mohanpb0

Any Industrial Engineer who has attempted to conduct Time and Motion Studies in a factory would have experienced first-hand, the reverse Hawthorne effect of workers trying hard to make work stretch to fill available time, intending to get a smaller quota of daily work. While on the floor, the practice of Rating during Time Study can to some extent help the observer to arrive close to the correct time standard, this alone may not be sufficient to completely nullify the reverse Hawthorne effect.
 
In certain other situations, the fear of being branded as slow, uncooperative or having what is conveniently, if popularly called, “an attitude problem”, can make people work faster than normal. This can also happen when the people being observed are mis-motivated to impress the observer with their speed of working.
 
In both the above situations, it would be very difficult to arrive at the correct baseline for the process. The process of neutralizing these effects and getting people to work normally cannot start on the floor during the observation for baselining.
 
Setting the stage
This process begins with setting the correct environment throughout the organization for all staff to be normal and be themselves throughout without any fear of any kind of retribution. This can happen only if staff are genuinely convinced about this in their heart-of-hearts. These staff will get convinced only if the Management demonstrates its correct intentions and walk its talk. The right actions are more effective than a million words spoken or written.
 
Before the observation begins, all the staff who are being observed will need to be addressed by the organizational Management. The purpose of the observation and baselining need to be explained clearly and any questions asked by the staff during this meeting need to be answered completely and satisfactorily by the Management.
 
Draft Benchmark
Once the staff being observed are satisfied and are willing to cooperate by being normal during the baselining, a few other things will need to be done before the observation. The Management should try to get through their own network, an idea of the baseline for similar processes in other organizations in the same or similar industrial sectors. Further, the Management should take the help of one trusted staff or Supervisor to have the process executed away from the floor in private. Sufficient number of transactions need to be observed which are representative of the real-life day-to-day scenario. From both these sources, the Management will get a reasonable idea of the practices and the time taken for the process they are planning to baseline.
 
Rating during observation
Once this is available, the observation can then start with a trained person rating the pace of execution of work continually. The Management will need to use the draft benchmark to check if the results returned by their current baselining efforts are close to the one they have assessed earlier. This checking will need to be done atleast twice a day and can be done once an hour.
 
Feedback
Once the check is done and it is observed that the Hawthorne effect or its reverse is visible, then the baselining efforts will need to be temporarily either halted or results should be ignored temporarily. The staff involved in the process will need to be called in again and the Management needs to re-explain to them the objective of the baselining study and their cooperation needs to be requested again. The feedback needs to be shared with them that they are working too fast or too slow than normal and they need to work in the normal manner and pace for the mutual benefit of all concerned.
 
To summarize, the approach required to be followed to neutralize the Hawthorne effect or its reverse would constitute the following:
1.    Setting the stage with the staff being studied by a open and transparent discussion and ensuring that each of the staff are convinced about the necessity and the advantages of working at a normal pace
2.    Developing a draft benchmark by:
a.    Getting some external benchmarks from other organizations for a similar process
b.    Preparing a draft baseline by observing a reliable staff perform the process outside the floor
3.    Rating the pace of the work during the study
4.    Checking the results of the study with the draft benchmark atleast twice a day and giving feedback to the staff being observed if any Hawthorne effect or its reverse is observed

Standard Deviation indeed has a completeness when measuring dispersion in that every data point in the set is used in the calculation. Would this feature not definitely make it a generally better measure than others like Range, which touches only the two extremities of the data distribution and the Inter-Quartile Range (IQR) which touches just two other points in the distribution? Standard Deviation is a measure that indicates the spread of data from the mean, It is a measure that uses every value in the data set. Range, on the other hand, is the difference between highest and lowest value in a data set - hence, the measure uses only two values in the data set . Interquartile Range (IQR) also uses only two values from the entire data set, IQR = Quartile 3-Quartile 1.
 
 
Further more, two distributions with widely different variations can have the same range by virtue of having the same minimum and maximum values but it is very unlikely for two such widely different distributions to have the same standard deviation. Standard Deviation is not as hyper-sensitive to extremities and outliers as the Range. But Standard Deviation is not suitable when the sample size is very small.
 
This question can have only an affirmative answer, but that alone need not necessarily make Standard Deviation the best measure of dispersion under all circumstances. For example for non-normal processes, Range may be a more useful measure of dispersion than the Standard deviation, which is at its most relevant best in a Normal Distribution.
 
