Bharath CN

Domain : Manufacturing : Oils and Gases Context : In Air separation unit the process is maximum hazardous and very sensitive, always on trigger of process failure(Even in stable condition) even due to small errors and internal noise or external noise, this leads to high risk of safety to the Employee, Environment, Surrounding and assets this is safety concerns due to critical characteristic of products and process, sensitive operations of high pressure and temperatures and other parameters. also the On supply to On site customer is so critical it’s mandatory to be on top and vigilant in managing the process and plant consistently. Intent : The intent was to build stable Artificial Intelligence Predictive and control Model to ensure high ‘’Safety and customer service and performance’’ even from start of the process and then maintaining the End to End stable process parameters which leads to better temperature and flow distribution and pressure ratios to attain the desired cryogenic product out put. HOW and what considerations are made to build the below AI Model : AI has emerged has assistant, guide and consultant to review the present process conditions been operated based on real online data and analyse that in real time in few seconds and make the suitable decision to the bring the process bias, to reflect the process output the intended output for highly safe and to ensure high Safety and customer service and performance To build the trust on AI Model by the operator, process operation team we have involved then in the design, considered all technical details, design conditions of the End to End process Lets say from high capacity Air compressors and high capacity Turbines and communication flow to End to End stake holders and taken all suggestions that would call a need for inclusion in the AI Model as Operators and Process Owners being the face of the process and close to reality they know the process very well. Major methodology Executed. Risk analysis, Brain Storming, Suitable best considerations were made, solutions were identified & Evaluation. The solution was built, tested. Simulations were done with involvement of Shop floor operators and Process Owners Ensured trust building and empowerment to Shop floor operators and Process Owners by involving them in End to End development and till Go-Live Commissioning. Control Measures taken to sustain the implementation and developed Trust New KPI & Redefining KPI suitable to upgraded performance with AI consideration. While on deployment the key question we had was ‘’ How Should Performance Metrics Change When AI Becomes Part of the Workflow?’’ Redefining the Performance KPI and it’s review and defining the New KPI’s We used the concepts of DMAIC project methodology of each phase that needs to define baseline performance of each KPI, measurements and redefining KPI and new KPIs creation to ensure ‘’control’’ in place to ensure the stability and capability in parameters and process. Approach : Brainstorming : As a first step we have conducted brainstorming to identify the possible ideas for what KPIs to be redefined and structured or identify the New KPI. Shortlisted the needed KPI using Multi voting and rated by experts and process Owners. Defining Base line Performance : Comparing with baseline performance from previous performance : as the process improved by AI Predictive Modelling Example : The Baseline performance of the Power Consumption of Air Compressor was 5200 Kwh, in other wards 185 kWh/t of oxygen produced, This baseline has to be redefined to 5132 KWh as first level change, 168 Kwh/t as the performance of air compressor is increased with reduced power consumption due to pressure profile distribution/reduction. Measure phase concept : MSA (Measurement System Analysis) performed for the all sensors on Logical validation, calibration were done to have accuracy in measurement, also verified and did the re-settings on Pressure Control valves on Lower and higher SPAN to ensure the Pressure Control valve operation to the fine tuned small variations provided by AI Predictive Model. Example : Earlier Turbine discharge Pressure Control valve was operating at 4.5 to 4.8 barg with new performance the Pressure Control valve was set and tuned for Lower span of 3.9 barg. Analyse Phase concept : Existing Process Failure mode Analysis was revisited, new RPN were calculated and New list of high priority failures were identified with New failures were also identified in concurrence with Operators and Process Owner team to look for new controls, Especially by applying POKA YOKE. POKA YOKE Methodology were redefined for all the necessary parameters to ensure the safety of the system and keeping process and people and asset safety as the priority. POKA YOKE ‘’Warning’’ : Example settings of existing ‘’Alarm’’ were redefined to new AI Predictive Model results and process change. POKAYOKE ‘’Control’’ : The Pressure, temperature transmitters SPAN settings were changed to bring in more Controls from existing. New measurements points were identified and installed during plant shutdown. POKAYOKE ‘’Shutdown’’ : The shutdown settings were reviewed based on new AI process requirements so that process and plant goes on shutdown when the AI doesn’t predict the internal or external noise which could have impact on safety and leads to undesired or uncontrollable process deviation. Control Phase concepts implemented : Included the new baseline, new specification limits, target, measurements MIN and MAX settings Frequency of New Alarm count was included so that to track how many times process is deviated. Communication and reports are ensured to get circulated to all concerned functions and team. 3 shifts technical support was ensured until the performance was established across all team members of all shifts. Conclusion : AI Model provides solutions which is probably close the stable process as needed by the standards but still there are strong workflow to be deployed with End to End thought process to avoid and to address any deviation or failure so that process runs with stable and capable enough to deliver Safety as first priority and for better customer service with high efficiency within the process.

