The Infosys global supply chain management blog enables leaner supply chains through process and IT related interventions. Discuss the latest trends and solutions across the supply chain management landscape.

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October 29, 2010

How far "Pull" concept should drive your Supply Chain Activities?

In this blog I will try to answer one of the most pertinent question every supply chain operating manager face in his or her day to day working. To set the context consider a simplistic model of a manufacturing supply chain.


In this supply chain on extreme right we have finished goods warehouses from where dispatches happen to end customers (Also called 3rd Party Customers). These finished goods warehouses draw the goods from regional warehouses. Regional warehouses in turn order the goods from finished goods factories depending on which product is produced in which factory. Few products can be ordered from subcontractor as well. Finished goods factories require some raw materials (or components) and some semi finished products. For example - If finished goods factory is manufacturing car then gear box assembly (A semi finished product) could be coming from some other factory. Semi finished factory in turn will require raw material and components for its operation which it will order from supplier. This is logical flow of demand. Now let us consider forecasting flow.


At finished goods warehouse demand forecasting is done for a reasonably long horizon, generally for 24 to 36 months into the future. Assume lead time of 1 month between finished goods warehouse and regional warehouse. Regional warehouse will see the demand forecast 1 month before finished good warehouse. Lead time could be different for different finished goods warehouse. So ultimate demand picture of regional warehouse is combination of all these lead times. Same will happen at factory level. Factory will see demand off set further based on lead times between regional warehouse and finished goods factory. Factory will have its own manufacturing lead time so semi finished factories will see demand even further before (to the extent of manufacturing lead time plus transportation lead time between finished goods factory and semi finished goods factory). And finally raw material and component demand will have additional lead time offset added because of semi finished product manufacturing lead time. It may so happen that supply chain cumulative lead time offset could become extremely long. Raw material we are ordering today at semi finished factory could be for a finished good warehouse demand for 3rd party customer which is 18 to 24 months into future. My premise is as follows....


"How logical is it to carry out all your today`s supply chain operations at upstream warehouses and factories based on a demand forecast for a period which is more than 12 months into the future?"

But this is what we generally do....right? We think there is no alternative. Famous "Pull" concept of supply chain management tells us to do so. We are told that if we do not do this we will end up building unwanted inventory in the supply chain, inventory is a "muda", it affects our responsiveness, it's bad for quality and so on. But think for a moment, is this logical? Put yourselves in shoes of a demand manager at finished goods warehouse. What is your prime motivation when you do demand forecasting? It is to do forecasting correctly so that you do not face stock out situation. You will try to be 100% accurate for next one month (If lead time from regional warehouse is 1 month). You will try to be reasonably accurate for next 3 to 6 months because you have good market intelligence for that period. You will attempt to be accurate for next 12 months because all the financial results are computed for a year and that has bearing on your performance appraisal. But beyond 12 months do you really care what is your forecast accuracy? You give this forecast more as obligation for upstream players to plan their work with almost zero interest in its accuracy. You always know that you will have multiple opportunities to revise this forecast before you are held accountable for its accuracy. If forecast beyond 12 months is developed with this philosophy, how fair is it to base your upstream operations based on this forecast? Answer in my opinion is clear. "Pull" concept should not drive supply chain if demand forecast beyond 12 months is used for driving it.

There are many arguments put against about above statement prime being "We know that forecast accuracy beyond 12 month at individual finished goods warehouse is wrong but when aggregated at regional warehouse level or at factory level it will balance out". Which means one finished goods warehouse will have positive forecast error and other will have negative error hence on an average accuracy beyond 12 month will be as good or as bad as it is in first 12 months. There are statistical theories to support this argument. This is not a convincing argument in my opinion. Forecasting is as much a science as an art. Final forecast number is arrived using algorithmic logic, market intelligence and gut feeling about future. Forecast beyond 12 month is based more on gut feeling for future. It has more macro considerations than micro. These factors are more or less same whether you are demand manager at one finished good warehouse or other so evening out of forecasting errors rarely happen beyond 12 months. So what is the answer?

In my opinion we should apply following methodology at each node in supply chain.

1)    Determine forecast accuracy at each node. For example, if you are say buyer for raw material at semi finished goods factory, compare demand thrown on you through "Pull" with actual consumption. Demand here means propagated "Pull" demand that comes on this raw material from forecast done at finished goods warehouses. By consumption I mean goods issued to factory for doing production.

