CONSULTORÍA

INGENIERÍA DE CALIDAD Y SIX SIGMA

Ayudamos a los clientes a identificar las fuentes de ganancias y ahorros en la operación regular, para tener más beneficios sin aumentar los recursos. Somos expertos en aumentar los márgenes mediante la reducción de las deficiencias en los procesos (exceso de scrap), altos costos de energía, procesos inestables, alta variación en los resultados). Nuestros consultores han sido sólidamente capacitados participando en múltiples proyectos y diversas empresas, para tener una amplia perspectiva de las situaciones y atacar los problemas desde sus causas raíz. Se utilizan métodos analíticos y estadísticos para comprender la situación e impulsar mejoras. Herramientas como diseño de experimentos, análisis estadístico de causas raíz y modelos matemáticos se utilizan para mantener los resultados.

RESULTADOS DE PROYECTOS

INITIAL SITUATION

  • The machining center is used to manufacture chrome-molibdene steel gears (20 Kg per part)
  • Scrap rate over 30% due to dimensional and geometrical characteristics
  • Production rate of 16 parts / hour and the need for change of the toolings every 10 parts due to wearing of the inserts

PROJECT DESCRIPTION | APPROACH METHODS

  • Definition of initial situation and targets
  • Measuring machine performance using Ppk as indicator
  • Definition of machine parameters affecting characteristics
  • Definition of an experimental layout to test the machine parameter effects at different levels
  • Developing of 18 experimental tests and measurements to obtain data and statistical items (average and variation)
  • Analysis of results and definition of the best parameters combination (improve runout and reduce variation)
  • Validation run and measurement of improvement
  • Standardization of new machine parameters

PROJECT RESULTS

  • Scrap reduced from 30% to 0%
  • Machine stop frequency to change worn tools was reduced from 30 parts to 10 parts
  • Tools consumption reduced to 1/3 of initial
  • Ppk increased form 0.25 to 3.06 (0.5 sigma to 9 sigma)

INITIAL SITUATION

  • Foundry company with 8 lines of production
  • Electric ovens working at 1500°C , 12 hours a day
  • Power company invoice reached 700,000,000 Kw/h
  • Efficiency indicator defined as total plant consumption divided by total tons of finished product
  • Initial value for the indicator is 2900 Kw/h per ton of finished product

PROJECT DESCRIPTION | APPROACH METHODS

  • Initially the data gathered is being split by
  • Analysis of capacity for finished product
  • Definition of mathematical model to predict total plant consumption based on production program (tons)
  • Balance of production program and capacity to increase efficiency
  • Reduction of energy consumption through cause and effect analysis as well as statistical tests to validate causes and corrective actions
  • Increase finishing capacity through cause and effect analysis and Lean Tools.
  • Scrap reduction through statistical analysis and design of experiments

PROJECT RESULTS

  • 28% reduction in energy efficiency indicator in 3 months
  • Savings over 100,000,000 Kw/h per year
  • Reduction of 20 tons of scrap per month
  • Production program balanced with customer demands and plant capacity

INITIAL SITUATION

  • Line of thermoformed PET packages – 1,250,000,000 units per year
  • Package wall thickness in specific points presents variation
  • Thickness under specifictaion produces scrap or customer complaints
  • Thickness over specification produces overcost of material

PROJECT DESCRIPTION | APPROACH METHODS

  • Initial study of design and drawings to define the optimal weight of material per package
  • Identification of differences between designed and actual weight (Tons of processed material vs. number of units)
  • Definition of indicators and how-to-measure project advances and results – Ppk – Process preliminary capability
  • Identification of potential causes of variation and statistical validation to find the real causes
  • Setting of values for each influent variable to make it constant
  • Fine tuning of the process using Taguchi Robust Design approach

PROJECT RESULTS

  • Scrap was reduced from 2.91 % to 0.03 %
  • Overmaterial was reduced from 4.22 % to 0.02 %
  • Scrap reduction results were savings of 900 T of material per year
  • Excess of material savings reached 1300 T of material per year
  • Total saving of the project: USD 1,000,000 per year

JUNTOS RESOLVEMOS SUS NECESIDADES TECNOLÓGICAS

CONTÁCTENOS

Nuestras soluciones contribuyen a la realización de los objetivos organizacionales de cada uno de nuestros clientes porque se ajustan a sus necesidades.





    Los campos con * son requeridos