Some of the Things We Do in the Design Engineering Program at École Polytechnique

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1 Some of the Things We Do in the Design Engineering Program at École Polytechnique Paul R. Stuart Department of Chemical Engineering École Polytechnique Montréal Apr. 24 th 2013 VCO Webinar

2 Real World Research Questions 2 Cliquez pour modifier les styles du texte Can the fixed and variable costs for different biorefinery strategies be well-estimated, including change-over costs etc? How should the market be segmented for novel functionally-unique biorefinery products? What should be targeted as SC objectives for a particular biorefinery strategy at each of the strategic and tactical/operational levels? How can the potential value and competitive advantage be estimated quickly for several different biorefinery strategies?

3 Real World Research Questions 3 Cliquez pour modifier les styles du texte For a unique biorefinery portfolio of commodity and/or added-value products: What are the decoupling points (MTS/MTO)? What manufacturing flexibility should be targeted? What is the best biomass procurement strategy for different biorefinery portfolios? How can the biorefinery bo e strategy be implemented incrementally, with market and financial success each step?

4 Forest Biorefinery Challenges 4 Cliquez pour modifier les styles du texte Managing g market volatility Biomass Availability and Quality Process Technology Partnership Strategy Product Portfolio

5 Metrics for Evaluating the Forest Biorefinery Supply Chain Performance Behrang Mansoornejad Postdoctoral fellow Research Supervisor Paul R. Stuart Department of fchemical lengineering i École Polytechnique Montréal Apr. 24 th 2013 VCO Webinar

6 Objective 6 Cliquez pour modifier les styles du texte To identify practical metrics that can quantify the flexibility and robustness of a system in a volatile market, and can be calculated at the early stage design using a tactical/operational supply chain model To introduce a conditional value at risk type parameter that can be used for analyzing the risk of sales decisions

7 Presentation outline 7 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions Introduction Problem context Performance metrics Metric of flexibility Metric of robustness Supply chain model Results Performance metrics Conditional value at risk Conclusions

8 Why Forest Biorefinery? 8 Introduction Performance Cliquez pour metrics modifier les SC styles Model du texte Results Conclusions North American forestry companies are in stalemate situation (Forbes, 2000, Joensuu, 2006) Declining market demand over many years Capital intensive industry with small ageing mills and equipment Loss of R&D culture by many forestry companies Volatile/rising energy and fibre costs Competitive advantages (Wising and Stuart, 2006) Access to biomass and harvesting know how Existing infrastructure Established supply chain close to forest biomass for wood, P&P products Potential Biorefinery is a facility that integrates biomass conversion processes and equipment to produce fuels, power, and chemicals from biomass (NREL). Forest tbiorefinery is a category of biorefineries i which h primarily il aims to process woody biomass as raw material, typically in retrofit to existing P&P mills (NREL).

9 Problem context 9 Introduction Performance Cliquez pour metrics modifier les SC styles Model du texte Results Conclusions Supply chain strategic decisions Assess the biomass availability Choose the process technology Find your partners Define the product portfolio Sustainable decision-making Economy IRR??? ROI??? Biorefinery strategies must be flexible in order to be robust to market volatility.

10 Problem statement 10 Introduction Performance Cliquez pour metrics modifier les SC styles Model du texte Results Conclusions Objective: Using metrics of flexibility and robustness To evaluate the performance of several biorefinery design options in a dynamic environment Given: Design options A few number of product/process portfolios Different process alternatives Different SC network configurations Market scenarios, representing market volatility Tool: A supply chain optimization model Calculates the profit of design options for every market scenario Quantifies the flexibility and robustness of each option A conditional value-at-risk parameter To analyze levels l of risk in making sales decisions i To provide required information for profit-risk trade-offs.

11 Metric of flexibility 11 Introduction Performance Cliquez pour metrics modifier les SC styles Model du texte Results Conclusions Flexibility in biorefinery The ability of producing several bioproducts with different production rates in different time periods based on the product price and demand. A justifiable increase in the capital cost compensated with the ability of the process to manufacture with flexibility The metric of flexibility accounts for volume flexibility It shows the deviation of production rate from the nominal production rate for all products/processes in all time periods in a dimensionless form Designing systems Metric of Flexibility ( MF ) Comparing the performance of alternative systems N C mpt C mp C mp t pm N t

12 Metric of robustness 12 Introduction Performance Cliquez pour metrics modifier les SC styles Model du texte Results Conclusions For a robust design, the control parameters of a system are selected in such a way that a desirable measured function does not diverge significantly from a given value (Bernardo et al, 1999). Robustness metrics: Quantifying robustness of SC planning (Vin & Ierapetritou, 2001). Mean, variance, maximum average deviation, standard deviation (Bernardo et al., 2001). Such metrics typically give an average deviation of a variable. Need for a metric that calculate the aggregate deviation from the desired value. Address the downside risk of market uncertainty: profits that are less than the base case profit are considered. Metric of Robustness ( MR) (Pr Pr Sc B PrB Sc ) 1