Domain : Manufacturing : Oils and Gases Context : In Air separation unit the process is maximum hazardous and very sensitive, always on trigger of process failure(Even in stable condition) even due to small errors and internal noise or external noise, this leads to high risk of safety to the Employee, Environment, Surrounding and assets this is safety concerns due to critical characteristic of products and process, sensitive operations of high pressure and temperatures and other parameters. also the On supply to On site customer is so critical it’s mandatory to be on top and vigilant in managing the process and plant consistently. Intent : The intent was to build stable Artificial Intelligence Predictive and control Model to ensure high ‘’Safety and customer service and performance’’ even from start of the process and then maintaining the End to End stable process parameters which leads to better temperature and flow distribution and pressure ratios to attain the desired cryogenic product out put. HOW and what considerations are made to build the below AI Model : AI has emerged has assistant, guide and consultant to review the present process conditions been operated based on real online data and analyse that in real time in few seconds and make the suitable decision to the bring the process bias, to reflect the process output the intended output for highly safe and to ensure high Safety and customer service and performance To build the trust on AI Model by the operator, process operation team we have involved then in the design, considered all technical details, design conditions of the End to End process Lets say from high capacity Air compressors and high capacity Turbines and communication flow to End to End stake holders and taken all suggestions that would call a need for inclusion in the AI Model as Operators and Process Owners being the face of the process and close to reality they know the process very well. Major methodology Executed. Risk analysis, Brain Storming, Suitable best considerations were made, solutions were identified & Evaluation. The solution was built, tested. Simulations were done with involvement of Shop floor operators and Process Owners Ensured trust building and empowerment to Shop floor operators and Process Owners by involving them in End to End development and till Go-Live Commissioning. Control Measures taken to sustain the implementation and developed Trust While on implementation we had the question ‘’How should responsibility be defined’’ to ensure the recommendations from AI predictive model is followed and ignored, what necessary actions to be taken and by whom, who is responsible, accountable, Concerned and Informed. AI has emerged has assistant, guide and consultant to review the present process conditions been operated based on real online data and analyse that in real time in few seconds and make the suitable decision to the bring the process bias, to reflect the process output the intended output. But the question remains is if ‘’The AI’s recommendation is followed, and things go wrong?’’ How should the plan of action to be taken & who should be responsible for it, What could be best RACI ? & The AI’s recommendation is ignored, and things go wrong? As AI Model doesn’t provide a definitive Fix, it’s not a solution or application which is not affected by any External or Internal Factor and it’s not one time installed or invested and forgotten. It’s to be tracked and treated as element of evolution and a process of Continuous improvement and evolution really holds good for this to assure system should not fall behind when the AI solution keeps merging with new horizons. Below Structured approach was deployed : Tools and System : Below tools and systems were implemented to find the true structure approach so that team is not lost with lots of information, change and new updates and would help to follow the simplified necessary steps, eliminate Non value addition, avoid confusions, blame, or risk-avoidance behaviour or perform the activities with risk. System & Standards : RACI Matrix : RACI Matrix was defined and developed to ensure the structured flow to take the actions, by whom, who is responsible, accountable, Concerned and Informed. This was evaluated with cross functional heads and team members, communicated end to end on RACI so that all are aware and aligned. SOP : Standard Operating Procedure : SOP revisions with new versions : Updated Step by Step SOP are created for each process lines and considered new operating conditions with AI predictive Model mentioning, below important detailed needs are incorporated. When to take actions when things can go wrong with AI Proposal and AI Proposals are Ignored When to Ignore the AI recommendation ? When to override and take the control in manual ? How to treat, What actions to be taken when the Alarm given by AI predictive system ? What to actions to take When the sudden and unexpected internal or External Noise appeared in the system ensure ? Standards : Standards were created Standard Documents creation and approvals as per approval hierarchy like creator, reviewer and validation, approver Review and revisions for new versions with updates so that change is tracked. Tools & Systems : ONE DOC : Application/Software to manage the Standards was implemented, with ‘’Definitive Identification Number’’ this has ensured the live availability of right standards