Appunti di Industrial Technologies

FACTORY LAYOUT PLANNING

Definition of the physical organization of the factory, search for the most efficient location of the shops.

Objective: minimization of the costs respecting the plant constrains. (multi-objective: min distances, max proximity).

Final result: CAD drawing of the factory layout.

Systematic layout planning methodology:

1. Analysis
   • Products (volume and amount of goods to produce)
   • Material flow (requirements for movement between shops)
   • Relations between activities
2. Design
   • Graph method
   • Space diagram method

Computerized layout techniques:

HP: given space is rectangular, every shop is rectangular or composed of rectangular pieces.

CRAFT -> layout improvement procedure (based on the minimization of moving cost among the shops, needs a starting layout).

Procedure:

1. Find the tables trips/day, €/m and compute the meters/trip calculating Euclidian distances
2. Calculate the total costs table
3. Try all the possible horizontal or vertical switches for the first

iteration and find the lessexpensive

Calculate the real cost of the less expensive iteration and switch if it is actually lessexpensive.

Restart if you want to do a second iteration.

ALDEP -> layout construction procedure (not working on the minimization of costs as CRAFT, it is considering in the best way the relationship between different areas).

Procedure:

1. Division of the area in blocks
2. Construction of the relationship chart
3. Start to allocate the shops from the upper left corner with a zig-zag U-shaped path and with the decided sweep width (2 blocks usually)

JOBSHOP

Organizing production systems in shop areas based on the job that the machine has to do; machines are grouped on the basis of technological processes; each grouping is called a shop.

Strengths:

1. High flexibility (mix, volume, product customization, product innovation)
2. Low impact of breakdowns
3. Low obsolescence of the system

Weaknesses:

1. Limitations in machine efficiency (high waiting time, many WIP,
Non posso utilizzare risorse al massimo della disponibilità)2. Le caratteristiche qualitative del prodotto possono variare da pezzo a pezzo3. La gestione della produzione è difficile4. Difficile calcolare la capacità di produzione CELLULE DI PRODUZIONE I pezzi vengono raggruppati in famiglie di parti e le macchine in celle, le macchine vengono raggruppate perché devono elaborare lo stesso tipo di prodotto. Ogni prodotto ha il proprio percorso all'interno della cella quando non è necessario spostarsi tra le celle. Punti di forza: 1. Razionalizzazione dei flussi di materiale 2. Riduzione del tempo di setup 3. La gestione della produzione è più semplice 4. Riduzione del lavoro in corso 5. Riduzione dei tempi di consegna 6. Migliori stime dei tempi di consegna 7. Ingrandimento e arricchimento del lavoro per i dipendenti 8. Lavoro di squadra all'interno della cella 9. Unificazione delle responsabilità del prodotto e del processo 10. Maggiore controllo sulla qualità dei prodotti Punti deboli: 1. Difficoltà nel bilanciamento del carico di lavoro tra le celle 2. Problemi legati alla variabilità della miscela di produzione 3. Difficoltà nell'applicazione a tutte le fasi della catena di produzione 4. In alcuni casi,

more machines than job shop

Difficulties to manage technological operations outside the cells

Problems related to breakdowns

Group technology: technological activities that we should run in designing a manufacturing system as a cell.

Methods:

• Informal methods: those with the same shape in the same product family (based on geometrical or technological features)
• Part coding analysis methods (based on geometrical or technological features)
• Production flow analysis
1. Clustering analysis -> obtain the data to build the table
2. ROC (rank order clustering)/similarity coefficients

Virtual cellular manufacturing (VMC): machines that belong to the cell are not physically together but the production planning and control system identify them as a cell -> more responsive to production mix variability. TRANSFER LINES

Each transfer line consists in a series of machines where a single product type flows. Each line is formed of stations and in each station there are different

rules MinSlack -> smallest slack time MinSlackMaxDur -> smallest slack time and longest process time Simulation models

rules

• MaxFol -> largest number of following tasks/operations
• Ranked Positional Weighting -> indicator of the total amount of time that a task needs after its execution, tasks are ranked by non-increasing PW

All these roles can be used with different approaches:

• Task oriented -> when the remaining time is not sufficient we open a new station -> simpler but less efficient
• Station oriented -> when the remaining time is not sufficient we consider the other available operations -> less simple but more efficient

ASSEMBLY SYSTEMS

• Fixed position: product is assembled in a single site, materials, equipment, tools are brought to the site.
• Assembly shops: different stations, each station is assigned a phase of the assembly process.
• Assembly cells: suitable for families of similar assemblies, products move during the assembly through a number of stations with some flexibility.
• Assembly lines: each assembly consists of a series of stations where the product is progressively assembled.

