Winning Manufacturing Strategies

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February 23, 2012

What happened to the Good Old Manufacturing? : Part 2

Guest Post by Varun Chhibber, Associate Consultant, MFG-ADT Online, Infosys

 

In my previous post I touched upon the recent trends in outsourcing of manufacturing and the main reasons cited by firms to outsource.

The road to outsourcing was not that easy, outsourcing faced a lot of opposition, as it was understandable also. Everything cannot be and should not be outsourced. For example it makes sense to outsource the production of an iPhone, but its designing, marketing etc. are still kept in-house. It is a decision that manufacturers have to take keeping in mind many factors.

Following figure shows the questions a Manufacturer need to ask before outsourcing any process:

questions to ask.png

Answering all these questions will help firms understand and gain a perspective about the ramifications of their actions. A firm has to ascertain what the risks, costs associated with a process are and also how dependent processes will be affected before handing over a process to some outside agency. Only after a thorough analysis of these answers a firm would be confident in deciding the fate of a process.

All said and done, in the end it's all about efficiencies when it comes to manufacturing. Manufacturers are moving their business wherever they can have higher efficiencies in the long run. Technology has helped manufacturers to this end a lot as it has enabled them to do what they could not think of earlier. Now they can control their operations spread far and wide across the globe and that too with great ease.

Business Value

Technology solutions like Supply chain management solutions, Inventory management solutions etc. have helped manufacturers make quick and profitable decisions in real time. It has led to a phase of technology fed manufacturing which has enabled manufacturers to manage their operations effectively.

This is where we come into picture; sophisticated technology solutions have helped numerous firms in streamlining their operations. All these technologies are enabling countless firms to manage their business in a more efficient way.

In our experience we have seen such technologies providing numerous Benefits to the manufacturers, some of them are:

·         Improved visibility across the supply chain which leads to a better control over the processes.

·         Timely action is aided which helps in avoiding delays and loss of reputation.

·         Strong partnerships are forged with the suppliers & distributers because of close collaboration.

·         Better planning.

·         Better inventory management which leads to greater liquidity.

Here's looking forward to a technology propelled futuristic manufacturing. For now, let's just say our goodbyes to the good old manufacturing!

Related post: http://www.infosysblogs.com/thought-floor/2012/02/what_happened_to_good_old_manu.html#more

February 20, 2012

A walk in the clouds (Part 1)

Guest Post by

Shailesh Shivakumar, Technology Architect, MFG-ADT Online, Infosys


Cloud computing is a buzz word for quite some time now. It was envisioned to provide paradigm shift to computing industry. On the same lines there is also a perception that cloud adoption is below the expectation. Is cloud really over hyped IT wave? Come; let's take a walk in the clouds to find out more.

 

A brief history of cloud computing..

Cloud computing is defined by NIST as "Cloud computing is a model for enabling convenient, on demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction".  Computation has evolved over generations starting from traditional IT to virtualization to standardization to automation to self-service cloud.  

 

 

Cloud concepts

 

 

CloudConcepts.jpg

Deployment models: private cloud consists of a cloud infrastructure exclusively for a single cloud consumer whereas a public cloud is provisioned for open use by general public. Hybrid cloud is a combination of two or more distinct cloud infrastructures.

  

Service Models:

The cloud pyramid depicts various service models offered:

ServiceModels.jpg


Essential Characteristics: Cloud infrastructure should support on-demand self-service with which the consumer can provision in the pre-defined process. Cloud should be ubiquitous network access which can be accessed from any device anywhere. The usage should be metered/measured to support only pay-per-use model and it should be elastic to support seamless scalability in real-time.

 

Cloud computing promises multi-dimensional benefits at various levels:

CloudBenefits.jpg


Trends, Prediction and adoption:

 

I am detailing few key survey statistics and its larger implications for the industry as a whole:

 

Cloud adoption and market is growing..

  •  Gartner predicts that cloud market would be 150B by 2013 while Merrill Lynch puts the number to 160B by 2011. IDC estimates that sales of public cloud will grow at 25% annual rate.
  •     Aberdeen group survey details that 48% of mid-sized enterprises have the highest cloud adoption rate with Small enterprises (38%) coming second and large enterprises (26%) coming last. However this trend is most likely to change: a survey conducted by Edge strategies in 2011 indicates that 74% of SMBs plan to use at least one cloud service in next 3 years. The same survey indicates that SME favor SaaS including business email, CRM, file share, collaboration, business apps and IaaS including data storage/backup.