2)    If the forecast accuracy for this raw material is drastically different than short term forecast accuracy  at finished goods warehouse, no point in using "Pull" concept at this node. Why should this node deal with more uncertainty than finished goods warehouse? Additional drop in forecast accuracy at this node has nothing to do with market uncertainty. It is induced by "Pull"

3)    Instead of using Pull demand create fresh demand forecast at this node based on consumption history

Planning done on the basis of this "independent" forecast will be much more accurate than planning done based on "Pull" demand. Do this analysis for each and every node in your supply chain where you do demand and supply matching in your supply chain network. You will be amazed to see that 70% of the nodes, mostly upstream, will fall in this category. Managing them as an "independent" forecast entity will do world of good for their planning than managing them as "Pull" entities. In few cases putting a strategic safety stock where forecast accuracy is a problem is much better approach. In summary just delink each node from being demand carrier for upstream. Make demand manager at each node a real "Demand Manager" rather than converting him / her into a postman who merely transfers the demand upstream by doing inventory netting. If you organize demand management this way, everyone is responsible for forecast accuracy rather than just handful of people.

A somewhat unconventional thought.



Crunching Procurement Cycle Time Through Business Workflow Optimization

Business workflow plays a vital role and is one of the time consuming step in procurement life cycle. Shortening procurement life cycle has multi-faceted benefits such as faster procurement request processing, shorter procure-to-pay cycle time and processing cost, avail ling early payment discount enabling efficient working capital management, enhanced customer-supplier business relationship. Crunching procurement cycle time requires special attention in business workflow optimization and reduction in approval time.

In one of our customer's procurement landscape, there was an imminent need for revamping business workflow to shorten approval cycle time for better efficacy of procure-to-pay cycle. We adopted following steps in achieving business workflow optimization:

a)      Categorization of commodities and approval limits to map to organization's approval requirements as per procurement policies and regulatory compliance requirements

b)      Adoption of role based approval nodes for commodity category while configuring business workflow in procurement platform. This simplifies overall business workflow there by cut down overall approval time

c)       Institutionalization of multi-dimensional approval authority (one person having multiple approval limits based on combination of factors such as commodity used, accounting hierarchy used in lineitem distribution, expense accrual general ledger) simplifies business workflow by cutting down number of approval nodes

d)      Short-circuiting business workflow such as partial or no business workflow for change orders duly complying with organization's procurement policies for change orders

To reduce the overall approval time, business workflow optimization and quickening request approval process should go hand in hand. Accessibility of approval requests and on-time action on it will shorten the approval time. The procurement platform should be capable of accepting requests from email and handhelds. Purchase requests should be accessible via mail, handhelds so that approver can approve requests from anywhere without logging in to the system. Also periodic generation of approval cycle time reports, capturing details like time to hit approver's inbox and time to get out of approver's outbox, number of approval nodes, overall time required to fully approve the request is required to collect various approval process data. Regular circulation of system generated reports and emails capturing snapshot on pending approval requests to approvers would help tracking pending requests awaiting their approval. Such reports help in analyzing the overall approval process efficiency and identify scope for improvement and further tuning.

Adopting above mentioned combination of steps followed by aforementioned calibration mechanism, helped our customer to reduce approval cycle time by more than 50% and thereby improved the procure-to-pay cycle efficiency. The calibration mechanism is helping to consolidate the benefits further and easing business to adopt Procure-to-Pay cycle in other departments & business units.

October 25, 2010

Impact of Social Media on Supply Chain

We recently concluded our All Hands Meet where I got an opportunity to meet my fellow colleagues from a practice called Next Gen Commerce. As the name suggests, it is associated with clients that are aggressively forward looking and ready to take bold steps in leveraging new technologies and venture into new domains to gain consumer mind share. One of the great things that I heard was about how companies especially in developed economies are trying to capitalize or monetize information that's available on net - basically using what we all call "social media" to gain competitive advantage.
While I was thinking all this and trying to imbibe all great things that I heard, I was wondering if there could be an impact of social media on supply chains. This blog of mine is therefore of an exploratory nature (since I have little awareness on this subject) where I would like to share my thoughts and seek your inputs and comments.
I am sure Social media is something that all of us know in bits and pieces, and it touches our lives today in some form or the other. The typical examples of social media are facebook, twitter and blog sites such as ours. Some of these are subject-specific and lot of these are fairly generic and provide a platform for people to share their experiences, ideas and opinions in this borderless world. Although, it is just a platform for people to exchange information, but I am sure, it has a powerful influence to businesses and of course, to supply chains.

