In part 1: Excel Value Stream Map we saw how a complex process could be Value-Stream-Mapped using Excel. We saw the importance of mapping feedback loops such as test-repair loops because they often become the bottleneck for the complete process. In this situation not all items flow along all branches so we need to calculate the % flowing along each branch. This enabled us to calculate the cycle for each workstation based on the staffing. Finally we were able to calculate the ideal staffing to balance the line: insure no workstation cycle is above the average inter-arrival time (takt time). This we did using Solver.

A value stream map is not complete without Work-In-Process (WIP) between process steps. The problem with WIP data is that it typically varies widely along time so a snap shot of the moment we visit the line may be very misleading. In our VSM Simulator WIP before each workstation (column Q) is calculated each hour:

New WIP = Previous WIP + Items processed by previous step – Items processed by this step

Items processed (column R) are calculated each hour based on Average time (K), Time Standard deviation (L) and Staffing (M) taking into account the constraint of enough WIP in the previous step. The simulator is assuming that process times follow a normal distribution.

Process times: Normal distribution with averages (K) and standard deviations (L)

On the other hand Inter-arrival time (Takt) follows an exponential distribution:

Inter arrival time distribution: Exponential with average K3

Lead Time in column T, for each process step, is the average time it takes for an item to be processed in that step including the waiting time due to the WIP in front of it.

Capacity utilization in column U is the average % time the workstation has been operating since the start of simulation based on its theoretical capacity.

WIP (Q) and Capacity Utilization (U) are shown on bar charts on the right:

Elapsed hours simulated are in B2. Products received for repair,  products repaired and products returned without repair are shown in column A:

Total values for columns K to U are shown in row 1:

K1 represents the average Value Add Time per product

M1 is the total staffing in the line

P1 is the maximum value of all cycle times: it corresponds to the line bottleneck capacity. P1 should be ≤ K3 (Takt)

Q1 is the total WIP in the line.

Received (A3) = WIP (Q1) + Repaired (A13) + Returned without repair (A8)

T1 is the Total Lead Time (hours): The average lead time seen by the customer (not including the customer contribution)

U1 is the overall line capacity utilization.

The Value add to total time rate can be calculated:

Value Add Time / TOTAL Time = K1 / (T1 x 60)

### To run the simulator:

press F9 to simulate 1 hour. Keep pressing to run several hours. The counter in B2 shows the elapsed simulator hours.

To start simulation with an empty line: press Ctrl key + r. This will reset all line WIPs to zero.

## In the tab Evolución you can see how all Processes and WIP values evolve along each hour in the main steps as well as the evolution of total Lead Time:

During the first hours, when the line is empty, there is no waiting time along the process so the total lead time is basically equal to the value add time of one hour. As queues start building up as shown in the WIP chart, waiting time starts to increase in all workstations and that causes the total lead time to increase to a value of 12 hours, where it seems to stabilize. This means that if we take a snap shot of the line in hour 7 basically all lead time is value add. On the other hand if we look at the line in hour 22, when stability seems to be reached, lead time is 12 times value add time: NVA/ VA = 11. Non value add time is made up of all the waiting taking place in front of each workstation.

We can see that, although the average inter-arrival time is 5 min (12 items per hour), 3 arrivals of more than 30 items each have collapsed receiving causing an accumulation of 80 items at reception which has eventually been absorbed by the line.

This is a typical case where most of the variability comes from the customer inter arrival times as seen in the process chart.

Tracking this evolution you can discover unexpected bottlenecks caused by variability that may need to be reinforced with additional staff. We can also estimate the total lead time we can commit to customers. In this case if the process is stable that would be 12 hours minimum.

### These are some of the effects we can detect in our simulation:

• Accumulation of WIP in front of a workstation due to insufficient staffing

• Low capacity utilization due to excess staffing

• Accumulation of WIP in front of the repair station due to low first-pass-yield in test (N11)

• Accumulation of WIP in reception (Q4) due to arrivals above plan

• High overall lead time due to excess total WIP in the line

• Highly unbalanced line as shown by large differences in capacity utilization

• Proportion of products under warranty different to plan: it would require line re-balancing

### Conclusions:

1. A static Value Stream Map is a good start to understand how the process behaves

2. Feedback loops such as test-repair loops are an essential part of the process so they should be in the VSM

3. A snap shot of the WIP situation along the line may not be representative of the normal operation

4. If the WIP situation is not typical the NVA calculation will not be correct

5. The VSM simulator provides a deeper understanding of the process behavior and it enables what-if studies to optimize the process

6. Simulation helps us understand some of the failure mechanisms caused by variability so we can act on the root cause to make real improvements

Value Stream Maps are typically designed to flow horizontally left to right. When a process is complex it becomes difficult to view the whole process: it may require several pages and this makes it difficult to see the total picture.