Additionally, when ordinal data are being dealt with, Range could be better suited.
 
Further, if at either ends of a distribution, there are open ended class intervals, the Range may be more appropriate.
 
IQR works better when there are outliers in the data as it discards the outliers by using the measure as IQR = Q3-Q1
 
Finally, when explaining concepts of variation to a diverse crowd consisting of staff at the lower levels of hierarchy, the Range may be easier to explain and understand.

Would like to stick to one example of a form required to be filled online following elaborate business rules. The complexity necessitated a check of atleast the critical, sensitive fields, which if mis-filled, could cause fatal errors. Very soon, the usual pressure of targets over-ruled "old-fashioned" quality intents and staff started submitting forms without even a cursory glance at what they had hurriedly keyed in.
 
The Quality team dutifully created a hard copy checklist to be filled in for every form which was supposed to make the Inputter check the entry before checking the relevant item in the list. Some sanity was restored and Quality stabilized albeit temporarily. But with the characteristic vigor in beating the system which Operations usually displays, the Inputters continued their "Rajadhani" speed data entry while filling in all checklists at the end of the shift long after submitting the form and committing the erroneous information to the customer.
 
The Quality team then got wise to this when customer complaints resumed and stepped in with random audits at different points of time during the shift covering various combinations of transaction types, time of the day, day of the week, staff and so on. The Auditor would pick up completed transactions at random and demand the check list. If the checklist were not available, the staff could face disciplinary action. Again there was a brief lull in customer complaints, which was all too short-lived as the Inputters began to take calculated risks.
 
The Quality team then made the checklist too online instead of the hard copy which was supposed to ease up the job of filling the checklist, but it proved to be mere wishful thinking. Finally, technology was brought in which prevented the Inputter from submitting the form unless the check-list was filled in and submitted. This was effective but Operations still complained about reduced productivity and rising costs.
 
More advanced technology was introduced which used business rules to fill in all rule-driven fields and left only those fields which required thinking and judgment to be manually filled in. The Inputters were now given other fancy designations like, "Validators" or "Integrators" and were sufficiently self-motivated to do their own checks of the manually entered fields, which improved Quality.

That the “Normal” in “Normal Distribution” or “Normal Data” means “Natural” rather than the other dictionary meanings like, “Ordinary” or “Typical” or “Regular” or “Usual” or “Standard” would become clearer if the origin of the so called “Normal” distribution is traced.
 
Somewhere in the 18th century C.E. a group of mathematicians and scientists in France were trying for a long time to make sense of a peculiar data distribution they had come across. They realized that one value was occurring most often and also that the other values lesser than and higher than this most frequently occurring value occurred at a progressively lesser frequency. In other words, as the value decreased from the most frequently occurring value, the frequency decreased and as the value increased more than the most frequently occurring value, the frequency again decreased. After a few days of research and discussion, they could not come to a conclusion and decided to take a walk in fresh air to clear their heads.
 
They came to an orange orchard which was full of trees bearing ripe oranges. Unable to resist the temptation, they plucked a few oranges and began to enjoy nature’s bounty. One of the group who was still thinking of the data, started to keep a tally of the number of seeds in various oranges he ate, using a twig for a pencil and the mud as a note book. To his surprise, he found that the seeds in the oranges followed a distribution similar to what they were breaking their heads about in their lab for the last few days. Quickly, he brought this to the notice of others who soon confirmed the similarity of the data distributions. It struck them that perhaps this distribution could be something that occurred naturally.
 
They tested this theory with certain other naturally occurring parameters and concluded that their theory was indeed correct. For reasons best known to themselves, they chose to name this distribution, “Normal” meaning that such a distribution occurred naturally. Or perhaps, the original French name given was translated as “Normal”. Whatever be the reason, it is now accepted that “Normal” distribution occurs naturally in many physical, social and biological processes. Therefore, if such measurements were made in a truly random manner, the data collected is expected to be naturally normal. Many a time, an apparently non-normal distribution, when investigated, would reveal some man-made cause like blending two distinct groups into one, or a skewed, non-random sampling and so on.
 
Apart from the usually quoted examples of Normality like heights and weights of people from a randomly constituted group, even product characteristics from a machine or a cell with untampered settings and without any technological restrictions, will be expected to be normal not just in their physical characteristics like length, diameter etc. but also in their functional characteristics strength, power, torque and so on. Additionally, medical parameters like blood pressure, blood sugar etc. are also expected to be normally distributed.
 
