,644 15,644 Tax (575) (1,725) (3,313) (4,224) (4,224) Annual Costs (2,170) (2,170) (2,170) (2,170) (2,170) Tax Credit 353 1,058 2,030 2,589 2,589 Cashflow (13,211) (262) 3,553 8,817 11,839 11,839 PV (13,211) (232) 2,782 6,111 7,261 6,426 NPV (13,211) (13,443) (10,661) (4,550) 2,711 9,136 Tonnes Co2 1,237 3,711 7,124 9,083 9,083
- 29. 29 Business Case Summary •While our model focuses on electricity savings, the Siemens monitoring system is versatile and capable of measuring water, steam, compressed air, and gases. Implementing this system will result in additional savings in these areas through reduced energy usage and waste. • Our projected savings fall within the 10%-30% range commonly reported in academic literature. Our research and interviews further suggest that these energy savings can be achieved within just a few years. However, for modelling purposes, we have extended the timeframe to 5 years. Early savings will expedite the payback period, which, over a 5-year span, are materially offset by a weighted average cost of capital (WACC) of 13%. • We are confident that even under conservative scenarios, the energy savings will offset the initial investment costs. Importantly, the soft benefits of this project may outweigh the financial return on investment. These include: Significant CO2 reductions, addressing the original problem statement. ISO50001 compliance. Carbon pricing advantages. Acceleration of the transition to renewable energy. • The transition to renewable energy is potentially where the most substantial returns will arise. A 10%-20% reduction in energy consumption will not only expedite the shift to renewable energy sources but also significantly decrease the future associated costs.
- 30. 30 Risks & mitigationstrategies People Risk Change Management Skills gap and Resources Training Technological Risks Regulatory Risks Reliability & Maintenance Scalability Mitigation strategies • Energy Awareness and Communication Strategy • Leadership buy in & role modelling • Robust change management and transformation journey • Appointment of the right skills and resources • Appropriate training modules complimented with employee personal incentive and development plans • Solution system functionality and integrate with business ERP • Validation of the system and ongoing utilisation of existing systems
- 31. 31 Change management strategy 8Step change management process Kotter’s 8 step model • Create a sense of urgency Step 1 • Build a core coalition Step 2 • Form a Strategic Vision Step 3 • Communicat e the vision Step 4 • Remove the Barriers Step 5 • Generate Short-Term Wins Step 6 • Sustain Acceleration Step 7 • Institute Change Step 8
- 32. 32 Next steps Key SuccessMetrics 1.
Editor's Notes
- #1 Good morning to our Aspen and GIBS colleagues and coaches and to our invited EXCO members.
- #2 The Action Learning Project problem statement presented to our team, was: “Developing Sustainable Manufacturing Practices for the Pharmaceutical Industry (i.e. Green Manufacturing)” <Click>
- #3 In today’s presentation, group 3 will <Click> kick off with a virtual meet of our team, followed by our ALP Problem statement and our project design. We will proceed to take you through our journey experience of the project phases namely, <Click> Empathise Phase, <Click> Define Phase, <Click> Ideate Phase, <Click> Prototype Phase, <Click>Test phase, and <Click> our project conclusion. <Click> Finally, time for questions and answers will end our session today. Let’s kick off with our virtual team meet!
- #4 Let’s take a look at our Empathise Phase
- #5 Aspen operates 23 manufacturing facilities spread across 15 sites globally. <Click> Our strategic manufacturing sites have international accreditation and our manufacturing capabilities encompass a wide range of product types, including steriles, oral solid doses, liquids, semi-solids, biologicals, and APIs.
- #6 As such, Aspen has an established Sustainability Strategy with 4 key pillars. <Click> Pillar 1 is PATIENTS and focuses on promoting access to medicines by providing a reliable supply of high quality, affordable products <Click> Pillar 2 is OUR PEOPLE and focuses on creating an inclusive, healthy and safe work environment <Click> Pillar 3 is SOCIETY and focuses on operating an ethical and responsible business underpinned by its shared values and governance structures. <Click> And Pillar 4 is ENVIRONMENT and focuses on practising responsible environmental stewardship, seeking to minimise any negative impact its operations have on the environment in compliance with applicable laws, regulations and other environmental management requirements. Whilst we agree that our project influences each strategic pillar from an aspect of macro and interdependent sustainability, the main pillar of focus <Click> is the Environment pillar.