Making the VSM vertically allows us to visualize complex processes in a single page.

Another common practice is to map the process by sticking post-its on a wall or large wrapping paper. This has the advantage of simplicity but it creates some inconveniences. It becomes impractical to save all the work done to continue working on it later on. It is also difficult to communicate to others: photos are difficult to read. If you ask other people, not present in the VSM exercise, to give their input they can not modify the work you have done.

Doing a VSM in Excel has several advantages:

1. The team involved in the VSM exercise can be located in remote locations and can all share a spread sheet in the cloud with communication via Hangouts or Skype.
2. All the work done is not lost at the end of the meeting and it can be updated remotely at a later stage by all process participants
3. Excel allows the addition of the process parameters for each process step by adding as many columns as required on the right:

·         Process time

·         Setup time

·         Cycle time

·         Work-In-Process waiting

Complex processes require often feedback loops: repair failing items and test them again. These loops are an essential part of the VSM because they often become the bottleneck of the total process.

When the process doesn’t have a single flow but there are several branches you need to estimate the proportion of items that will flow along each branch and this you can do by collecting data along a period of time.

Process times have variability so you will need to estimate not only their average values but also their standard deviations.

In this Repair process the average time it takes to receive a product is 2 min and its standard deviation 0.5 min.

20% of the products are under warranty so 80% will follow the “N” branch.

Average inter arrival time of products coming from the customer for repair is 5 min. This is the takt time the process has to comply with.

Not all products go through all process steps, therefore we need to calculate % throughput for each step based on the % of products flowing along each branch.

In order to balance the line we need the right staffing on each process step to insure enough capacity to handle the product arrivals. This means that Cycle time  Inter arrival time (takt).

Cycle time  =  Average time   x   % Throughput  /  Staffing

We can calculate the staffing (Full Time Equivalent) required for each step M4 – M13 with Solver

To do this we calculate the total staff required in cell M1 just adding all the yellow cells in column M. This cell is what we want to minimize as long as we meet the requirements that all cycle times P4:P13  are below the inter arrival time in K3.

We are adding the constraint M4:M13 0.1. The proposed staffing by Solver in the yellow cells M4:M13 is:

Which gives a total staff of 13.6 (M1). This is a theoretical minimum which assumes all staff has all skills, which is not the case.

In cells D1 to H1 we obtain the compound for each process participant (some perform several operations) obtained from the column M.

If Receive and Coordinator can be combined we may have to round up the sum of the two from the 1.6 requirement to 2.0

Invoicing we round up from 1.8 to 2. Test from 2.9 to 3 and repair from 7.4 to 8.

This manual rounding will lead us to this situation:

Where the total staff has increased from the theoretical 13.6 to 15 after considering the different skills required in the different steps.

This increase in the staffing has an effect on lowering the cycle of the steps affected below the takt time of 5 minutes.

Looking at the % Throughput column we notice that some steps only process 4% of the products to be repaired while test and repair have to process 123%. The reason for this is that only 80% pass the test OK the first time. This means 20% have to be tested a second time. Same with burn-in, where 10% fail and have to be retested.

So far we have not taken into account variability but we will do so by adding some additional columns to our VSM converting it into a simulator in our next article: How to convert your VSM into a simulator to experience variation

How can I understand the effects of variation in my process? What are the side effects of my improvement actions? What are the possible failure mechanisms? You can "Learn By Doing" with this simple Excel based simulator.

Process simulation helps us understand the value stream dynamic behavior: how variation affects key process metrics such as WIP, Lead time, Throughput, Cycle time, On Time Delivery, etc.

Understanding these dynamic effects will enable us to get to the root cause of problems implementing definite solutions and not just containing the symptoms.

• Close all Excel files before you open this one. Enable Macros.
• Excel configuration: Manual calculation. Enable iterations. Number of iterations: 1

## Process Flow Concepts

THRUPUT (or THROUGHPUT): Number of items actually processed per unit of time. (Spanish: FLUJO)

CYCLE TIME: Time elapsed between two consecutive outputs. (Spanish: CICLO)

TAKT TIME: Cycle time required by the customer or market demand. (Spanish: TAKT)

WIP (Work In Process): Number of items being processed or waiting to be processed. (Spanish: Obra En Curso)

LEAD TIME: Total time in the system (process plus waiting). (Spanish: PLAZO)

Thruput could be defined for one single workstation or for a whole line. In the case of dealing with a whole line thruput is what comes out from the last workstation.