documents in the Library to the Process Owners Alert system & Action Log Books & Reporting : Implemented specific action Log book via Online Share point action log book, in which Operators and Process Owners mention the alarm, deviation from AI system and what actions were taken, remarks for the difficulties faced, also the need of further evolution in the AI program to Nullify the error or Noise,, or to mention the Idea if any ? Share point (Online) : Online Share point action log book, in which Operators and Process Owners mention the alarm, deviation from AI system and what actions were taken, remarks for the difficulties faced, also the need of further evolution in the AI program to Nullify the error or Noise,, or to mention the Idea if any ? MOC : Management of Change Management of Change is ensured and given a clarity and facilitated the Live updated Document at any point of time, this has helped to avoid confusions, blame, or risk-avoidance behaviour. Conclusion : AI Model provides solutions which is probably close the stable process as needed by the standards but there are high chances also that AI suggestions needs to be thought about and questioned to avoid the failures due to possible wrong suggestions provided by AI by not taking Noise in to considerations or additional necessary prompt in it’s background verification and suggestions. So End to End thought out Structured process is to be implemented as mentioned above to ensure no confusions, mistake, blame game, to held accountable and responsible, to keep the risk behaviour in control by the AI system also by human.

Domain : Manufacturing-Oils and Gases Context : In Air separation unit High Capacity compressor and High Capacity turbine are very sensitive equipment which operates at very high pressure and high speed, temperatures and expansion ratios, here the key is how this high precision process to be operated stable absorbing all the noises external and with in process noise and deliver a consistent parameters output values so that requirement of process is met, unless these this condition is met the process control always unpredictable and with quality variations. Intent : The intent was to build a Artificial Intelligence Predictive and control Model even from start of the process and then maintaining the End to End stable process parameters which leads to better temperature and flow distribution and pressure ratios to attain the desired cryogenic product out put. It was clear decision and direction of business to deploy a BB Project to achieve the first level of consistent Sigma value of grater than or Equal to 3.0 and later to review on improving further. HOW and what considerations are made to build the below AI Model : AI has emerged has assistant, guide and consultant to review the present process conditions been operated based on real online data and analyse that in real time in few seconds and make the suitable decision to the bring the process bias, to reflect the process output the intended output. To build the trust on AI Model by the operator, process operation team we have involved then in the design, considered all technical details, design conditions of the Air compressor and Turbine and communication flow to End to End stake holders and taken all suggestions that would call a need for inclusion in the AI Model as Operators and Process Owners being the face of the process and close to reality they know the process very well. When Should People ''Trust'' an AI’s Recommendation : I will give a real example : The operators and shop floor people are the owners of the process, they believe only when the AI model delivers a result which is as expected by them for their process objectives and centre line management KPI given for each parameters. First Confidence with Simulation : So considering this point we have engaged the shop-floor team and operators in design and development to assure that how its build to take them in trust first, with their suggestions and then the same Operators and process owners were participated in simulation of the AI model, They run by themselves seen the output and given the feedback for corrections and fine-tune, many times they accepted the results also. the expected results in the same. ''First Trust'' after 2nd-Simulation & Dry Run : The logics and model were corrected based on End to End review and asked the Operators and process owners re-perform the simulation once again in presence of technical experts, The results to operators and process owners found to be favourable. ''Evident proof of trust'' on AI Model during Commissioning and Go-Live & Post Go-Live : The plant, Air compressor and Multiple turbines were started and taken in line with AI Model predictive & Control system, Found to be favourable with expected desirable result and stable process parameters with in control limits, observed the simulation nodes and possible deviation would lead to failure and the bais, taken control to verify the observed deviation optimized further for the consistent stability of parameters. Positive Business outcome : For Air Compressor : Achieved the Process capability of 1.25 and with a sigma Value of 3.75 For Turbine : Achieved Process capability of 1.42 and with a sigma Value of 4.26. Customer satisfaction due to improved product quality, due to consistency in Online supply to customer. Savings in Power consumption Savings idle running