ASSEMBLY LINE

Classification according to the material handling system:

• Paced
• Intermittent
• Continuous
• Machine-paced -> CT and production capacity perfectly controlled but probability of no completion
• Operator-paced -> no problem of unfinished pieces but CT is variable and determined by the slowest operator
• Continuous flow paced lines -> system moves slowly at a constant speed and operators follows the pieces
• Operators can't stop the line -> CT and production capacity perfectly controlled but probability of no completion
• Operators can stop the line -> no problem of unfinished pieces but CT is variable and determined by the slowest operator
• Un paced -> it's up to the operator to move the pieces to the next station, we use buffers between stations -> no problem of unfinished pieces, CT can be exceeded occasionally, CT and production capacity are not perfectly controlled
• Synchronous
• Asynchronous

The size of the buffer:

1. The

production capacity PC increases with the increase of the buffer size until a certain point -> trade-off between production capacity and occupied space (costs, WIP, throughput time)

For a given buffer size, the production capacity increases with the decrease of the coefficient of variation CV of the times of the assembly tasks.

Balancing constrains in the design of a manual assembly line:

Balancing objectives in the design of a manual assembly line:

• Technical objectives
• Minimizing the number of stations given the CT
• Minimizing the CT given the number of stations
• Minimizing the total idle time
• Minimizing the probability of no completion (in machine-paced line or continuous flow line where the operator can't stop the line)
• Minimizing the probability that the times of operations in one or more stations exceeds CT (in an operator-paced line or in a continuous flow line, in case the operator can stop the line)
• Economical objectives
• Find the proper balance between

efficiency and cost

Minimizing the total expected cost TEC

Models for line balancing:

• Linear programming
• Maximum fixed utilization rate
• Probability of no completion

NB. The probability of no completion can be mitigated by putting riskier situations at the end of the line and not at the beginning

Design of continuous flow line elements of flexibility– PACED LINES

1. Space and distance -> time buffer against assembly time variability -> length=buffer size
2. Speed of the conveyor -> if you increase it you reduce CT, if you decrease it you increase the size of the buffer L=total distance considering also the safety zone
3. D= distance in which we can change the conveyor for reducing of completion

SIMULATION

Replication of a system into an environment that we can study and easily control.

Types of systems:

• Continuous -> variables change continuously
• Discrete -> variables change at specified intervals of time
• Deterministic -> no uncertainty in the simulated
phenomena
• Stochastic -> there are some aleatory variables
• Static -> system doesn’t change in time
• Dynamic -> system changes over time
Monte Carlo Simulation
• Static and stochastic
• Uses random sequences to simulate a certain aleatory phenomenon so that we can have a simulation for which we can make statistical analysis, the higher the number of experiments, the more reliable the solution
Elements for Monte Carlo simulation:
• Information on the statistical distribution
• Generator of random numbers
• Mathematical representation of the simulated process -> relationship between variables
• One or more aleatory variables as output
• Possibility to evaluate the results
Procedure for Monte Carlo simulation:
1. Generation of a sequence of random numbers
2. Transform them into variables of interest
3. Use those variables into mathematical formulations
Discrete events system and simulation

Imitation of the operation of the real world process or system over

timePredict - Predict the behaviour of a system under certain conditions that most likely will manifest while the system operates

Approach of discrete events:

• Focus on the times between the events
• Update the system only at this specific time point
• We have full information about history since nothing happened between two time points

Main components of a DES:

• Entity: thing with distinct and temporary existence that passes through a system
• Resource: thing with active and permanent existence