Takt time is generally different to lead time. Only in the case of a single operator performing the entire process will these two values be the same: he will process one item completely and them move on to the next item, therefore the lead time (processing time) will be equal to the takt time (time between two consecutive outputs).

Work-in-process may be defined again for a single workstation or for the whole line and it includes all the items in the system either being worked on or waiting.

A stable system may be defined as one where the work-in-process and the thruput are stable (in statistical control).

In a stable system the rate of items entering the line is equal to the rate leaving it.

This chart shows a process made of four steps. The takt time (10 min) equals the step time, therefore the lead time will be 4 x 10 = 40 min.

Thruput = WIP/LT = 4/40 = 0.1 unit/min = 6 unit/hour

In the following chart the curve on the left represents the cumulative number of items started in the line. The curve on the right is the cumulative number of units finished. From hour 0 to 25 the process is stable: the starts line is parallel to the finishes. The horizontal distance between these two lines represents the lead time (time elapsed between one item being started and being finished). The vertical distance between the two lines represents the WIP (number of items in the system in that moment in time).

We assume that each item being started has to wait its turn in a queue until all the items which are already in the system are processed at the thruput rate. The total time in the system for this item will therefore be the time to process all the items in the system including itself.

From hour 25 to 30 we have decided to stop all starts in the line, as a result the WIP has been depleted (from 20 units it has gone down to 10) and therefore we see that lead time has also been halved (from 10 hours to 5 hours).

From hour 30 on wards the line is also stable.

The thruput of the line in this chart is the slope of the finish line. its value is 2 items/ hour.

We can see that the slope has remained constant all the time, therefore lead time varies proportionally to WIP:

LEAD TIME = WIP / 2

JUST-IN-TIME (JIT): Buffer capacity constraint: stop if output buffer is full

CAPACITY UTILIZATION:  Rate between customer commitment and line capacity

YIELD:  Rate between good items and total items processed. (Spanish: APROVECHAMIENTO)

YIELD LOSSRate between bad items and total items processed. (Spanish: MERMAS)

COMPOUND YIELD:  Resulting from all step yields. (Spanish: APROVECHAMIENTO TOTAL)

BATCH MODE:   Process items from several periods in one go. (Spanish: POR LOTES)

Just-In-Time (or Continuous Flow Manufacturing) consists on limiting artificially the buffer capacity to a minimum in such a way that nobody is allowed to process an item unless he has room for it in the output buffer. This empty space can only be created by the next workstation when it removes an item. If we follow the same procedure up to the end of the line we see that it is the final customer who activates the whole line by removing finished product/ service from the end of the line. This "hole" created by the customer "bubbles" upstream along the whole line activating as it goes along all the workstations.

This mode of operation is also called a PULL system as opposed to a PUSH system where the items are pushed into the line and they are processed as they come (case of no buffer constraints).

In some processes not all the items which start in the line find their way to the end: some are lost on the way. This is something common in state-of-the-art technology lines: some items are found defective and non-recoverable and therefore they are discarded along the way.

This effect is also common in business processes: every time there is a control, a signature, etc. there is a possibility of a unit being rejected and therefore not proceeding any further in the process. An example of this is a purchase order which is rejected by the Finance department due to budget restrictions. The immediate consequence of yield is that since there are more items starting in the line than items finishing, the capacity requirements in all workstations will not be constant along the line: the first workstations require more capacity than the last.

Batch processing again is something common in information systems (batch run as opposed to real-time). But this also happens often in many business processes: an engineer who reports all his calls at the end of the day; a manager who processes his mail once every two days, etc.

# 1. Ideal Process

You have committed to deliver to your customer exactly 100 items each day.  He absolutely needs each one of them.  One missing item can cause him a major disruption, therefore if one single item is missing it will be considered as a decommitment. Each item has to be acted upon by 5 different departments in sequence before being delivered to the customer.  At the beginning of each day there are 100 items in process on each department and each department is processing 100 items therefore the customer gets at the end of the day the 100 items you have committed. Everything is so perfect that nothing seems to move. If it was not for the clock counting the days at the top right corner of the screen one would think that the simulator has stopped.

SET-UP:

Press START to run the simulator

QUESTIONS:

• How is delivery performance?      % on time  =
• Ave WIP =
• How real do you think this situation is?