In the above situations, any distinctly non-normal distribution would need to be treated as unusual.

Based on the calculated Rolled Throughput Yield of 100%, the process cannot be inefficient. But there could be assumptions that are made in this calculation which may reveal the inefficiencies in the process. These assumptions may result in some convenient omissions, which end up boosting the Rolled Throughput Yield to 100%. Sometimes, these assumptions also lower the perceived design efficiency. Some of these assumptions are:
1.    Related to available time
The equipment or plant is available for operation 24 X 7 = 168 hours in a week, but the Management chooses or there is business only for running five days a week for two shifts of eight hours each. Within this time of 80 hours per week, the plant operates at 100% rolled Throughput Yield, but from an overall time availability angle, there is already a loss of around 52% ([168 – 80] / 168).
 
2.    Related to Capacity
There could be a batch process operating in a drum, with a design capacity of 80 metric tonnes. Due to poor maintenance, some residue of the charge has solidified inside the drum reducing its usable capacity to 70 metric tonnes. The Rolled Throughput Yield on 70 metric tonnes is indeed 100%, but w.r.t. the Design Capacity, the yield loss is 10/80 = 12.5%.
 
3.    Related to Scope
For most product types, the Rolled Throughput Yield is 100%, but for some product types, the yield drops in one or more processes. But these product types are scoped out when calculating or presenting Rolled Throughput Yield calculations.
 
4.    Related to Mode of Operation
Some low yield processes get outsourced so that the “organization’s” Rolled Throughput Yield remains at 100%. But if Rolled Throughput Yield is assessed for the End-to-end process, it may go below 100%.
 
5.    Related to Changeovers
Changeovers are “Non-production” times. So, these times are not included in the available time. If considered the right way, the Rolled Throughput Yield will be less than 100%.

The effectiveness of any tool is dependent on the user and method use. So is the case with the “Fishbone Diagram (FBD)” or “Cause and Effect Diagram (CED)”.
 
No tool can achieve anything not intended by the user of the tool. A tool can only provide different perspectives to the user to take a decision. It is very much possible for the user to junk the information the tool provides and go by his or her feeling. The “Fishbone Diagram” or “Cause and Effect Diagram” is no exception.
 
Misuse of a tool can also include erroneous use, which could be either a genuine error or an intended misuse.
 
Means to pre-conceived end - The most common misuse of the FBD is to doctor various bones so that all root causes that emerge are in line with decisions already taken. Logic can be thrown to the winds as each immediate and root cause are written so as to justify the decision. Effects instead of causes - Another common mistake people can make is to reverse the plotting of causes as a hierarchy of effects. Rather than progress causes from the effect to the root cause, it progresses through subsequent effects. Incorrect or inaccurate problem statement definition - A guess or an assumption is made when documenting the Problem Statement or Effect. Then with the effect itself not being very correct, of what quality can the supposed “Root Causes” be? Too much of guess-work in the causes - While all proposed causes are to begin with atleast, potential causes, if too many causes are all out of guess work or out of assumptions without a validation plan, then the likelihood of the problem being solved is next to nothing. Tracing back from the root cause - After reaching the root cause, by relentlessly questioning “Why?” a comfort syndrome results in picking up an immediate cause rather the root cause.  Using Solutions as Causes - To prepare a justification for investment in a solution, solutions end up getting prefixed by “Lack of”. Examples could be lack of automation, lack of maintenance support etc. Giving up after identifying one Root cause - Either due to the excitement of having identified a root cause or due to sheer laziness, it is possible to forget the basic tenet that one problem may have multiple root causes. Confusing correlation with causation - Mistaking certain commonalities in various instances of problem occurrence as the cause of the problem itself is another common error. Working to a strict time deadline - While no activity can go on endlessly, it is not possible to brainstorm and think through all root causes in a hurry or when wanting to close the meeting within a particular time. Many staff who participate will take quite some time to warm up and by the time they are ready to contribute, the meeting is over. Criticizing proposed root cause ideas - It takes free, unfettered thinking to arrive at all root causes. If the thought-process of the participants are stifled for any reason, the fish bone will not complete and thus not effective.  Holy cows - There are certain people or certain processes in the organization which are sacrosanct and cannot be touched, let alone be changed whatever be the consequences. Therefore, all root cause analyses stop at this point. “Out of control” causes - To be on the safer side and not end up with responsibilities, it is best if the fishbone analysis is guided to causes not within the organisational control at all so that no one in the organisation is tasked with the responsibilities of implementing corrective action. People related causes - Documenting clichéd people related causes like, “Human error” (Are animal errors possible?), “forgot” (Is the process so dependent on memory) will not help in resolving the problem. Focusing on “Who” rather than “What” - A classical distraction is to focus on who is the root cause instead of what.