of equipment and deterioration. Realization of product soon after few hours of Factory start up. Measures taken to sustain the developed Trust : Regular Audits & AI performance review Documentation until complete sustenance System alarm tracking/decision lists of shifts MOC, Management of Change method implementation Clear accountability (list of monitors, approver, in charges, especially on Logical changes) Review for bias, fairness, and Legal & regulatory compliance Maintain deviation logbooks for each parameter, failures against the target values AI Model performance review, correction, maintenance and development : Review the performance of the AI system on continuous basis with team and adapt the necessary change to be brought in to the model new dynamics. Take all the learnings from the feedback given by the alarms of system and people. Training and communication Ensure each and every change in the model is communicated to the end to end team members and provide full necessary training on the actions to take. Example, if AI Model is to be over rided by Manual control or visa versa from manual control to AI Model, Ensure to provide hands on experience. 2nd part of the Question : When Should the operator and process Owners should Override AI Model. AI Model doesn’t provide a Permanent Fix, it’s not a solution or application which is not affected by any External or Internal Factor, so considering this we have designed ‘’alarm’’ system which AI predicts the deviation which is out of it’s control and informs the user to take necessary action either online with AI or by taking AI in OFF Mode and override manually to control the process until the External or Internal factors are nullified and then take back the AI Model in line once the noise factor really disappeared. One of the example of External Noise factor for which AI Model has to be taken off and Manual Human Override has to happen. AI Model can’t accommodate the dynamic process variation of downstream or upstream caused due to sudden Pressure fluctuation by control valve malfunction, power frequency variation. This surge in Power frequency or Power factor leads sudden dip in Compressor pressure, RPM and Turbine RPM and expansion ratio. During the above situation if the process is not taken in control sure it leads to destable the whole plant , plant will shutdown different out of control settings, so It’s necessary to take the process out of AI Predictive control and operate manually to bring the process under control and then after manual stabilisation give back the control to AI Model. Conclusion : AI has emerged has assistant, guide and consultant to review the present process conditions been operated based on real online data and analyse that in real time in few seconds and make the suitable decision to the bring the process bias, to reflect the process output the intended output. But AI Model doesn’t provide a Permanent Fix, it’s not a solution or application which is not affected by any External or Internal Factor and it’s not one time installed or invested and forgotten, it’s to be tracked and treated as element of evolution and a process of Continuous improvement and evolution really holds good for this to assure system should not fall behind when the AI solution keeps merging with new horizons. As additional details : How above AI enabled Model was developed and the major steps followed as below : Risk & Bias : The whole process was studied for Severity of failure Air compressor and Multiple Turbines, occurrence and detection, especially on detection failure at each step of the process and instruments while incorporating the logics for predictive model, bias limits were identified, precision modelling were used to arrive at the accuracy of the flow meters and control valves, power and power factor tuning for ramp up and ramp down. Model definition : AI predictive Model was developed by considering historical manual operations data, Risk thresholds, keeping continuous sustainable focus with Model metrics, logical validation, establishing stability and calibration of all instruments and analysers. Simulation & Dry Run : The developed model was tested on dry run simulation for Air compressor and Multiple Turbines to see the model is a best fit and need of correction and fine tuning for desired output, simulation run was executed to identify all the predictive proposal based on assumption for the defined out put of each process parameters, power settings, pressure out outs, temperature, speed of turbines, inlet and out let pressures, temperatures, Valves, pressure relief valves, sensors with the reduced bias. Process conditions of Air compressor and multistage turbines and instrumentation output was measured and noted, temperature and pressure requirements at each process/instrument outputs, logics were corrected, replaced, fine tuned for desired output. Commissioning and Go-Live & Intended sustenance overtime : The plant was taken in line with AI Model predictive & Control system, observed the simulation nodes and possible deviation would lead to failure and the bais, taken control to verify the observed deviation optimized further for the consistent stability of parameters of Air compressor and Multiple Turbines and achieved the Process capability of 1.25 and with a sigma Value of 3.75 and for turbine parameters achieved Process capability of 1.42 and with a sigma Value of 4.26.

