Winning Manufacturing Strategies

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January 30, 2012

Crisis Management in the Times of Global Manufacturing Supply Chains

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

 

Crisis management has been the biggest bane for the manufacturers in the recent past. Manufacturing industry suffered from one crisis after another last year. First it was Japan earthquake and then Thailand floods.

The Japan Crisis caused major supply constraints for global automotive industry resulting in poor capacity utilization of plants, long waiting times, cancellation of orders etc. Similarly during the Thailand crisis many manufacturers were forced to shut down production or cut back the output due to disruption in supply of critical parts.

 

All this can lead to long term issues if timely action is not taken. This is why there is a need of better crisis management systems as it is otherwise difficult:

a)      To gauge the impact of crisis

b)      To come up with appropriate Strategy to counter the situation.

 

Gone are the days when all the production took place at one location. Today Gadgets, Automobiles etc. contain thousands of constituents procured from all over the world. Semiconductors may come from Korea, Metals from Asia/Africa, Electronic components from China/Japan, the list is endless.

Following figure shows what all Information is required by a manufacturer in a Crisis situation:

 

Crisis management.gifBusiness value: This is where we come into the picture. We can help our partners to come up with better systems and processes to gather all this information in a form which is comprehensible to Top management and aids them in making quick decisions about corrective actions to be taken.

With this invaluable information a Manufacturer can:

ü  Be better prepared for crisis situations

ü  Assess the impact of the crisis effectively

ü  Adopt a strategy to mitigate the impact

ü  Arrange for the same components from a different location which is not affected by the crisis thereby keeping the production smooth.

ü  Inform all the stakeholders beforehand about the expected delays. Quick & timely Crisis communication is quite necessary for long term partnerships.

Hope we all learn lessons from the recent events which have shown us that it can take months to assess the full impact to a global manufacturing supply chain. When a manufacturer gauges too late, they have already lost critical time giving rises to a significant revenue & reputation risk.

January 27, 2012

Electric, Hybrid or Gasoline: Illusion, Fusion or Confusion

Guest Post by Jagmeet Singh, Principal, Manufacturing Management Consulting Services, Infosys Limited

Ok, so here is what the big deal is about. What type of vehicle has the lowest carbon footprint and is most eco-friendly. At first instance, it would look like electric vehicle is the best without a doubt. No tailpipe emissions and hence no harm to the environment. Well, this is not the complete picture. Rather this starts another interesting debate.

 

What one would miss in taking that call is the source of electric energy that electric vehicle would consume for charging and unfortunately in today's world, the source is still primarily COAL and hence huge amount of carbon emissions right at the source!

By the time one deliberates the above fact, comes another verdict published by Ricardo, which claims "electric and hybrid cars have a higher carbon footprint during production than conventional vehicles, but still offer a lower footprint over the full life cycle". So shall we ignore source and production for the sake of long run and at the same time break the myth/illusion about EVs? This definitely confuses a person who would like to do his or her bit towards a better future.

So, I thought of taking help of mathematical models. And for calculating carbon footprint and environmental impact, the best approach seems to be Life Cycle Assessment (LCA).  I looked into research paper, blogs, and many more articles on the subject. I read some very interesting insights ranging from battery type to vehicle weight and fuel type. And the more I read the more I got interested in the subject resulting finally into a confused verdict.  Yes, that is right.

Even LCA methodology has some limitations today because of unavailability of an algorithm standard. Not only that, every input factor like country, state and local region power supply generated different results. Some purely focused on the source of power and declared verdict. Others focused on emissions in vehicle use phase and yet others talked about EOL (End Of Lifecycle) to finally make an argument.

Now, if I were to buy vehicle today it would purely be my decision as to what I consider the most important factor to environmental pollution. Be it source of energy, consumer phase, EOL or something else.

What would you consider?

January 25, 2012

How does Search technology boost Decision Intelligence?

Guest Post by

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

 

Decision Intelligence leveraging search engine technologies and text analytics is the revolutionary and new approach of decision intelligence by exploiting the enterprise information assets and the unstructured web content. It started with the vision of addressing Business Intelligence platform limitations, but then it started redefining the field of decision intelligence by merging Business Intelligence (BI), Competitive Intelligence (CI), and innovative and new technologies derived from the search engine market. We all know that, BI encompasses a range of IT tools, usually accessible through a common BI portal that aids decision-making for enterprise analysts and managers, including financial performance reporting, monitoring of current operations, performance benchmarking, marketing analytics and sales trends identification etc. CI enables the enterprise to understand their competitors' strategies, and those of other important market players, and is particularly intended to identify trends and new and potential opportunities for growth, mergers and diversification.

 

BI platforms have evolved from a simple operational reporting to multi-dimensional analytical capabilities platform, but still face significant financial and technical challenges. BI platforms only process the structured enterprise information assets and do not process the large volumes of unstructured enterprise assets like emails, presentations, office documents, web pages, chat transcripts, logs etc. which have a very large potential in critical decision making for the business. The platforms have multiple interfaces for accessing various functions, needs to integrate with multiple data sources and hence make it overly complex for the business users, limiting the adoption and use. The efforts to scale and integrate the BI platforms with multiple data sources within the enterprise is highly complex, very costly, time consuming and difficult to manage within an enterprise. Also, as the ETL tools doing the input to data warehouses and data marts operate in batch mode, the decision making is not effective as it does not happen on the fresh data. Like BI, CI systems also have some inherent limitations arising from CI's dependence on huge volumes of unstructured data as an essential data source to derive competition information and trends. The web in particular has been a challenging CI data source, because of the strong dilution of the information relevance and very weak signal to noise ratio. Hence because of this automated CI systems remain fairly limited in scope and till now focus has been on customized manual research.
 