# 2. Capacity Bottleneck

In the ideal case seen before all departments had a capacity to process 100 items per day and that is exactly what they did. In real life this perfect matching of the capacities of the different departments does not happen: there will always be differences in the capacities even if theoretically they ought to be equal. If these capacities are different there will be one department which will have the lowest capacity of the whole line and this department will constitute a bottle-neck for the total process. We will call this a "capacity bottle-neck" to distinguish it from other possible types of bottle-necks we will see later. The capacity bottle-neck may be due to some critical equipment: computer, photocopier, etc. which is in short supply. The cause may be a critical skill which is in short supply in the organisation.  It could be a person who is ill in one of the departments or an authorization which only one person can sign and happens to be very busy.

SET-UP:

1. Press Stop to stop simulation

2. Change step 2 capacity to 99 (press ENTER )

3. Press the Start button

QUESTIONS:

• What happened to delivery performance?   % on time  =
• Where is the queue building up?
• Why?
• First priority: Customer satisfaction. How can we improve delivery performance if the bottleneck can not be improved?
• How about renegotiating the delivery commitment with the customer?

# 3. Renegotiate Commitment

In a normal situation it is possible to compensate for a lower capacity in one department by working a bit faster in order to catch up with the work or by working overtime, etc. There are, on the other hand, some cases where there is a physical limitation which can not be compensated by overtime or there are legal constraints, etc.  which prevent it. In a case like this, where there is no possibility to open up the bottle-neck, all that is left to do is tell the customer and renegotiate with him volumes which are in line with the bottle-neck capacity.

SET-UP:                                                (Continue from previous exercise)

1. Press the Stop button

2. Change Commit to  99 (press ENTER)

3. Press the Start button

QUESTIONS:

• How is delivery performance now?   % on time =
• Is the queue still growing?
• Why?
• How can you stop the queue from growing further?
• How about limiting the line starts to the bottleneck capacity?

# 4. Limit Starts to Bottleneck

The bottle-neck is therefore dictating how much you can commit to your customer but it also dictates how many items you should start at the beginning of the line. Starting more than the bottle-neck can handle is a waste: the number of units coming out of the line (thruput) will be equal to the bottle-neck capacity and therefore all the units started above that capacity will simply pile up in front of the bottle-neck increasing not only the inventory but also to the lead time.

SET-UP:                                                (Continue from previous exercise)

Change Step 1 Capacity  to 99

QUESTIONS:

· Is the queue still growing?

· Why?

· Why do we still have a queue if the problem has been solved?

· How can you reduce the queue?

· How about limiting starts below delivery level for some time?

# 5. Limit Starts to Reduce WIP

By controlling both the output and input to the line with the bottle-neck we can now say that the problem is solved. But is it really? We see that the lead time is still too long and this is due to the excess inventory which has accumulated in front of the bottle-neck. Something similar happens when we have been overeating during holidays: going back to normal eating habits is just not enough if we want to get rid of the extra kilos. In this case therefore you have to flush away the excess inventory and one way can be to limit starts below the thruput level until the excess is depleted.

SET-UP                                                  (Continue from previous exercise)

1. Change step 1 Capacity to 90

2. Run (press Start) until the excess inventory is depleted (press Stop)

3. Change back step 1 Capacity to 99 (press Start)

OBSERVATIONS

· What happened to Ave WIP when you limited starts below delivery?

· Why?

· What happened to Av Lead time?

· Why?

· Did you miss any deliveries during this operation?

· How can you reduce excess WIP otherwise?

# 6. Apply JIT to Reduce WIP

Another way to eliminate the excess inventory in the line is to apply "Continuous Flow Manufacturing" also called "Just In Time". This consists basically in banning the accumulation of inventory by decree. The way to do that is to ask all departments to stop processing the moment they do not have an empty place to put their output. That place to put the output will only be created the moment the next department in the line removes the previous item in order to work on it.  With this logistics nobody processes more items than the ones going to the customer and in case of excess inventory the corresponding departments stop processing until the excess is depleted. To simulate this method you will fill the line with inventory by putting excess capacity in all workstations (departments) for a while. When the line has this excess then it will be depleted applying the JIT method, which is simulated by constraining the buffer capacities to the level committed to the customer.

SET-UP:

1. Stop simulation

2. Reset

3. Clear WIP

4. Set all Capacities to 500

5. Start simulator until line is full (all WIPs = 9899)

6. Stop and change all Capacities back to 100

7. Apply JIT: Set all Max queues to 200

8. Start the simulator and watch the effect

QUESTIONS:

· What happened to the WIP?