Domain : Manufacturing-Oils and Gases Problem statement : Air separation manual product process line up after start of plant is taking 32 hours instead of 18 hours, leading to production loss by 8 T with an wastage of electrical consumption of 405 kwh/T, which corresponds to 3240 KWh loss. This subject is been a pain point for on-site customer supply and delay in supply due to late product delivery would impact the on-site customer and leads to penalty. The intent was to optimize the manual product line up with all the expertization and data mining to build a AI model with predictive control which smoothen and increase the efficiency of the product line up. AI Enabled Predictive Model for Process start up and product line up : Risk & Bias : The whole process was studied for Severity of failure, occurrence and detection, especially on detection failure at each step of the process and instruments while incorporating the logics for predictive model, bias limits were identified, precision modelling were used to arrive at the accuracy of the flow meters and control valves tuning for ramp up and ramp down. Model Definition : AI predictive Model was developed by considering historical manual operations data, Risk thresholds, keeping continuous sustainable focus with Model metrics, logical validation, establishing stability and calibration of all instruments and analysers. Simulation & Dry Run : The developed model was tested on dry run simulation to see the model is a best fit and need of correction and fine tuning for desired output, simulation run was executed to identify all the predictive proposal based on assumption for the defined out put of each process parameters, Valves, sensors with the reduced bias. Cycle time reduction at each process and instrumentation output was measured and noted, temperature and pressure requirements at each process/instrument outputs, logics were corrected, replaced, fine tuned for desired output. Commissioning and Go-Live & Intended sustenance overtime : The plant was taken in line with AI Model predictive system, observed the simulation nodes and possible deviation would lead to failure and the bais, taken control to verify the observed deviation optimized further for consistent out put and supply to customer. Positve Business outcome : Cycle time efficiency was reduced by 3.8 hours on the first opportunity and with further optimisation in successive start ups cycle time reduced to 6.5 hours consistently. Cusromer staisfaction due on on-time delivery Savings in Power consumption Savings idle running of equipment and detiriration. Realization of product soon after few hours of Factory start up Now for the above practical case, the question is how do we sustain this AI enabled process Intended to work overtime : Though the AI model matrix is designed and developed on previous manual vast data and live realistic data extracted from ASPEN system, FOXBORO to meet the real auto situation, still it may miss the sustenance or consistency without inclusion of real scenarios in physical process, also user settings given by operator or team on live process, how AI Predictive model consider’s that manual live commands correction with real time predictive correlation/correction, sometimes user corrections and sudden real time process prediction/alarms from the downstream process would generate low confidence output from AI Model. Here is an example, which is even out of human control : AI Model can’t accommodate the dynamic process variation of downstream or upstream caused due to sudden power fluctuations. During these times It’s necessary to take the process out of AI Predictive contol and operate manually to bring the process under control and then after manual stabilisation give back the control to AI Model. With evolution, AI model should be incorporated with highlights that immediate correction of response for high risk and also ensures high detection to keep the severity of failure as low as possible. Regular Audits & AI performance review Documentation until complete sustenance System alarm tracking/decision lists of shifts MOC, Management of Change method implementation Clear accountability (list of monitors, approver, in charges, especially on Logical changes) Review for bias, fairness, and Legal & regulatory compliance Maintain deviation logbooks for each parameter, failures against the target values AI Model performance review, correction, maintenance and development : Review the performace of the AI system on continuous basis with team and adapt the necessary change to be brought in to the model new dynamics. Take all the learnings from the feedback given by the alarms of system and people. Training and communication Ensure each and every change in the model is communicated to the end to end team members and provide full necessary training on the actions to take. Example, if AI Model is to be over rided by Manual control or visa versa from manual control to AI Model, Ensure to provide hands on experience. Conclusion : AI Enabled process is not one time installed or invested and forgetton, it’s to be tracked and trated as element of evolution and a process of Continuous improvement and evolution really holds good for this to assure system should not fall behind when the AI solution keeps emerging with new horizons.