Segmentation could be called as the process of dividing the population into distinct subsets or segments that behave in the same way or have similar features. As each segment is homogeneous, they are likely to respond similarly, within. For effective segmentation, segments need to be measurable (The very purpose is to measure effects within a segment and between segments), identifiable (This is mandatory if all data is to be correctly segmented), accessible (The efforts at segmenting should not become more than the benefits obtained by solving the problem), actionable (The segments arrived at must be practically feasible to work on) and large enough to be effective (Each segment should have a critical enough mass). The segments arrived at need to be based on a logic that can be related to the problem being investigated or business goals being pursued. These questions may help drive the analysis of the segmented data.
·         Is there one defect category that occurs more frequently than others?
·         What factors contribute the most to the variation in Project Y?
·         Do results differ across factors?
 
Segmentation, sub-segmentation, cross-segmentation and matrix-segmentation divide the data population into homogeneous data segments. Multiple data segmentation can be used effectively to isolate problem transactions that give us a handle to work on solving the problem.
 
The criteria for this segmentation can be the natural transaction categories in the process or specially created criteria.
 
In the case of the former i.e. segmentation along transaction categories, the population is split into various segments and extent to which the transactions in each segment have been impacted by the problem is measured. This will help in identifying those segments which are most impacted adversely by the problem. Thus, the problem segment or segments have been identified. Then, by identifying the characteristics and features of these problem segment or segments that are significantly different from those of the other segments which are not impacted by the problem, it is possible to identify those characteristic or feature that are most impacted by the problem. These could be the immediate cause of the problem. This will then need to be root-cause analysed and appropriate controls implemented. This way, it is possible to avoid shooting in the dark when trying to find the root cause of the problem. Segmentation along transaction categories has helped to narrow down the areas to be root cause analysed, thus saving time, effort and money in the problem solving exercise.
 
In the case of the latter, i.e. segmentation along specially created criteria, it is possible to formulate criteria along suspected or potential root causes. By segmenting the data population along potential root causes, the segment or segments impacted most by the problem can be identified and along with the segments, the root causes themselves can be identified. Here, by almost directly identifying the root cause, even more savings of time, effort and money can be achieved. Here, data segmentation is actually being used to verify root causes.
 
Going further on root causes, Segmentation analysis also assists us in planning and implementing different corrective actions for different segments that contribute effectively to improvement. A repetition of the segmentation post improvements and measurement of the problem impact will reveal the effectiveness of the corrective actions implemented.
 
Thus Segmentation Analysis supports preparation for, conduct of and verifying effectiveness of root cause analysis.

At a routine military inspection at an Army barracks, the Colonel inspecting the unit asked a new recruit, “What is the first thing you do, when cleaning your rifle?” to which the recruit answered, “Make sure that the rifle is mine”.
 
Behind the humour of the above-mentioned quip, is an important lesson to be kept in mind before embarking on a project, big, small or miniscule. One needs to make sure that he is on the right job. Identification of outliers in a data set is akin to what the army recruit rightly said.
 
If processes, be they in a factory or in a laboratory or in an office were all running as intended and planned, there would not be any reason to have problem solving measures as there would not be any problems at all in the first place. It is only because that this does not happen all the time, that data needs to be collected and problems need to be solved.
 
Problems are not exclusive only to the process being studied. They can also happen in the monitoring, measurement and data collection processes themselves. Due to problems in any of these, some transactions or parts can get impacted. It could be possible that there was a problem with the measurement device or gauge or software. Due to one or more of these reasons, the value of the metric for the one or few transactions or parts may go out of a normal expected range or be zero or the maximum value on the measuring device. Also, there could be errors in transcription or reporting.
 
Before embarking on an analysis of the data collected, or as a first step of the data analysis, it makes sense to check for possibility of any occurrences of the above. Mere occurrence of a low or high value of a metric need not make it an outlier. The situation and the other data need to be considered. For example, when collecting data on weights of normal male adults, if a person’s weight is recorded as 8 kilograms, obviously this is an outlier caused by a digit missing on any one side of the “8”. But when collecting data on weights of new born babies, weights even around two kilograms may not be an outlier.
 