· Did you miss any deliveries?

· Is this method of reducing excess WIP safer than the previous one?

# 7. Variability Bottleneck

Apart from the capacity bottle-neck experienced before there is another type of bottle-neck which is generated by the fact that a department has high variability in its performance. Although very often both types of bottle-neck will be combined in the same department or process step we will isolate the pure variability bottle-neck (same average capacity as the rest) in order to see its effect on the total process.  Typical variability bottle-necks are checking or approval steps, where some items take very short but some others may take much longer. Public services or services which are outside our control may typically show also high variability. Poor quality also generates variability because you have to repeat some operations on some of the items.  Equipment failure or low performance can also cause variability.  When several concurrent inputs are required for a certain process step it will happen that they will not arrive at exactly the same time, therefore one may have to wait until the last one arrives, this is therefore another source in performance variability.

Examples of variability bottleneck:

· Test/ quality control step

· Public service (mail, transport, customs, etc.)

· Quality problem step

· Sensitive/ critical equipment

· Multiple concurrent input step

SET-UP:

1. Stop simulation

2. Reset

3. Clear WIP

4. Set step 2 Variation  to 20 (Capacity varies at random between 80 and 120)

QUESTIONS:

· What happened initially to delivery performance?    % on time =

· What is the average production of STEP 2?  Average #2 =

· Who is causing the problem in the line?

· Is he likely to admit it? Why?

· Ave WIP =

· Where is the WIP building up?

· Is delivery performance improving as WIP builds up? % on time =

· Why?

· What happened to Av Lead time?

· What can we do to reduce Lead time?

WIP build up as a result of variability in step 2:

# 8. Apply JIT to reduce Lead time

The purpose of JIT is to reduce excess inventory leaving the absolute minimum required to operate. By doing this waiting time will be reduced and therefore the total lead time will also be reduced. But does it have any side effects?

SET-UP:                                                          (Continue from previous exercise)

Set Max queue to 200 in all steps

QUESTIONS:

· % on time =

· What is the average production of all steps? Average =

· Which STEP is causing the problem to the whole line?

· Is it obvious if we just look at the Average line?

· Has Lead time improved by applying JIT?           Av Lead time =

· Can Lead time decrease and On-Time-Delivery get worse?

· Would you recommend this solution?

· Why?

· What happens if you have high variability and apply JIT?

· In a case of high variability, how can you reduce lead time and still meet your commitments?

· How about reducing capacity utilization?:

# 9. Reduce Capacity Utilization

Assuming we are not able to eliminate the root cause of the problem (variability in STEP 2) how can we meet our customer commitments and at the same time reduce lead time? We will try reducing capacity utilization, which means that we will not commit to our customers 100% of our capacity but only 95%, leaving the 5% balance to compensate for the variability of STEP 2.

SET-UP:

1. Stop simulation

2. Reset

3. Clear WIP

4. Set step 1 Capacity to 95

5. Set step 2 Variation to 20

6. Set Commit to 95

QUESTIONS:

· % on time =

· What are the following values after stability?

Ave WIP =                                   Av Leadtime =

· What are the negative effects in this case?

· What is the effective thruput in the line? Ave Thruput =

· Would you recommend this solution?

When capacity utilization approaches 100% then lead time tends to infinity. The reason for this is that variability makes thruput to drop and items start accumulating making the queues, and therefore lead time, longer and longer.

A similar thing happens in a motorway: as soon as the flow of vehicles gets near the motorway capacity speed quickly drops and queues form for several kilometers. This causes the time to increase dramatically.

Point A in the graph represents a situation of high variability and low capacity utilization. If we increase utilization (point B) lead time will increase. If we were able to reduce variability (point C) we would have both: high capacity utilization and low lead time.

# Process Yield

Yield is the loss of items during the process. This loss may be due to defects that force to scrap an item or it may be due to the item being rejected in one of the control/ approval steps.

Process Yield Examples:

· Defective silicon wafer scrap

· Scrap after test/ quality control step

· Losses on transport service

· Order cancellation

· Work permit application rejection

In those cases where there is yield loss in several process steps we want to know what the total yield will be in order to size our organization properly.

Examples:

· Semiconductor line

· New technology

· Low quality process

A yield bottleneck is typically a control step where rejection of some items may take place

# 10. Yield Bottleneck

SET-UP:

1. Stop simulation

2. Reset

3. Clear WIP

4. Set step 2 Yield  to 99

QUESTIONS:

· What is the total thruput in the line?  Ave thruput =

· How many units do you start in the line on each lead and how many do you finish?