Domain : Plant based ingredients Manufacturing, high volume production Context : We manufacture Plant based ingredients with key head products and many by-products in high volume while processing high volume of agriculture crop as raw material. The process is of complex and the products are produced in dry form and liquid form. For the products produced in dry form has complex process of drying and concentrated liquid streams addition to recover different streams as saleable product with specified moisture limits. Condition of the Process : While ensuring recovery of one of the internally generated concentrated liquid stream. It’s observed that there is an increase in moisture by increasing the 50% concentrated soluble liquid while other parameters were constant though there is an increase it's was evident that any variation or excess addition would lead to high variation in product moisture from 4.5% to as high as 15%. the high moisture and high variation in moisture is leading to jamming of silos and long down times, reliability issues and impacting OEE. Why and how this really add value to process with other benefits to Organisation : Stable increase in moisture by 2.5 % from earlier average of 5.5 % to 8.0 %, means increase in Quantity by 4.5 T/Day Improved Yield by 1.8 %/2.5 T/day by stable addition of Concentrated liquid stream addition, Increased performance of workshops : OEE by 4 %+ Due to constant settings on other parameters, there are few more tangible benefits like, savings on steam consumption, power savings on constant air flow, Etc.. Dryer downtime reduction to 8 hour/month from 38 hours/month Improved safety across shifts, reduced the stress and workload for the operators and production team This opportunity was observed in May-2024, As a reactive approach, We planned to roll out a DMAIC project and used ''Hypothesis testing in Analyse Phase'', through which identify a suitable spot or corrélation to establish a constant settings for 50% concentrated soluble addition. Before mentioning about AI Involvement, let me give a real example from our manufacturing Process where we preformed Hypothesis testing. Hypothesis testing used to find the ''statistical significance'' and then to arrive at the ''practical significance'' to generate the solution and solution validated in Improve Phase. STEP 1 : With the sample data > than 30 did the first check, descriptive analysis with Box plot was done, didn’t find the difference and not conclusive we decided move on to inferential statistics through Hypothesis testing Step 2 : To choose the appropriate Hypothisis test and formulate the H0 and Ha The Assumption for Hypothesis testing was consucted. The quantity addition of 50 % concentrated soluble liquid stream has positive impact on Product Moisture variation > or Equal to 380 ltr/hr - ''H0'' The quantity addition of 50 % concentrated soluble liquid stream has positive impact on Product Moisture variation < 380 ltr/hr - ''Ha'' Here in this step MBB rethink and data credibility when AI Involved : Here MBB should take the opinion by the process expert and team to validate via shop-floor reality not by the AI inferences. STEP : 3 Define the Alpha for above assumption, Alpha at 0.05 If p is less than α, reject H0 and accept HA If p is greater than α, accept H0 and reject HA Here MBB should take the reference of process difficulties, customer needs and criticality before finalising the Alpha value for the Hypothesis while reviewing the AI results. STEP : 4 As the Output ‘’Y’’ is the Continuous and comparing one sample test with a target, To compare mean sample data with target value, found data was normal and has the standard deviation. Based on above reference the ''1 sample Z Test'' was conducted in minitab. Normality test : Peformed the normality test of the data and data found to be normal with ''P value 0.531'' which is greater than Alpha. Step 5 : Statistical conclusion As P value at ''0.531'' greater than Alpha 0.05, Accept the H0 saying that The quantity addition of 50 % concentrated soluble liquid stream has positive impact on Product Moisture variation > or Equal to 380 ltr/hr - ''H0'' Step 6 : Practical Conclusion and Business Decision Based on the above inference we come to a conclusion that any 50% concentrated soluble addition more than 380 ltr/hr rate would increase the moisture by keeping rest the dryer process control parameter constant. As next step we conducted for the Practical significance with linear regression analysis, the equation Y= M+(b*X) got was for one of the