 

Business drivers for cloud adoption..

  • Server virtualization: Out of 50M physical servers today, 60% of server workloads would be virtualized by 2013 
  • Low TCO:  IBM estimates that cloud adoption would reduce IT costs by 50% and improve capital utilization by 75%.
  • Business agility: Sandhill survey indicates that 50% of its respondents cited agility as the key driver to move to cloud platform; similarly Information week survey puts this number to 65%.  
  • Move towards SaaS model: IDC finds that SaaS revenue would grow 5 times faster than traditional packaged software in 2014; 34% of all new software purchases will be consumed via SaaS.  
  • Mobility and social computing: Mobile market is going to surpass the traditional desktop shortly and in addition to this social computing is adding lots of data each passing second. Cloud is the platform of choice for this scenario. 

February 17, 2012

Suggestive consumer experience with recommender technology..

Guest Post by

Ketan Chinchalkar, Senior Project Manager, MFG-ADT Online, Infosys

Consumers face a dizzying array of choices when navigating through the online content and products. Traditional navigation and search can leave consumers wanting - and leave money on the table as far as e-retailers and content owners are concerned and hence it is very important that a website provides a suggestive experience to the consumer based on his behavior on the site and also through implicit and explicit preferences. This will help the website achieve customer stickiness and loyalty, better content monetization and a potential sales conversion. A "Recommendation Engine" in layman's terms is defined as a platform which presents the right content, at the right time, in the right context, at the right value, over the right channel, to the right customer. Now this content can be an e-commerce item, a product, part or a service, offers, promotions, pdf and office documents, a marketing campaign, news, promotions, images, videos, audio, social comments, ratings, reviews, community content and blogs content etc. It will do wonders, if similar kind of suggestive experience is provided in a multi-channel environment like email, mobile, print, POS, stores, kiosks, customer service, social networking and that too by mining and leveraging cross channel user behavior.

 

A typical recommender system will take inputs in form of User (profile attributes, demographics, clickstream, transactions, and searches), Content (products, services, offers, promotions, metadata, and media) and the User Context. The core of recommendation engine is real time data and pattern mining algorithms, Artificial Intelligence, Neural networks and most importantly the collaborative filtering techniques. The data mining Engine creates and updates relationships between like-content based on the content taxonomy or in some engines it may be based on customer profiles as well. Data mining algorithms analyze customers' browsing and shopping history, answers to user-preference questionnaires and surveys, and profiles resulting from this information, and translate consumer activity into product and preference relationships that could indicate what consumers might be interested in. Collaborative filtering involves grouping of users having similar preferences and browsing history and then create multiple associations of item to item and item to user recommendations. For e.g. similar customers who viewed this item also viewed, Users who bought this item also bought. Concept wise, the output of a recommendation engine is classified into 3 types: personalized based on user profile and browsing behavior, social based on similar users profile and transactions and related based on the relation and similarity of the content.  Practically, no one approach will meet all the needs, devoid of shortcomings and hence the recommender systems are designed to mix the standard approach appropriately to enhance the relevance and the context quotient. Recommender systems also have a business rules or a filter engine for the business users to control the recommendation output and filter the recommendation output in multiple dimensions like user, items and transactions.

In the recent years, Social media has been enjoying a great deal of success, with millions of users visiting sites for social networking, blogging, micro-blogging, sharing etc. These social media sites rely principally on their users to create and contribute content; to annotate others' content with tags, ratings, and comments and to form online relationships. Facebook is playing a big role in today's social word of mouth and is indeed becoming a trusty recommendation engine. Facebook users are not only creating a more personal relationship with a brand, they're sharing that relationship with their friends and family. Many website catalogs/shopping carts can now be integrated with the social networking sites, like Facebook etc. and the user interactions there can be a valuable source of input to these recommender systems. The collaborative factor gets implicitly taken care by the social grouping in these sites and if the consumer likes, dislikes, tags, comments , opinions, bookmarks, blogs, posts, shares, polls etc. are being inputted to the recommender systems in an offline manner it can provide a real personalized and social perspective to the recommendation output.  What I mean by real is, the recommendations derived using the social media source are more of value to the user, as the similar users based on which the recommendations are provided are his close friends, colleagues, acquaintances and relatives. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.