13 Supply chain optimization model Introduction 13 Cliquez pour modifier les styles du texte Performance metrics SC Model Results Conclusions Thee model ode iss a multi period u t pe od model ode which c considers co s de s the t e management a age e t o of a multi product, u t p oduct, multi ut echelon SC. Customer 1 52 periods of one week The variables and objective function calculated weekly over a year Orders: by contract and on the spot Different types of feedstock provided id d by b severall suppliers li The steam produced by both fuel and biomass Volume flexibility: Production level varies between certain boundaries Processes can produce several product using different recipes One recipe in each period Changeover time and cost are considered Supplier 1 Customer 2 Customer 3 Supplier 2 Customer 4 Supplier 3 Customer 5 Customer

14 Supply chain optimization model 14 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions Variables proc lprt proc lprt ord l l mt sales f lkmt period t rec h lprt : Selection of a recipe during a time period t : Selection of a recipe that is used in consecutive time periods : Selection of order sup f jlmt : Flow of material between locations (supplier, mills and customer) during time : Number of hours spent on a process recipe during a time period t x proc lpmt y proc rec lpmt y lprmt : Consumption/Production of material m using recipe r on process p during time period t mat S lmt : Inventory of material during time period t w lpt v input output output input lpt v lpt w lpt : Consumption/production of steam and electricity on process p during time period t

15 Supply chain optimization model 15 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions Objective function Revenue Electricity cost Sales cost Variable operating cost Fixed operating cost Changeover cost Shutdown cost Transportation cost Storage cost Procurement cost

16 Supply chain optimization model 16 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions Constraints Procurement and demand Transportation Inventory Recipe

17 Supply chain optimization model 17 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions Production Volume flexibility Steam generation Electricity generation

18 Targeting the level of flexibility 18 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions Thermochemicals diesel Feed Feedstock Handling Feedstock dryer Biomass Gasification SynGas GTL Product Separation 100 Profitability - Levels - of of Flexibility - Market - Scenarios Capital Cost vs. Level of Volume Flexibility waxes Capital Cost ($MM) ROI (% %) Profit ($ $MM) Volume Flexibility Volume Flexibility (%) (%) Optimistic Sc.5 Optimistic Sc.1 Sc.6 Sc.7 Sc.4 Sc.5 Sc.9 Sc.1 Sc.8 Sc.8 Sc.4 Sc.7 Sc.6 Sc.9 Pessimistic Pessimistic 0 Mar rket Scenario Mark ket Scenario Volume Flexibility (%) 0

19 Comparing design options 19 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions Thermochemicals diesel Feed Feedstock Handling Feedstock dryer Biomass Gasification SynGas GTL Product Separation waxes A 1 diesel Hydro treating Jet fuel A 2 Blend tank diesel Hydrotreating Jet fuel A 3 Hydro treating Jet fuel diesel Hydro treating Jet fuel

20 Comparing design options 20 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions Biochemicals Succinic/ Malic acid Lactic acid Lactic acid Succinic/ Malic Succinic/ acid Malic acid

21 Comparing design options 21 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions Thermochemicals Biochemicals Profitability (ROI) 27% 25% 23% 21% 19% Robustness and Profitability vs. Flexibility A 2 A 1 A ess Robustne Profitability Robustness Profitabilit ty (ROI) 34% 32% 30% 28% 26% Robustness and Profitability vs. Flexibility B 1 with no flexibility B 1 B Robustn ness Profitability Robustness 17% Flexibility (%) % 0% 14% 17% Flexibility 1

22 Conditional Value at Risk (CVAR) 22 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions CVAR aims at guarding gagainst realization of uncertain parameters by going beyond the expected evaluation when expressing the uncertainty of system parameters. A constraint is added d to the optimization i i formulation The contractual order acceptance percentage (OA) should be bigger than a risk factor. A high OA implies less risk. Contractual orders are fixed in price and amount over the long term. Lower OA connotes more spot orders which might cause profit increase, but poses higher risks, as spot demands are not certain.

23 Conditional Value at Risk (CVAR) 23 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions OA% Sc.1 Sc.2 Sc.3 Sc.4 Sc.5 Sc.7 Sc.8 Worst Robustness Profit 22% % % % % % % Average Profit and Robustness vs. Contractual Order Acceptance Percentage Robustness SD ($MM) Profit ($MM) Robustness Profit % 40% 60% 80% 100% Contractual Order Acceptance (%)

24 Conclusion 24 Introduction Performance Cliquez pour metrics modifier les SC Model styles du texte Results Conclusions The developed metrics can very well quantify the operational performance of a system in terms of flexibility and robustness. They can be used for Targeting the design of a system Comparing different alternatives It can be shown up to what level of flexibility, profitability is improved. It was shown that increasing flexibility will make the system more robust to market volatility. Such metrics can be used along with other economic metrics. The CVAR studies show that optimum percentage of order acceptance is different for each market scenario. Lower risks may imply lower profit and thus, an appropriate trade off analysis ought to be performed to choose the right percentage of order acceptance.

25 25 Thank you! Apr. 24 th 2013 VCO Webinar