data was : Moisture % = 1.6 + (0.0068*Soluble addition) between the parameters with Soluble addition and moisture are found that the 2.33 % moisture could be increased stably with additional 108 ltr/hr more to the dryer by keeping rest of the parameter constant. MBB rethink & data credibility with AI Involvement : AI provides hypothesis based on its study in data pattern, correlations and past examples and often without practical reality or shop-floor reality. Hypothesis testing is clearly manual and performed by team and to be validated by strong expert/team review. AI provide the inner version of the data but the MBB along with team should come out with true Hypothesis conclusions and cross verify the result provided by AI and surety of data patterns, means the validation based on decision specific cross verifying the AI inferences. MBB succeed by cross verifying the data, decision, pattern or Correlation understanding given by the AI and not by just blindly accepting what AI populated. An advantage of AI also should be cross verified by the team under the suggestion of MBB though the all the inferences given based on multiple parameters analysis, like for example dryer inlet and outlet temp or air flow & steam in and out temps or feed and product outlet parameters. This avoids the over trusting of data makes a thread with Physical process constraints and physical performance.

Domain : Plant based ingredients Manufacturing, high volume production. Context : We manufacture Plant based ingredients with key head products and many by-products in high volume while processing high volume of agriculture crop as raw material. The process is of complex in it's nature in separation of components from raw material as main product and By-products. Condition of the Process : The Yield of a prime byproduct was 4.5 % against 5.5 %+ this yield is of one of the 5 different products produced out of primary rawmaterial from different workshops. Interesting factor yield of one product is can be lost in to other product stream out of 5 and which is loss on yield of one product and increase in yield of another and this is not a straight number, each product price is different example for product 12000 INR/T where as another 52000 INR/T, 18000 INR/T Etc,, when loss of yield happens to another it impacts the quality of product and revenue. The other major concern was product loss to effluent treatment plant (not recovered as product) and impacting it's performance by increased solid load. Goal/Improvement initiative: Increase the Total Yield by Increasing and optimising the Yield of every product to it's standard Yield from 4.5% to 5.5 %+ by deploying a DMAIC project. Why and how this really add value to processwith other benefits to Organisation : Improved Yield by 1.0 % Increased performance of workshops : OEE by 2 to 3% Increased revenue with respect to each product : in total 1.2 Cr/month Environment : Reduced Effluent generation by 500 m3/day Reduced water consumption : 50 m3/day Reduced Over processing and solid waste generation by 1.5 T/Day Reduced transportation and motion to and fro to warehouse due to reduced waste generation. Increase in standards of work, ease of operation to shop floor people. Aim for 5S workplace. This opportunity was observed in May-2023, As reactive approach We planned to roll out a DMAIC project. Define Phase : As part of Define phase Business case, Goal statement (SMART) and problem state, Team members of 8 including process expert and with a time line of 6 months were identified in Live project charter. Business case : The Yield of 5.5 % from current yield of 4.5 % would increase Revenue of 1.2 Cr/month, not doing this is a loss of revenue, loss of product to waste, effluent, OEE < by 2-3%, transportation and motion loss in between production and warehouses. This increase in Yield has to be implemented by Dec-2023. Goal statement : To Improve the Yield of Ingredient process from 4.5% to 5.5 % by Dec-23 Problem Statement : Very low Yield of 4.5% in place of 5.5 %+ since 3 months (Mar-23 & May-23) leading to loss of 350 T of product and corresponding impact on revenue of 1.2 Cr/month Team Members : 8 people including process expert, Production operator, supervisors, process owner, maintenance/instrumentation team member Project Scope : SIPOC : Identified all In scope, out scope, Supplier customer of the process were identified with respect to each products and by-products. Overview of process flow were documented so that each product process were fully considered to identify