Average #1 =            Ave thruput =

· What happened to the difference between started and finished units in the case of a capacity bottleneck?

· And in this case of a yield bottleneck?

# 11. Compound Yield

In a process where items are lost/ rejected in several steps we want to know what the total process yield will be as a function of the step yields. This is what we call compound yield.

SET-UP

Set all step Yields  to 90

OBSERVATIONS

· What is the total line yield?  Ave thruput =

· Is it what you would expect?

· How do you calculate total yield from step yields?

· Is 90% step yield acceptable?

· What would happen with larger number of steps?

· What happens to the difference between the units started and those delivered?

# 12. Additional Capacity to Compensate for Yield Loss

How can we estimate the capacity requirements for each process step in a case of yield loss? What are the buffer size requirements? What we want to achieve is deliver the customer requirements with the minimum capacities possible and keeping the minimum amount of inventory to reduce lead time.

ASSUMPTION:

Yield = 90  in all steps

OBJECTIVES:

Commit = 100

% on time = 100

QUESTIONS:

•  Estimate the minimum capacities required to achieve this:
 1 2 3 Capacity
• What is the cost of this solution?   Ave Cap =
• How can you make lead time equal to 1 while meeting delivery commitments?
• How about limiting the maximum queues?
 1 2 3 Max queue

# Batch Mode Operation

Batch operation means accumulating items for a number of periods and processing them all in one period. This simulation will assume that the average capacity of the batch-operating step is still the same as in the rest of the steps.

Batch mode examples:

· IS weekly run

· Manager's weekly sign off

· Weekly transport/ delivery

· Mail distribution every 4 hours

· Burn-in run with full chamber

# 13. Batch Mode Effect

A step with a batch period of 5 and average capacity of 100 will process no items during 4 of the periods and 500 items in one single period (the average will be 100 per period).

SET-UP:

2. Stop simulation

3. Reset

4. Clear WIP

5. Set step 2 Batch to 5

QUESTIONS:

• Is the batching of STEP 2 affecting the thruput of the total process?        Ave thruput =
• Is it affecting on-time delivery?          % on time =
• What is the effect in WIP?                  Ave WIP =
• Why?

# 14. Low Yield + Variability

In real life we will not find single effects as seen so far, but rather a combination of the different effects. When several effects coexist we will not find a single optimal solution. Depending on the relative cost of capacity Vs inventory/ lead time the optimal solution will vary. Typically increasing capacity drastically will solve the problem but it will be an expensive solution.

SET-UP:

1. Select Simulator tab

2. Stop simulation

3. Reset

4. Clear WIP

5. Yield = 99 in all STEPS except  #1

6. Variation = 10 in all STEPS except  #1

OBJECTIVES:

· % on time = 100

· Minimum average capacity ( Ave Cap )

PROPOSED SOLUTION:

· Required capacities and Max queues:

 1 2 3 Capacity Max queue

· Resulting    Ave Cap =

· % on time =

# 15. Bottleneck Effect Recap

The different bottlenecks we experienced produce different effects on the process parameters. Let us recap in order to compare. In real life we will find combinations of a number of these effects and therefore we might need a number of corrective actions in order to improve. Indicate how each parameter will be affected in the long run.

# Time Stamp : A key process metric you can collect for free

Data collection is not for free. While an operator is recording data, he/she is not producing. In general variable data such as a speed, temperature, etc. requires some sort of equipment to measure it and therefore it is more expensive to collect than attribute data: Pass/Fail, On-time/Late, etc.

Time is a key variable in any process which can automatically be collected by any PC, Portable, Tablet or Smart Phone, therefore it does not require any additional measuring equipment.

Entering a date or a time manually into a terminal is prone to multiple typing errors which if undetected can distort all subsequent key metrics.

If data is collected on paper and then typed into the system  at a later stage, apart from the waste of this operation, the errors might be difficult to recover.

If we are able to simplify the data recording operation, the operator can report in real time all incidents or data entries, in which case the data collection application can automatically associate a Time Stamp to each piece of data entered:

 Incident reported Automatic Time Stamp Line stopped 21/06/2017  19:40 Line restarted 21/06/2017  20:08

The operator just selects the incident from a drop down list and the time stamp is added automatically.

In the previous example by subtracting the two time stamps you know the line was stopped for 28 minutes.