In the overall technology landscape, a Recommendation engine can integrate with the Search Engine where the search engine acts as an aggregator of the User and Item content across various enterprise repositories. It can also interface with the Search Engine for the search keywords and the search term based recommendations. Recent trends have shown that cases where real time recommendations are not needed the user clickstream needed as one input to the recommendation engine can come from the Web Analytics feed. Business users also take the advantage of the Campaign Management/Lead management systems, Data warehouse and CRM systems to create targeted groups or control groups based on the segmentation done within these systems. Recommender output can also be integrated with email, SMS and print Campaigns which have an amount of personalization and cross sell/up sell.

The recommender technology is still not completely mature; organizations are not 100% sure if implementing recommendations would increase the sales revenue/conversions marginally, as the ROI can be difficult to measure. Lot of Recommendation Engine products in the market are new and emerging in this technology and will be mature as more implementations happen across the globe. There are also many open source recommendation algorithms available in the market which can be leveraged to custom built a full blown recommendation engine. Amazon, the most popular B2C and B2B e-talier has its own patented Recommendation platform and uses traditional collaborative filtering, cluster models and search based filters to personalize the online store for each customer. Most of the commercial Recommendations systems in the market are available as hosted service at the vendors end, and very few provide an option of co-locatable product at the customer side. As there are no clear cut market winners in this technology, which tool or platform to go with OR whether to build the engine from open source algorithms, will depend on the customer specific requirements/use cases, pricing model, TCO and time to market. Most vendors provide simple APIs so that their engines can easily integrate with websites, emails, stores, mobile devices etc. Some vendors offer remote business rules management, reporting, and A/B testing capabilities as part of a packaged recommendation service.

To say a recommender technology only is applicable to a retail domain will not be true, as now days many manufacturers/OEM have their own e-commerce/content/brand sites and they are also now becoming internet and social savvy. For example a recommender system in below use cases will also give a personalized experience to the consumer.

·         Content/Brand/service provider website for providing real time personalized targeting

·         e-commerce website for providing personalized, social and related item recommendations

·         Customer service, Call center solutions to give a personalized experience to the customer during a call, chat or an enquiry.

·         Personalized experience in Knowledge Management solutions

·         Campaigns (Web, Email, Mobile, Print)

·         In store devices, Kiosks

·         Improving Product configurators by means of a collaborative recommender system

·         Social commerce

·         Intranets/Extranets

·         Many more use cases where there is user-item association

 

In the near future, I see a recommender technology becoming a ubiquitous piece of multi-channel consumer experience. For example, the technology could leverage a location based component. "I might have a wireless communications device with GPS capability", and as I get near a store, I could get pinged with a message saying there's a sale there on a CD I might like." One burgeoning development is matching consumer tastes across different lines of businesses, such as using knowledge of customers' tastes in one area, like music, to sell them products in another area, like books. This will also depend on future business alliances and partnerships, along with advances in the technology.

 

February 6, 2012

What happened to the Good Old Manufacturing? - Part 1

Guest Post by Varun Chhibber, Associate Consultant, MFG-ADT Online, Infosys

 

Recently there has been a lot of hue and cry over the outsourcing and the job losses that followed. Major companies have been accused of exporting jobs to Asian countries such as China when there has been an ever increasing need of job creation in their native countries. To this companies argue that they are creating a lot of jobs indirectly by creating opportunities and if the jobs are not moved to save efficiencies, eventually it will lead to further job losses!

What used to happen?

In earlier times major companies used to employ lakhs of employees. All the manufacturing used to be at one place and this provided huge employment opportunities to workers which led to the rise of a new prosperous middle class. This has changed over the years and today the manufacturing supply chains are spread across the whole globe.

Related Post: http://www.infosysblogs.com/thought-floor/2012/01/crisis_management_in_the_times.html#more

 

Rationale for Outsourcing

outsourcing.gif

 

Business value

But a part of this trend has been changing with the advent of ecommerce, more and more companies are going the online way bypassing the traditional channels (retailers, wholesalers etc.). Directly approaching customers saves a lot of cost and due to this reason some manufacturers are shifting their focus from manufacturing overseas.

By manufacturing in say USA, what a manufacturer gains? :

·         Made in USA Tag has its premium and perceived quality is higher (valued by customers)

·         Reaction to the new trends could be swifter if manufacturing is done locally (quite applicable to apparel manufacturing).

·         Quality issues can be solved well in time avoiding heartburns later due delay in delivery to the customer, rejection of the consignment or going to the market with sub quality products.

This is where we come into picture and we can help our partners by providing them with online solutions in the domain of ecommerce which will help them go directly to their customers. This doesn't have to be the only way going forward (different channels can be employed simultaneously), but it could be one of the ways for future.

What do you think?