- #7 When we researched Green Manufacturing we identified that this is an extremely vast topic and <Click> thus we decided to undertake a Qualitative Study to further investigate and narrow into a specific problem definition. <Click> Our core research methods focused on obtaining information via <Click> 3 main sources: <Click> - Secondary Data in the form of reports <Click> - Academic Articles in the form of Publications on Google Scholar <Click> - And Interviews in the form of a semi structured questionnaire, performed with selected stakeholders identified with high influence, high interest in our project.
- #8 Using the information obtained from all 3 sources of data, we decided by majority vote, to further explore the areas of <Click> Waste and Carbon for our study. We populated a <Click>, Funnel Diagram to help us narrow our broad topic to a No-Go, Go decision, leading us toward a redefined problem statement. <Click> Our decision criteria used for the No-Go/Go decision was based on whether Existing projects were underway; Anticipated feasibility of the project scope, Feasibility of timescale and Challenges, for each projects.
- #9 Investigation into the carbon stream indicated that projects were available and underway for further development. We identified an opportunity to complete a project that marries energy monitoring and use of IOT as a potential solution to the problem. A project of this nature would meet the ALP brief in terms of feasibility of the project scope and feasibility of timescale. Thus we concluded our final funnel decision as GO on the use of IOT in energy monitoring for impact on energy consumption thereby reducing carbon emissions.
- #10 Using detailed project overview on potential energy projects for Aspen SA Operations, provided from the ALP Sponsor, a pareto diagram was performed to assess which of the energy projects would yield the greatest estimated value of savings (in %). Results of the Pareto diagram indicate that an energy project involving IOT would yield the greatest estimated value of savings (in %).
- #11 Using a 5-WHY problem-solving tool, the Pioneers resolved to a solution of an IOT energy monitoring system which would give visibility of energy consumption at an asset level. Refer to Image 6. 5 Why problem-solving tool, used to identify a solution.
- #12 How does cutting back on electricity consumption affect carbon emissions? In South Africa, the dependence on fossil fuel-generated electricity remains substantial, estimated at around 70%. To put this in perspective, burning just 0.5kg of coal yields roughly 1kWh of electricity, resulting in approximately 1kg of CO2 emissions into the atmosphere. Considering that PE sites alone consume about 70 million kWh of electricity annually, reducing our electricity usage directly translates to a significant reduction in CO2 emissions.
- #13 In the context of our project, the Internet of Things (IoT) will involve connecting sensors to every energy-consuming asset at a site level. Rather than relying on just one data point for energy consumption, we'll have hundreds of them, providing real-time insights into energy usage. This real-time visibility into energy data can be leveraged to efficiently manage and minimise energy consumption. As a result, the decrease in energy usage will directly lead to a reduction in CO2 emissions.
- #14 Using IoT to achieve energy savings offers several key benefits: Energy Awareness: By providing visibility into energy usage through KPIs, dashboards, and energy simulations, Aspen can foster a culture of energy management. Predictive Maintenance: This benefit aims to enhance uptime, lower maintenance costs, reduce equipment failures and energy spikes. Configurability: Machines and batch runs can be optimised for energy usage by adjusting factors such as speed, load, temperature, and pressure. Benchmarking: Assets can be benchmarked against other sites, industry standards, or standard operating instructions. Batch Scheduling: High-energy batches can be scheduled during lower tariff periods or against cheaper sources of renewable energy. Accurate Product Costings: This enables correct commercial decisions to be made. Additional benefits include compliance with ISO50001, the possibility for carbon pricing, eligibility for tax credits, scalability, significant cost savings (as detailed in the ROI model) and gaining a competitive advantage. Notably, authorities in some countries, such as the UK, are implementing evergreen assessments, which give a weighting to companies who can demonstrate they are implementing measures to reduce carbon emissions during the tendering process.
- #16 Using the information obtained in the empathise phase, we thus redefined our problem statement from “Developing Sustainable Manufacturing Practices for the Pharmaceutical Industry (i.e. Green Manufacturing)”, to “To determine the optimum IOT-based solution for Aspen to deploy within SA Operations, that will monitor energy and in turn reduce energy consumption and thereby contribute to a reduction of carbon emissions.”