The way to register a time stamp with Excel is:

 Current Time Stamp English Spanish = NOW() = AHORA()

From a time stamp X you can easily obtain:

 English Spanish Day of the week =WEEKDAY(X:2) =DIASEM(X;2) Week of the year =WEEKNUM(X) =NUM.DE.SEMANA(X)

You can also obtain the Shift (Morning, Afternoon, Night) from the hour of the Time Stamp with a formula.

This data obtained from the time stamp could be very useful during analysis in order to detect significant differences between:

• Hours
• Shifts/ operators
• Days of the week
• Weeks
• Months

Very often data is still being collected on paper because there are no terminals at the point of use and it would be very costly to install them.

On the other hand we all use cloud based technologies in our private life: smart phones, tablets and portables sharing data in the cloud, with basically no other cost than the WIFI/ Wideband connection.

A new approach of BYOD (Bring Your Own Device) is being practiced by some companies which makes data collection almost free by using the employees own smart phone or tablet with the Company WIFI and private cloud.

In the following example a spread sheet is shared in Google Drive by line operators who report line incidents in real time with their phones and maintenance engineers who report the repair actions the moment they do it also with their phones.

Operators select the equipment, component, defect and severity with a single touch on each of the pull down lists. The current time stamp is entered automatically and the expected repair date is also calculated automatically from the severity and defect reported date.

If a repair date is missing and the expected repair date is exceeded cells become red to warn the maintenance engineers. The moment the maintenance engineer enters the repair action the time stamp is also entered automatically.

Lists of pending repairs are obtained from this table with pivot tables to inform the maintenance engineers of all pending repairs and their due dates and severities.

We can easily extract  reports such as:

• Average repair times per severity
• Correlation between defects and repair actions
• Frequency of defective components for each equipment

### CONCLUSIONS:

• Paper recording can easily be replaced by widely used devices such as smart phones and tablets which require no operator training
• Easy  availability of these devices enables reporting in real-time and therefore real-time control of the process
• This enables the automatic recording of time stamps associated to each piece of data the moment it is entered
• Data entry is made much more simple and robust by eliminating data entry errors which are difficult and cumbersome to detect and correct at a later stage.