The extent of impact of social media to supply chain may vary across industry segments, for e.g.-  companies in a typical service industry or consumer goods are definitely more prone to get affected than others. While I am not going to detail on where the impact is felt more, but in general, there are few questions that come to my mind when I think about all this:
1. Which are the areas within supply chain that are likely to get affected, and why?
2. Have companies realized this trend or still think it as relatively premature to act on?
3. If companies wish to leverage social media for improving supply chain performance, how to actually operationalize such a vast amount of data that's available to us
4. And so on and on.....
Let me share my thoughts specific to just point no. 1 listed above.
 In my understanding, unlike other traditional media such as print and television, social media is one of the channels where people can freely express their opinions about the various products and services used. The consumers become brand ambassadors and companies are left with no option but to manage perception created due to all this. Social media acts as a strong medium to influence consumer behavior and thereby company's sales. Let's take a simple example: you wish to buy an electronic product, what are the immediate steps that you are likely to take. Look for the various ads, see the commercial, and talk to friends and relatives to gain more information about competitive brands. What are the chances of you likely to go to a social media site and search for this product/brand that you wish to buy and read various comments that are posted? I think the chances are high; most of us do that, and very often get influenced by what others have experienced...
Take another example: suppose you plan to buy food products or anything like that for your baby, don't you think, you would definitely reach out to your friends in your network to seek more information. This being a different type of product category may drive people to closed-group communication rather than going out in public to seek information. Such closed-group behavior (just like 'group think') is easily enabled through social media sites that are present today, and therefore, will really impact a consumer's buying behavior.
The two examples that I shared show how company's sales may get influenced by interactions/communication that happens on social media. If I need to link this behavior to understand its impact to a supply chain process, the closest that come to my mind is 'demand planning'. We all know that the key aspects of a typical demand planning process include 'sensing' and 'shaping' of demand. As per AMR Research's definition, Demand sensing is the amount of time it takes to see true channel purchase or consumption data and Demand Shaping is a series of focused activities to drive and improve revenue. Demand sensing is a critical input to make Demand shaping process more effective.
Marketing professionals spend dollars on research and promotions to understand and influence consumer behavior to create pull and increase company's topline. As more consumers move to social media and start using networking sites to influence their decision making, I think, marketers have an opportunity (and of course the associated challenges) to sense changes in demand more quickly and capture this new trend in their demand shaping process. Demand planners need to design this additional feed of information from marketing team in the overall demand planning process in order to capture the latest consumer behavior and feed it back to supply side. To me, this is a great thing to happen since now, we have the information directly from consumers (no intermediaries or research agencies are involved) at a much faster rate.
Having said that, I think there are many challenges to face, before this information is really put to use. The foremost being "How to do all this or How to operationalize". Issues like Data quality, loads of data, more noise in data, etc etc need to be managed... and I really don't have answers to all these questions.
So, I urge all of you to share your experiences and especially the examples if you have seen companies leveraging social media in improving supply chain performance. Please do share your comments, thoughts and feedback on this subject. I am sure there are many other facets of supply chain where businesses will see the impact happening; definitely areas where a company works with other trading partners such as suppliers, logistics providers etc. It is just a beginning of a new supply chain world and this will definitely result in addition of a new chapter to most of supply chain text books we have today... Look forward to hear from you all.

October 22, 2010

Sourcing from China: Part-1: Testing your fishing skills: stages of your sourcing maturity

Sourcing is growing as the main highest growing profession in China. Even though the volumes are low but the growth factor of these professionals at above 20% is the highest among all professionals in China. But for companies to start sourcing from China, they need to go through a 3 stage journey to finally fully integrate China as an integral part of their sourcing decisions. Check out the stage where your company fits in, and you will know how much road is required to me covered to reach the final destination. But many would agree that sourcing from China is not a destination but a journey, as new challenges emerge as the progress continues. Sourcing from China is like fishing.... If you don't agree read on

Stage-1: Buying the fishing Rod

In Fishing parlance this is the stage when you go ahead; gather info about fishing techniques, locations of fish availability, fishing rods, the fly-line, the fly-reel, the guide, the ferrule, the bait etc. You would read books and would surely consult experts and discuss in the family to take a collective unanimous decision. The decision is taken about what fishing rod to buy, where to start fishing, whom to take along and what fish to catch. Needless to mention you would start with small pond fish as usual.