- #17 Let’s take a look at our ideate phase<Click>
- #18 Budget versus actual spend for energy usage over the period of Financial Year ’23 (FY2023) viz. 01 July 2022 – 30 June 2023, indicated that less energy was used as budgeted for. Refer to table 1. Indicating Aspen SA Operations budget energy usage 2023 and Table 2. Indicating Aspen SA Operations actual energy usage 2023. Actuals for this period are indicative of the positive outcomes of energy projects implemented at the site, resulting in less energy consumption and Rand-value savings.
- #19 Furthermore, actual spend for energy usage over FY2023 indicates that winter months of June, July and August experience more expensive energy (Overall demand for energy during winter months cause a increase in the Rand cost per KWH).
- #20 Financial scenarios were performed to further illustrate the anticipated Rand-value savings from an energy project involving IOT. Scenarios were performed using an estimated 10%; 15% and 20% IOT Annual Savings respectively. Each scenario indicated favourable savings of at least R11.7m.
- #21 <2sec> Let’s take a look at our prototype phase<Click>
- #22 Interviews with two service vendors were performed to identify products that would provide an IOT energy monitoring system and we compared the products using an agreed criteria for comparison. Our criteria included (but not limited to): Integration to existing hardware, integration to ERP, ISO compliance, Installation time, Power requirement, Software offerings and reporting, Data to information conversion, Cloud-based offering, Support of Local resources in SA and Scalability. After a thorough evaluation of the two energy monitoring solutions, Siemens emerged as the preferred option for several reasons. Recognised as an industry leader in the IoT 100 Index, Siemens offers excellent energy monitoring functionality, high accuracy, and robust reporting tools. Key factors influencing this decision included compatibility with existing Siemens hardware, extra granularity in reporting, customisable features, and integration with Aspen’s ERP system. Additionally, Siemens demonstrated scalability for future use throughout the supply chain, making it an excellent choice for Aspen’s energy monitoring needs. As a final a cherry on the cake, the German government have offered funding of 50% towards the project with Siemens.
- #25 Interviews with two service vendors were performed to identify products that would provide an IOT energy monitoring system and we compared the products using an agreed criteria for comparison. Our criteria included (but not limited to): Integration to existing hardware, integration to ERP, ISO compliance, Installation time, Power requirement, Software offerings and reporting, Data to information conversion, Cloud-based offering, Support of Local resources in SA and Scalability. After a thorough evaluation of the two energy monitoring solutions, Siemens emerged as the preferred option for several reasons. Recognised as an industry leader in the IoT 100 Index, Siemens offers excellent energy monitoring functionality, high accuracy, and robust reporting tools. Key factors influencing this decision included compatibility with existing Siemens hardware, extra granularity in reporting, customisable features, and integration with Aspen’s ERP system. Additionally, Siemens demonstrated scalability for future use throughout the supply chain, making it an excellent choice for Aspen’s energy monitoring needs. As a final a cherry on the cake, the German government have offered funding of 50% towards the project with Siemens.
- #26 Now that we have concluded a project roadmap with Siemens, our next step is to finalise the URS and costings. We have a meeting scheduled with Siemens on 7 February 2024 to finalise these details. This will enable us in concluding our business case for this project and execute a pilot study, using this solution.
- #27 Now that we have concluded a project roadmap with Siemens, our next step is to finalise the URS and costings. We have a meeting scheduled with Siemens on 7 February 2024 to finalise these details. This will enable us in concluding our business case for this project and execute a pilot study, using this solution.
- #28 Now that we have concluded a project roadmap with Siemens, our next step is to finalise the URS and costings. We have a meeting scheduled with Siemens on 7 February 2024 to finalise these details. This will enable us in concluding our business case for this project and execute a pilot study, using this solution.
- #29 Now that we have concluded a project roadmap with Siemens, our next step is to finalise the URS and costings. We have a meeting scheduled with Siemens on 7 February 2024 to finalise these details. This will enable us in concluding our business case for this project and execute a pilot study, using this solution.
- #32 Now that we have concluded a project roadmap with Siemens, our next step is to finalise the URS and costings. We have a meeting scheduled with Siemens on 7 February 2024 to finalise these details. This will enable us in concluding our business case for this project and execute a pilot study, using this solution.