the loss of yield in each step and opportunity to improve. Toll gate review was done to ensure alignment with organizational goals and the strategic direction of the ‘business, goal statement existed that defined the results expected to be achieved by the process, Problem statement were clearly defined and measured interns yield % Measure Phase : More detailed process flow map was defined to map the ingredients main product and by product and water/solid extraction streams Theoretical versus present operating solids with water distributions data were gathered for each respective process stream MSA Gauge R & R was done for main product stream and few by-product stream, Gauge R&R % was <8% and contribution % was < 3 % and part to part was high for main ingredients and for one of the by-product was gauge R&R was >42 % and contribution was above 10% so it was rejected, measurement machine was replaced and few were calibrated and retested the Gauge R & R. Few parameters Logical validation was done. Measured the stability and capability of the process parameters of decanters RPM, Pressure and flow settings , temperature, Dry solids % and on other parameters like water ratio settings, feed flow Vs each stage pressure settings (few parameters of the chart are Xbar S and found to be with out lairs and unstable) Analyse Phase : Conducted the Root cause analysis (Fishbone analysis) to identify the potential causes of poor yield at each stage of the process, identified causes for poor separation of streams and reasons for cross over of solid yield, further continued to find the critical X's using Pareto analysis, scattered diagram and multiple regression analysis to identify the critical X's between the flow, pressure, Solids % in the stream, RPM of the decanter, overflow and underflow parameters. Trails conducted to improve the R2(adj) value above 70% and closer to 90% DOE was modelised for the by-product stream on a decanter to separate concentrated high protein above 60%, it was 4 factorial design, with interaction plot and contour plot found two parameters were in strong interrelation, Flow and RPM in strong relation with feed solids %. Improve Phase : Based on the analyse phase inferences, the solution was generated on decanter parameters through DOE optimisation data of contour plot and interraction plot. optimized value settings were also derived from DOE regression Equation Solutions were generated by Brainstorming and solution evaluation done by multivoting and expert and process team, operators validation Evaluated for VA and NVA steps in process maps and optimized, identified one of the process step called as humidification was not in working condition, it was taken in line for operation. Control Phase. DOE Analysis and data, Contour plots, interaction plots, optimization settings chart and check lists were displayed as visuals in shop-floor and operator control room. MSA results were displayed and explained to production and quality team to have sustenance of performance Detailed control plan for each X's and Y on Critical X's list and outputs were tabulated with specification, target, responsibility, quality Spec data to maintain and sustain. Assessed the stability and capability of renowned process celebrated the success of the project achieving 4.93 % at the end of 6th month and 5.38% by 7th month and continued to sustain the improvement. Shared this project as best practice with rest of the plants, especially the DOE results and learnings on Protein production. AI can reshape (but does not replace) the DAMIC method. Financial gains were validated by Finance department. As we have seen above the process is very complex and many manual controls and need lot of expertize to get in to the insights of the process, If we take the example of DOE done. there were times it was very difficult to bring the feed solids constant considering the high volume process, it was a challenge and it was the case for many other parameters, external influence was more on parameter settings, It could be same in other process other than this also,, Though DMAIC can't be replaced by AI still AI can reshape and assist and simplify and give optimized result. Like in the example of DOE output the optimised result could further be optimised for parameter shift and drift or small bais from the settings. AI could also reshape the baseline performance setting by evaluating the data at the stage of measure phase, in defining the problem statement and Goal statement based on historical data at the stage of define.