In sourcing stage-1, the process is similar. Before an organization forays into China, the Top management is involved throughout in all activities of planning to implementation. First a thorough study of the attractiveness of China as a sourcing location is conducted and debated. Then the decision is taken on the parts to source. The organization here starts with standard catalogue items or Class-C items, which has abundance of incumbent local suppliers.  A small team is set-up with a small office, with small targets, but a huge amount of internal organizational focus.


Stage-2: Fishing on the banks

After you have done the initial preparations, you start with fishing on the banks. You select a lonely quiet spot on the banks of the location with abundance of fishes and sit there for hours to "fish". This stage requires a lot of patience. Also it is here that you start developing some of the skills.... Sensing instincts, speed of retraction. This stage is much unstructured and you would falter on a number of days. Patience and perseverance is required to develop the "fishing acumen" to clear this stage.  Each day's results are enthusiastically discussed over long hours and critically analyzed.

In an organizational activity of China sourcing in Stage-2, the organization starts mobilizing the small team to start sourcing. They begin by preparing directory of possible potential suppliers and supplier evaluation procedures. Since the sourcing team has a "small" target, they look for niche locations with large collection of potential suppliers in this locality. Many a times their expectation about suppliers is "dashed" and they would return "empty handed" without getting a supplier of choice. But each attempt of getting a supplier is analyzed thread-bare, both by the small sourcing office as well as by the Organizational headquarter, to define future strategies and tactics. For any selected suppliers, small pilots are also planned and executed. These sourced parts are again thoroughly scrutinized and inspected/ tested before being put to use. All types of comparisons are made in terms of quality, reliability, cost advantage, delivery stability, investments etc.  Though there is less action on the ground in China, but a lot of analysis of these actions takes place in the sourcing offices and headquarters, under direct vigil of Top management.


 Stage-3: Foray into waters

As the "budding fisherman" gets the pulse of the waters, and the initial successes urges him to start adopting different advanced techniques of fishing.

·         Some adopt combat fishing, where they would foray into water with their fishing rods with many other fishermen and attempt at catching the highest load among them

·         Some adopt pole fishing, where they would be perched on one of the poles in the water and fish

·         Some adopt net fishing, where they would foray into waters with their fishing nets and catch fish

·         Some, who are more adventurous would deploy under-water fishing, where they would dive with SCUBA equipments to catch fish

·         Some would take boats or trawlers to catch the "big fish"

This nature of foraying into water is very competitive and fishing is done either as individuals or in groups, manually with rods or nets or in boats or trawlers, and they would either catch river, deep sea or underwater fish.

Sourcing from China in stage-3 also is a very competitive show. This stage happens after the success of pilots and the skills developed during that stage. The office team expands into separate purchase and development teams.  In some cases the development teams would outnumber the purchase teams. Organizations start integrating their suppliers into the product development stages, to get the added benefits of material substitutes and alternate production stages customized to China. Some organizations would be more aggressive to set up R&D centers and even shift their Purchasing headquarters to China, as their volumes of purchase increase leaps and bound. Each organization adopts their most convenient method in this stage of sourcing to extract the maximum benefits. This is the stage where organizations even start developing cut-throat competition with other "buying organizations" to push them out of business. But many others would come into some alliance or partnership to leverage and tap the maximum potential. Some would make their China office a key part of their Global Supply Chain. The products being sourced would be more complex and customized products, instead of the standard catalog products with which they started.


As mentioned earlier, sourcing from China is a destination for some organizations, when they reach stage-3 of their sourcing journey. For some the sourcing is only a journey, where they continue to accept and overcome higher and stiffer challenges of sourcing and continue in their effort to integrate China in their global sourcing strategy and business plan. Do you also see yourself in one of these stages of the sourcing journey in your attempts in China sourcing? Is there any stage which I may have missed which is of primary importance to you? Do write back and let me know about where you see yourself in China sourcing. Part-2 of the blog sequence will be dealing at challenges in these stages and how we have seen organizations adopt simple and complex ways to overcome them.

