Disruptions in Supply Chain Management – Learning it the Hard Way

In the light of ever diminishing profit margins, over-whelming demand for shortest possible time delivery and minimum inventory holding, presence of a responsive and resilient supply chain is a necessity. External factors like natural disasters, fire accident at supplier end and government restrictions that are not under control of the company also pose a significant risk to smooth functioning of the supply chain. To address these issues let us have a look at some of the companies that had to deal the issues that arise due to supply chain disruption.

In March 2011, Japan was hit by earthquake and tsunami which caused huge loss of lives and also affected the production facilities of major automotive companies namely Toyota, Nissan and Honda. The effect was of such magnitude that it took Toyota over six months to restore production to pre-calamity levels, resulted in a production loss of 140,000 cars and 30% decrease in company profits.

Not all disruptions are man-made. Closer home, labour strike at Maruti Suzuki India’s Manesar plant in June 2012 led to the shutdown of the plant for a month. The resulting effect was a daily loss of INR 900 million and significant drop in sales and market value. This led to long waiting period and cancellation of bookings of its top selling cars. 

Supply chain disruption is an unplanned event that adversely affects a firm’s normal operations. It can be a sudden increase or decrease, in demand or supply that leads to imbalance between the two. 

A convenient solution for such an unplanned situation can be, building Inventory. But holding excess inventory can be a costly affair. Reasons behind it – the inventory carrying costs are incurred continually and also the inventory is meant to be used only in a rare events of disruption. So, the company may end up paying for resources that remain untapped forever! There are also concerns for obsolescence of the products. At the same time, holding excess inventory is reasonable in case of commodity products that are associated with low holding cost and low risk of obsolescence. For example, large amount emergency fuel reserves are maintained by United States as Strategic Petroleum Reserves (SPR). It is one of the largest emergency supplies in the world.

For products showcasing high inventory carrying cost and risk of obsolescence, having limited but multiple suppliers is a better approach. Controlled decentralization, hedges the risk possessed by a centralized or pooled (single) supplier in case of untoward situations. As in case of technology companies like Samsung Electronics  which always aim to have at least two suppliers, even if the second one provides only a fraction of the total volume.This approach helps the company to lower risk of disruption in the supply chain.

While disruptive environments are hard to avoid, whether a company mitigates or fails in that situation depends lot on the company. A classic example for the case is a major fire that broke out in a plant in Philips electronics in March 2000. It was a major supplier of semiconductors to Nokia and Ericsson and the fire destroyed chips meant for millions of phones. Nokia was quick to respond to this by sourcing chips from other plants of Philips and also procured from other suppliers. This multi-supplier strategy along with timeliness, led to little effect of the situation on Nokia’s demand supply capabilities. The other major customer of Philips, Ericsson was confined to single supplier strategy. The single source of chips led to disruption in mobile phone production by Ericsson. As a result, Ericsson ended up losing significant market share to Nokia and a drop in net profits.

The contrasting outcomes to a single event showcases that a responsive and resilient supply chain can alter the fortunes of a company. Breakdowns at a point in the supply chain can have consequences, which are not limited to that region rather, on a global scale.

Thus as a supply chain manager, one should take an informed decision in designing the Supply Chain, by taking into consideration all the factors and risks involved; past industry practices and experiences; and whether it is sustainable for the company and the specific product.

References:

1. WIPRO Consulting Services, “Supply Chain Vulnerability in Times of Disaster”   http://www.wipro.com/documents/resource/ Supply_Chain_Vulnerability_in_Times_of_Disaster.pdf

2. Sanjeev Prashar; Harvinder Singh; AnshuKatiyar, “MarutiSuzuki India Limited: Marketing,” Ivey Publishing, Feb 6, 2013

3. Sunil Chopra and ManMohan S. Sodhi, “Managing Risk to Avoid Supply-Chain Breakdown.” 

http://sloanreview.mit.edu/article/managing-risk-to-avoid-supplychain-breakdown/

4. William Schmidt Ananth Raman, “When Supply-Chain Disruptions Matter.” http://www.hbs.edu/faculty/Publication%20Files/13-006_cff75cd2-952d-493d-89e7-d7043385eb64.pdf

5. https://en.wikipedia.org/wiki/Strategic_Petroleum_Reserve_(United_States)

6. M. Sodhi and S. Lee, “An Analysis of Sources of Risk in the Consumer Electronics Industry,” Journal of the Operational Research Society 58, no. 11 (November 2007): 1430-1439.