If the data analyser does not identify, investigate and remove outliers from the data to be analysed, any measures computed from the data with outliers can be incorrect as most measures are sensitive to every data point in the data set. Further, any advanced analytical tools if used on data with outliers can return incorrect results and mislead the investigator and send him on wild-goose chases. Worse still, the investigator can incorrectly conclude that there are no problems with the process and thus discontinue the investigation.
 
There are popular formulae for identifying potential outliers in terms of the Inter-Quartile Range (IQR) and Quartiles that can help in a first level screening. For example, points lower than the first quartile by more than 1.5 times the IQR and points higher than the third quartile by more than 1.5 times the IQR are considered as mild outliers, while points lower than the first quartile by more than 3 times the IQR and points higher than the third quartile by more than 3 times the IQR are considered as extreme outliers
 
But all points identified by these formulae cannot be blindly called outliers and left out. The reasons for these outliers need to be investigated before a decision to ignore them or include them can be taken. Potential outliers can occur due to the following reasons.
 
Improvement Opportunity
As this is a valid data point due to an hitherto unknown cause, to be considered and further investigated
Genuine data error
Investigate the data point and correct the data
Malicious intention
Subject to reach and time available, interact with those who have misreported, motivate them to report the correct data and use it; if not feasible, can be identified as an outlier
Lack of standardisation
After confirming the reason, can be identified as an outlier
Uncontrolled sampling error
As the sample itself is incorrect, can be identified as an outlier

Knowing and looking for any of the seven wastes and just working to eliminate them will not be as effective as making waste elimination part of an organization-wide Lean Transformation Programme. Having been questioned by the Leadership on the best uses of the concept of Seven Wastes, would use this opportunity to also present my proposal for Lean Transformation in my organization.
Lean Transformation Program
1.    Mission Statement
a.    Derive Lean Transformation Mission Statement from the Organizational Mission Statement
b.    Achieving the Lean Transformation Mission will take the organization closer to achieving its Mission
2.    Objectives
a.    Derive lean Transformation Objectives (Quantified, Tangible) from the Mission Statement
b.    If the Objectives are fulfilled, the Mission will be achieved
3.    Governance Structure
a.    Steering Committee
b.    Working groups
c.    Frequency of reviews
d.    Roles and Responsibilities
e.    Creation and Approval of Lean Roadmap
4.    Steering Committee
a.    Responsibilities
b.    Authorities
c.    Targeted Savings
d.    Budget approvals
e.    Training plan
f.     Socialization Plan
g.    Plan for leveraging Technology
h.    Base-lining of current costs
i.      Target setting guidelines for Working Groups and Projects
j.      Norms for Rewards and Recognition
5.    Working Groups
a.    Responsibilities
b.    Authorities
c.    Expenditure approvals
d.    Target finalization
e.    Norms for identifying Lean Transformation projects
6.    Training of Trainers
a.    External or Internal training of In-house trainers
b.    Preparation of gamified training material, props, artifacts, audios and videos by these trainers for training other staff
7.    Lean Awareness Sessions
a.    Plan for introducing all staff to basics of Lean Management
b.    Training to be done by trained trainers
c.    Weekly and Monthly Awareness Training coverage report vertical-wise and for the organization
8.    Training in Lean Methodology
a.    Training of staff on Lean methodology including waste identification and Value-Stream mapping in batches
b.    Games and Exercises inbuilt in the training session
c.    Lean Project completion mandatory for certification after training
9.    Socialization
a.    Roadshows, Standees, Posters, Videos
b.    Competitions and Quizzes with Prizes
c.    Sensitize all staff to become intolerant to any waste
10.  Project Identification
a.    Lean Transformation project identification in all verticals
b.    Cross-Functional Lean Transformation Project team formation
c.    Identified projects to be reviewed by Vertical Working Group and cleared for kickoff
11.  Project Execution
a.    Progress of Lean Transformation Projects in a structured manner
b.    Structured approach to executing Lean transformation projects by Waste Identification and Elimination including
                      i.    Scoping the project
                     ii.    Base-lining current costs for process being improved
                    iii.    Documentation of existing process steps in detail using appropriate tools like SIPOC, Process Maps
                   iv.    Preparation of Current State Value Stream Map
                    v.    Analysis of Current State VSM
                   vi.    Waste identification in Current Value Stream
                  vii.    Preparation of Future State VSM
                 viii.    Risk analysis through FMEA and other Tools, Control evolution and implementation
                   ix.    Kaizen blitzes to eliminate identified wastes
                    x.    Implementing the improved process after selective, controlled piloting
                   xi.    Preparation of (Now) Current VSM, comparing it with (Earlier) Future VSM and identifying further opportunities 
                  xii.    Handing over the improved process to the Process Owner
12.  Project Reviews
a.    Review by the Working Group at different stages and after different milestones
b.    Review by the Steering Committee after project completion
c.    Identifying opportunities for reading across and replicating Best practices in other verticals
13.  Auditing of benefits
a.    Auditing of the benefits by Finance and validation of the same
b.    Lean Transformation Project Benefit validation report by Finance
14.  Rewards and Recognition
a.    Recognition of project team members depending on benefits obtained and impact created
b.    Annual Lean Transformation Celebrations
c.    Embedding Lean Transformation in the organisation’s DNA
15.  Continual Lean Transformation Programme Improvement
a.    Periodic review and audit of Lean Transformation Programme Methodology vis-à-vis audited benefits
b.    Leaning out the Lean Transformation Programme
c.    Continually improving the ROI of the Lean Transformation programme