October 6, 2010

Will best of breed EAM packages be taken over by ERPs?

Couple of months ago, one of the colleagues from my previous organization was discussing with me on this topic. His point of view was that now these days ERPs (SAP & Oracle in addition to few others) are coming with almost all the features which EAMs provide. CIOs do not want to handle the complexities associated with multiple pack ages & vendors, unending complex integration issues, and of course IT cost escalations. Hence ERP packages would soon dominate the asset management space as well and there may not be many takers for EAM packages. He also ratified his point of view by his recent experience with two of his prospective clients opting for an ERP instead of an EAM; SAP being selected over maximo in both these cases.

Though I am convinced that EAM packages do have a bright future and would continue to have a large user base, yet a best of breed EAM package being my bread & butter for last one decade forced me to revisit & endorse my views on this. So, I wore the hat of a package evaluation team and considered following facts -for an EAM package selection:
• Within an organization, Business decides the solution and IT is the facilitator
• The packages are evaluated based on the business process fitment & best practices
• Ease- of - use : Change management also plays an important role
• Maintenance departments are no longer perceived as cost centers and single asset failure even for a second may be very expansive to afford
• Of course, total cost of software & related implementation would also be a deciding factor
The ease of use is one of the most important factors which should be seriously looked into while selecting a package for production & maintenance department users as most of these users mainly work on the shop floors, operating heavy critical machines and they do not have luxury to sit in offices and explore software packages. The software, they use, should assist them in entering & analyzing the data quickly & easily. Most of the time, these production assets are operated on 24X7 basis, with people working in shifts to get maximum output without any damages to human life & surrounding environment, hence software should be built for users rather users accommodating themselves for software.
Considering these high level points, I compared ERPs & EAMs in general without getting into specifics of individual packages and what I found is as follows:
(1) ERPs are mainly developed keeping in mind of business needs of accounting, material management, manufacturing, sales, finance & payroll processes etc.
(2) While ERPs continue to add EAM features to their products, EAMs are also not lagging behind. EAMs, specially maximo, have added many more unique features, industry specific solutions within the product e.g. liner assets, various other industry solutions etc, in last few years.
(3) EAMs cannot be used in silos and they would need integration at-least with financial function of an ERP or client's legacy financial system.
(4) ERPs tend to be rigid in terms of ease of use (specially for shop floor users), configurable option and customization possibilities, as compared to EAM packages. EAMs provide an easier navigation & presentation layer to deal with.
(5) Maintenance operations are becoming more complex & mature day-by-day and need robust software solutions to take care of business

Considering all of the above, I found that both ERPs & EAMs would continue to gain more maturity and EAMs would be one step ahead of ERPs in terms of overall benefits for asset intensive organizations. If implemented, EAM module of an ERP, often take a back seat against much heavier accounting & manufacturing modules, and fail to provide the real benefits.  EAMs, with their rich functions, ease of use, best practices & business process maturity would continue to be selected by asset intensive organizations. But at the same time, EAMs also have to find the easier way to integrate with ERPs so that businesses can take advantage of both the solutions together resulting into ERP+EAM rather than ERP Vs EAM.

Additionally, we need to realize that in today's world, an EAM package is not about just creating some asset records and doing regular PM jobs etc. The technology is advancing, world is getting interconnected, assets are becoming smarter, problems are getting more & more complex (not to forget the recent oil spill), hence the businesses need smart & intelligent EAM packages rather than just a maintenance module. So long live EAMs.

October 5, 2010

Is "Higher Forecast Accuracy" the silver bullet?

To answer this, lets take a step back and try to answer a more fundamental question - Do we need forecasts? Our immediate response to this will be YES. And for most of us, the response will be based on the following key challenges most of the Supply chain professionals face across multiple dimensions:
1. The drive towards Globalization has resulted in the focus to not only look at the developing markets for cheap supply, but also to tap these developing markets to drive future growth
2. Increasing lead times and lead time variability with most of the manufacturing bases of suppliers outsourced or offshore
3. Increasingly demanding customers with information at finger tips [thanks to internet] and lower brand loyalty
4. Intense competitive activity driving lower prices and reduced scope for differentiation
5. Increased pace of product innovation - rapid new product introductions combined with rapidly reducing product life cycles
Both the Product Supply Chain and Information chains are getting longer, making the task of the Supply chain professional challenging… and the need for Forecasting and Demand Planning more and more necessary, to ensure a steady flow of products to the right place at the right time in the right form.