### Results

Design of experiments (DOE) to optimize wave solder parameters for complex multiple technology electronic boards.  Reduction of average wave solder DPMO from 25,000 to 5,000
Production scheduling optimization for electronic board assembly Total average lead time reduced from 20 to 15 natural days. Work-In-Process reduced accordingly.
Benchmarking on the materials planning and procurement process Average overall lead time from customer forecast received to purchase orders to suppliers reduced from 30 to 20 days
Value Stream Map of the RMA electronic board repair process to identify bottleneck and main contributors to lead time.  Overall repair lead time reduction to meet the targets requested by the customer.
SPC (Statistical Process Control) workshop and implementation of real time DPMO and FPY feedback to operators SPC real-time feedback implemented in manual insertion, wave solder and reflow solder lines. Manual insertion average DPMO reduced from 5,000 to 1,500. Wave solder from 3,000 to 500 and Reflow solder from 200 to 40.
Implementation of 8D analysis for mechanical problems in the telephone assembly process.  Identification and solution of root cause in the covers plastic injection molding supplier
Improve Functional test First-Pass Yield in the DVD assembly line     The root cause was identified and corrected. Testers were modified and FPY increased to meet the target.
Value Stream Map and optimization of the process to manage CoC (Certificate of Compliance) for components received and electronic circuits delivered to aerospace sector customers   Implementation of a robust (fail safe) process both to check supplier CoCs and generate CoCs for customer shipments.
FMEA and Control Plan workshop for aerospace electronic circuit assembly and test  Optimized Control Plan developed based on a process FMEA which gathered experiences from the different Assembly Plants.
Customer order scheduling optimization Scheduling optimization based on committed dates, parts and machines availability led to an improvement of On-Time Delivery and customer satisfaction.
Task force to optimize finished product packaging  Reduced the total number of different packing materials, reduced the number of suppliers and implemented a Kanban replenishment system with suppliers which reduced the inventory in the Plant.
SMED workshop to reduce the setup time in the high volume electronic board assembly lines for the automotive sector Setup time was reduced from 1 hour to 15 minutes in the FCM and GSM automatic component placement machines increasing the effective overall line capacity by 10%. On-time delivery was also improved as a result.
Implement a Kanban replenishment system to supply components to the SMT assembly lines for aerospace  Line stoppages due to missing components were eliminated and setup time was reduced.  Direct workload cost reduced 120k euro/ year
Reduce nitrogen consumption in reflow solder machines 33% reduction in consumption by automatic interruption when machines stop.
5S implementation in the Point of Sale Terminal assembly line Required space was reduced 50%. Productivity and flexibility increased.
Reduction of Dock to Stock lead time in the materials reception process Average lead time was reduced 50% by removing the pre-reception step and installing a visual system to insure all materials are received in less than 24 hours from truck unload. Work instructions were improved and new ticket printers installed to avoid labeling mistakes.
In-coming inspection lead time reduction  Lead time reduced 66% and inspection productivity increased.
5S implementation in the PTH lead forming line Stock out elimination and productivity increase
Autonomous production cell implementation with hourly control panel Production and quality hourly controlled at the production cell level led to an increase of productivity and delivered quality.
Solder paste dispensing stencil design automation  Engineering design time reduced from 4 hours to 15 minutes on average. Error rate was reduced and the effective design capacity of the tooling design department was increased.
5S: Individual tool box elimination task force Individual tool boxes were removed and tools assigned to each work station in a visual control panel to insure all tools required are easy to find and easy to put back. This led to an increase in quality and productivity
5S: Implementation of daily cleaning of all workplaces (5 min) at the end of each shift for all employees (both line and offices)  Improved productivity, company image in front of customers and work environment.
X-Box electronic board repair process optimization Value Stream Map load balance, stress test first-pass-yield improvement, BGA module replacement process improvement leading to double the effective repair capacity.
Optimization of the ticket printer assembly line: Value stream map, flexible line layout, one-piece flow, hourly production panel, component replenishment system.  Operator time was reduced 50%, Space required reduced 50%, Manufacturing lead time reduced from 24 hours to 3 hours. Total savings: 50 k euro/ year.
Optimization of electro medicine device test process: Value stream map, flexible line manufacturing layout, hourly production board, component replenishment system.  Manufacturing diagnostics were modified to group operator interventions during the automatic test so that one operator could handle two functional testers. Operator time was reduced 50% and the line could be operated at different speeds by adding operators. Total manufacturing lead time was reduced from an average of 2.5 days to 2 hours. WIP was accordingly reduced.
Setup time reduction for color change in the Rotocolor printers by doing a design of experiments (DOE) Model change Value Stream Map, Analysis of lead time contributors, setup optimization, identification of Rotocolor critical parameters affecting the resulting tone after baking
Lean diagnostics of the Company Workshops and bus maintenance service in city transport company Identification of productivity improvement opportunities: 5S, Visual management, Repair standardization and control, Total Productive Maintenance, Kanban spare part replenishment, etc.
Materials Dock to Stock lead time reduction and productivity increase Productivity increased and lead time reduced 50%
Machining tooling consumption reduction Standardized tool references with a tool catalogue and implemented a single database to control tool availability in the plant before a purchase order is issued to a vendor
Press availability improvement by reducing the number of breakdowns and downtimes  Applied TPM techniques to reduce breakdowns and optimise equipment maintenance
Install autonomous cell production in precision assembly line to increase productivity and reduce overall manufacturing lead time.  Productivity increased 25% and lead time divided by 5
Lean Six Sigma workshop to optimise Preduster/ Breading/ Fish frying line Lean diagnostics, 5S improvements, VSM: bottleneck identification, SMED and TPM projects identification to increase productivity
Pharmaceutical products manufacturing process optimization to reduce lead time and improve productivity. Lot size reduction, Manufacturing lead time reduction, Implementation of visual management and daily production scheduling. Job tracking by means of bar codes. Equipment availability and maintenance tracking.
Operating theater productivity improvement Lean methodology applied to operating theater management: Value Stream map, Setup time reduction, 5S, medicine and consumable replenishment, visual controls, Poka-Yokes
pH control of water discharge to river in copper mine processing plant Root cause analysis of pH fluctuation and actions to meet strict environmental requirements
5S and TPM implementation in orange juice processing plant  5S and TPM implemented in pilot area: orange receiving, cleaning and storage
Setup time reduction in hot steel mill for leaf spring production Off-line jig preparation with attachment screws. Hot dimensional control of first samples. Real time incident reporting and feedback.
Yield improvement in paper mill line On-line reporting of process parameters and line incidents to provide real time control and be able to analyze cause and effects. Associate defects detected on the rolling process to process parameters during its production sometime before. Losses before rolling reported, analysed and reduced.
Lettuce field collection productivity improvement Truck schedule optimization to spread deliveries along the day and avoid end of day bottleneck in the distribution warehouse. Effective collection to delivery lead time reduction