Frida Kahlo, the Mexican painter once said, “Nothing is absolute. Everything changes, everything moves, everything revolves, everything flies and goes away”. The first part of this statement is applicable to all subjects, including, “Rework”. While, “Rework” is traditionally considered a waste, in the following situations, “Rework” may be less unwanted.
1
Innovation
By definition, innovations or the first attempts at it cannot give exactly the desired results. Unsuccessful innovations are required to eliminate failure modes and rework leads to a marketable product
2
Product Development
When factors outside the control of the organisation change, like tighter laws, rapidly evolving customer requirements etc., rework is almost mandatory to sell the product
3
IT Coding and Development
Especially when large applications are developed over a period of time, the circumstances under which decisions related to development would have been taken would have changed with time. While under the set of then prevailing conditions, the decisions would have been correct, in the present circumstances, the same decisions may warrant review. Upon review, if these decisions are found to be not appropriate to the current circumstances, then they may need to be changed and the work impacted may need to be redone. This is preferable to trying to avoid rework and ending up releasing a poor application with known issues.
4
IT Testing
As part of testing a unit or a module of an application, if say Mixed case incorrect input is not detected as an error by the code, it is advisable to rework the code to further increase its robustness
5
Ignorant Market
When no one including the potential users know what they need, the only way to create and supply what is needed is by experiments, testing the market with prototypes, feedback on failures and rework
6
Painting or creating other works of art
Perspectives are available only after creation of the entire work of art or atleast part creation. Rework for improvement is the route to the success of that work of art
7
Cooking
When testing the soup to be served at the beginning of a huge dinner, if the soup is found to be too salty, rework in the form of dropping a few quartered potatoes over a simmering flame is essential to de-salt the soup and make it consumable
8
Wine making
Wines are known to get better with age. If a wine has not been sold after the targeted ageing (say) ten years, its price may further increase as in the additional waiting time the wine would mature more and thus become better. This additional ageing is a kind of preferred rework.

In any business, performance is typically expected to vary over time and w.r.t. inputs. When comparing two performances, it would not be completely correct if a decision that the performances are different were to be taken based on comparison of just one or few data points from both the performances. Sampling errors should not influence the decision. Therefore, it is essential that the correctness of the decision taken should be sustainable over time.
 
For the decision to be sustainable, data that reflect the sustainability of both the performances will be required. Once this data is available or is collected, the decision based on this data is also expected to sustain over time. The decision that is taken based on samples must hold good for the populations also. In other words, even after some unavoidable overlaps of both the performances, perhaps due to chance causes, the difference in the performances of the two populations must be visible, conspicuous and clearly discernible. In other words, the difference in the two performances need to be significantly different.
 
But “significance” is quantitative and statistical. The significance of the difference is assessed from statistical data of the two performances. Statistically significant difference represents the clarity or discernibility of the difference between the two performances and the sustainability of this difference over time. Performances of two populations with a statistically significant difference will remain different over time unless there are some special causes in play on one or both of them.
 
But how significant is significant? This depends on the objective of comparison and the stakes involved. The margin of error tolerable in taking a decision on the difference between the performances depends on these factors. For different combinations of conditions, this margin of error could be 1% or 5% or 10% or any other agreed number. This is the error involved in the decision to conclude that the two performances are significantly different based on the available statistics.
 