Technologies derived from the enterprise search market and text analytics are proving to be an ideal complements to the BI and CI technologies, as they are capable of eliminating the above mentioned technical and financial challenges and opening the door to their integration within a unified decision intelligence platform. The enterprise search engines in the market today can handle large volumes of structured as well as unstructured data and they also give a meaningful structure to the unstructured data indexed by the engines. The semantic technologies at the core of search engines are specifically designed to analyze and process textual data (unstructured information assets) and provide a meaning to the unstructured data and create relationships within the structured and the unstructured data indexed within the platform. The search platforms are scalable to handle hundreds of millions of data due to the search engine Index and Distributed architecture, which, even the strongest of RDBMS cannot handle, and that too it does with a very low TCO. These search platforms also support SOA architecture which enables rapid deployment and easy integration within the complete enterprise information ecosystem like enterprise databases, BI back ends, file shares, email systems, web content like websites, microsites, blogs, forums, communities etc. and enterprise systems like ERP,CRM, CMS and legacy systems etc. Along with the advanced technology, search platforms also provide simple and user friendly interfaces with natural language querying, intelligent querying and browsing options, collaboration and rich graphical tools capable of doing multi-faceted navigation. 
 

Unstructured data sources are heterogeneous and widely dispersed inside and outside the enterprise firewall and hence the challenge is to locate the exact and meaningful resources, and to extract, classify, and exploit the useful information they contain from the point of view of decision systems, e.g., effectively detecting named entities, identifying patterns, enriching thesauri, performing semantic analysis, dynamically creating content summaries etc. The unstructured analytics in the search engines work by autonomically transforming non-structured textual data into structured information using semantic analysis techniques drawn from artificial intelligence, including:

 

  • Automatic language detection
  • Lemmatization, or the intelligent recognition of the form and variations of words from a language, i.e., feminine or masculine, plural, conjugation state, adjectival usage etc.
  • Advanced phonetic recognition to manage typographical errors, inversion of letters or letter groups, homonyms, synonyms etc.
  • Personalized semantic filtering, adapted to the ontological context of each enterprise
  • Semantic analysis, detection of lexical forms (patterns), recognition of named entities (people, places, times, dates etc.), integration of business thesauri (ontologies), and creation of specific business context semantic rules 

The traditional decision intelligence systems can answer the question like "Which product was the top seller for the last quarter" but would not be able to answer prognostic questions such as "Which product is likely to attract the highest number of consumers in New York two months from now". The second question will need analysis of data within the enterprise boundaries and also a deep analysis of data outside the enterprise boundaries to detect the latest trends and sentiments of people to yours and competitors' products in New York and also recent trends which can influence the buying process. The search technology exploits new data collection, processing, indexing and access technologies that remove the existing technical and financial barriers associated with leveraging unstructured data in traditional BI systems. This will enable the enrichment of BI platforms with emotive analyses and product or brand popularity using the information available in an unstructured format in blogs, forums, press sites, dedicated web sites, testimonials, buyer comments, communities etc. 
 

Let's consider, another example, the case of a business analyst, studying factors that may explain a marked decrease in maintenance contract renewals for a specific product.

With the decision intelligence platform leveraging search technology, the analyst can:

  • First of all, locate the period in which there is a sharp decline
  • Search customers that have chosen not to renew the contracts
  • Look at e-mails containing requests or complaints sent to the company by these customers, and, if desired, view text transcripts of telephone conversations between these same customers and the company's customer service representatives
  •  Search for the names of competitors possibly contained in these documents
  • Seek out data on the company website blogs and forums, where customers have talked positive or negative about that product
  • Seek out data on the Web containing special offers or promotions offered by these same competitors
  • Seek out data on the Web containing blogs, forums, communities where people are talking about that product and the competitor's similar product. The platform is capable of doing the sentiment analysis of yours and competitors' products and this emotive analysis will definitely help the business to understand the factors of declining sales
  • Create an overall report for the top management, sales and marketing, maintenance departments with the data collected from the platform in terms of graphical visualization and data exports.

 

Some specific examples of this platform in Manufacturing domain can be, to detect the maintenance and manufacturing problems by processing unstructured data such as repair logs and service notes. Manufacturing organizations can also leverage this platform for global data aggregation, dashboard presentation and analysis for the Production Control Organization, analyzing data pertaining to Inventory Management, analyzing data pertaining to the relationship between various entities like Suppliers, customers, automotive parts etc. thereby enabling more effective crisis management. Organizations can also use the platform to analyze and study the market sentiments for their products and services by aggregating the web and the social media data and can have a single unified dashboard which provides them with a 360 view of their data within the enterprise and data related to their and competitor products on the social web.
 

This market of decision intelligence leveraging enterprise search and text analytics is massive and many traditional BI vendors have still not fully switched on to the value of this market and hence we see many fairly unknown vendors (at least unknown to the traditional BI professionals) pushing into the BI market and claiming market share. Vendors such as Endeca, Fast, Exalead, InXight, Attivio, and ClearForest etc. are all doing well in this space of decision Intelligence using Enterprise Search and Text Analytics. It is no surprise that BI vendors are starting to introduce partnerships with these relatively new kids on the BI space and we could see more acquisitions possibly occurring in the next 12-18 months. We already know that FAST got acquired by Microsoft around 2 years back and lately we have seen Endeca being acquired by Oracle as a major game changer in this space. Watch out how this market grows in leaps and bounds in the coming months & years and how major players like Oracle, IBM and Microsoft etc. strategize to acquire the huge potential of unstructured data.