Now that we agree that we need to forecast, lets take a quick look at what the Forecast entails…

In a book ‘Dance with Chance: Making luck work for you’ (published by Oneworld Publications in 2009 and written by Spyros Makridakis, Robin Hogarth and Anil Gaba) I recently read, the authors state the process of Forecasting whether carried out by people or models, is to identify some pattern or relationship amongst the relevant variables and then extrapolate from these the future. Identifying patterns intuitively is what humans do constantly and well. Models do systematically what humans do intuitively. The data available however is a combination of both these patterns and the noise [or the element of randomness].
The goal to achieve higher Forecast Accuracy gets challenging taking into consideration the following key principles:
1. The future is never the same as the past and hence a straight extrapolation will not always be helpful
2. The available historical data contains the underlying pattern inter-mixed with the noise [random element]
These principles imply that the goodness of fit of the models to historical data has little correlation to the potential of the accuracy of the forecast generated by these models. Also complex statistical forecasting models run the risk of over-fitting to the historical data and mistake patterns for noise.

Having briefly looked at some of the key aspects of Forecasting, lets now explore the concept of uncertainty, before we proceed to address the question with which we started…

As mentioned earlier, the available data is composed of both the underlying pattern or relationship and the noise or the element of randomness… It is this element of randomness that drives the measure of uncertainty or unpredictability in the data.

Well, we know what uncertainty is, but how do we quantify it?

One of the most commonly used measures of dispersion in the data is Standard Deviation, which is a statistical measure of dispersion of the various values of a dataset from its mean or Central Tendency, assuming that the data is normally distributed. Since this measure considers the squares of deviations, the deviations below and above the mean do not cancel each other out and gives more weightage to the larger deviations compared to the smaller deviations. The higher the Standard Deviation for a dataset, the more dispersed are the individual data points and thus more challenging the task of generating forecasts with higher accuracy.

Now that we have a way to quantify uncertainty, how do we consider this uncertainty?

I would again like to refer to the approach the book ‘Dance with Chance’ mentioned earlier in the blog, wherein the authors propose the following 3-A framework to deal with Uncertainty:

  1. Accept that you are operating in an uncertain world and thus identify the range of possibilities
  2. Assess the level of uncertainty using the available data and any additional inputs
  3. Augment the range of uncertainty estimated in the earlier step
    Applying this in the context of Demand Planning, we must not only generate statistical forecasts, but also assess the uncertainty involved in the demand patterns and further augment this uncertainty to create a range of forecast.
    One of the ways in which this can be achieved - The system generated statistical forecasts can then be further enhanced with any information of the external events that the Demand Planners are aware of along with the probabilities of these materializing. Further to this, we can apply a factor [which can be derived empirically or based on guidance from management] on the baseline statistical forecast to factor for the uncertainty involved.
    While this concept is very simple to understand and logical to follow, this approach is not followed commonly…

So to address the main question, my argument is as follows -
No… Higher Forecast Accuracy alone cannot be a Silver Bullet. The blog from my colleague covers in great detail various measures of Forecast Accuracy and suggests their applicability in different scenarios. These need to be further supplemented by considering many other aspects to address the challenges mentioned earlier.
One of these and also the central theme of this blog, is the acceptance and understanding of Uncertainty involved and dealing with the same by employing some of the levers mentioned earlier.

I would like to end by sharing the following quote from Donald Rumsfield - “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we now know we don’t know. But there are also unknown unknowns. These are things we do not know we don’t know.”
It is by accounting and planning for the later two, which can help us be better prepared to deal with uncertainty and thus help make our Supply Chains more flexible and responsive…

In this blog, we have briefly looked at some ideas for accepting and dealing with Uncertainty… Further to this, identifying and understanding the factors that contribute to this uncertainty has the potential to provide more leverage, as we could work towards factors that are within our control to limit this uncertainty itself, which would be the topic of one of my subsequent blogs.

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