Uses of the concept of Statistically Significant Difference in Problem Solving and Decision Making
The uses of this key concept of “Statistically Significant Difference” to solve problems and take decisions are innumerable, a few of which are given below.
 
1.      Comparison of performances between two or more
a.      Time periods
b.      Processes
c.      People
d.      Suppliers or Service Providers
e.      Applications
 
2.      Assessing effectiveness of
a.      Training
b.      Improvements
c.      Corrective Actions
d.      Action taken on suspected root causes
 
3.      Evaluating
a.      User ratings in market surveys against marketing campaigns
b.      Performances of new recruits against agreed targets
 
In all the above cases, Hypothesis Testing can be effectively applied to assess the existence of a statistically significant difference.

Some of the situations in which the "Push" system is generally successful would be one or more of the following. No two situations are the same, even if some appear similar.
 
1
Demand is easily and accurately predictable
Due to an accurate forecasting system, the risk of carrying “dead” inventory is less. Moreover, by planning and pushing a steady volume to the market, supply chain and production are also steadied, thereby eliminating delay losses.
2
Conversion costs between products is low due to late point differentiation
If in spite of an accurate forecasting system, there is a difference in the final product type demanded, the stock of Product A can be converted to Product B at a very low cost and pushed on to the market.
3
Very short time demanded from order to delivery
If a very short delivery time or instant delivery from the point of time an order is placed is demanded by the market or customer, there is no option except to supply from stock and avoid revenue losses due to short supplies.
4
Products do not deteriorate during storage
When there is no constraint on “shelf life”, the risk of inventory to be written off is low. Further more, inventory is being used up sooner rather than later, reducing cost of delays.
5
Carrying cost is less than cost of lost business
When a manufacturer is able to make up for the expense of carrying inventory by exploiting the predictable demand, the likelihood of profiting, “net-net” is high when compared with the potential loss of business, customers and reputation by becoming Just-Short-Of-Time rather than Just-In-Time
6
Long, geographically global supply chains with their own unpredictability
Even with the best e-Kanban-powered pull system, the long winding, supply chain that traverses the entire globe is so packed with potential “delay-bombs”, that some “good-old” stock, which can be pushed becomes the life-saver
7
Shipping costs can be optimised by shipping in bulk
When the costs of transporting raw material or components or sub-assemblies can be whittled down to almost next to nothing by using up (say) full container space, stocking up and pushing is not a bad idea
8
Demand profiles across time periods are static
When there is no fluctuations between days of a week, weeks of a month and months of an year, it is profitable to stabilise production and supply chains by planning and pushing an average volume periodically to the market

The SIPOC is one of the most useful, yet one of the most maligned tool. Like with any tool, merely preparing a SIPOC will not yield much benefit. One should consciously attempt to draw maximum benefits from it.
 
The SIPOC is pocket treasure trove of process information, which is all sufficiently all-encompassing in its completeness, yet manageably small in size, both physical and electronic. The traditional SIPOC has information on Suppliers, Inputs, Process, Output and Customers, while the enhanced SIPOC has a whole lot of additional, related information like process step times, who does what step, the team size and distribution across shifts, the average volumes of these transactions, the qualifications of staff for this process, the training required and so on.
 
The first beneficiary of a SIPOC is the person or team preparing it. Documenting multiple aspects of a process makes the SIPOC creators first understand their process better  and also raises various questions in their mind. They will either find answers to these questions and better their understanding or identify opportunities for improvements in the unanswered questions.
 
Once the SIPOC is complete and is available for viewing or study by all, this opens up more and more opportunities for improvements. All the potential triggers required for stimulating improvement ideas are self-contained in the SIPOC. There are the process step times, which can provoke improvements to automate and reduce work content. There are various stakeholders in the process, which can trigger efforts to simplify the process. There are staff qualifications and skills required, which can initiate deskilling programmes. Every data point or piece of information in the SIPOC has a lot of value in the current context as well as in the outlook for improvements.
 
Additionally, the SIPOC is a most excellent training tool both for the Senior Management who may want to have a bird's eye view of the process to take certain strategic decisions or for the hands-on trainee who wants to understand the process step by step to execute them.
 
To summarise, the SIPOC being a repository of process information is a wonderful documentation, improvement, knowledge management and training tool.