The article is written by Aditya Pratap. He is PGP first year student at Indian Institute of Management Raipur. 

Supply Chain Management in India : Challenges and Opportunities

Greater and more intense competition between global value chains are leading to a substantial shift in the expectations from supply chains. India's complex operational challenges and increasing expectations make the job of a supply chain professional extremely difficult. In this article, we shall try to analyze the Indian scenario and provide some suggestions to chart the best way possible to create robust supply chains. Finally, we would conclude with a concise look at the initiatives being taken by the government, the industry and allied sectors to augment the supply chain infrastructure in India. 

Fig 1: The competition today is between value chains

The competition today is primarily between value chains. This makes it imperative to collaborate within the organization across following three levels,

•          Functional areas
•          Value chain 
•          Beyond the value chain

     With organizations being more diverse than ever, it is important to follow a tailored approach rather than a one size fits all approach. This differentiated approach will enable organizations to take care of different market and product needs. 

     Supply chain managers must be ambidextrous, able to see the bigger picture while also focusing on the details. In this regard, the single demand forecast for the entire organization can be the chief enabler with total cost optimization for capacity planning and scenario analysis for risk assessment. To take care of all stakeholders in the value chain from customers to vendors, supply chain professionals proactively need to apply pull replenishment strategies. This will invariably involve a solid information infrastructure, regular inventory calibration and removal of artificial demand distortions.

     The supply chains are becoming increasingly complex due to

•                       Broader product portfolios
•                      Shorter product life cycles 
•                      Increasing customer expectations 

      So it is important to actively manage supply chains. Going forward, it will be important to prune the non value added activities and capitalize the value added activities.

  

   We further look at the real-time problems faced by supply chain professionals and the unique characteristics in the Indian context. In India, the biggest bottleneck is the lack of proper logistics’ infrastructure. A lot more can be accomplished if there is better infrastructure and the ability to scale up to get products to far flung areas, especially small towns and villages. There is a huge opportunity once infrastructure bottlenecks are removed. The country has the potential to emerge as a supply chain centre of excellence for the world. The corporations need to build larger distribution centers in the interiors to consolidate access. The use of larger, efficient and appropriate vehicles is equally important. Another issue is the cost of movement in India due to long routes.

I    We compare the Indian scenario with the United States of America, the highway network in the US enables inter-state commerce system. It allows long-distance travel efficiently. In the US, for instance, a barge system for non time sensitive goods like copper or steel are moved in large quantities at low cost.  Similarly, India has a huge road structure but the same is not integrated to provide a strategic advantage. But if we can eliminate barriers and the congestion, and create a road or rail system allowing long-distance travel, then it helps in getting foreign investments in manufacturing. Retailers and Supply Chain Management companies would develop infrastructure and distribution centers. This will lead to consolidation and scaling up of distribution at lower costs.

    

     The cold supply chain in India is almost non-existent.The wastage of perishables that happens because there is no proper infrastructure for temperature control and refrigeration of goods is substantial. We have an to look at water, rail and road systems and connect them with ports. 

Fig 2: The cold chain logistics  

Now, we shall provide an overview of the initiatives presently underway to improve the supply chain system in India.

The Dedicated Freight Corridor Corporation of India Limited (DFCCIL) was registered as a company under the Companies Act 1956 in 2006. This company under Ministry of Railways was conceived and formed to undertake planning & development, mobilisation of financial resources and construction, maintenance and operation of the Dedicated Freight Corridors. The construction of the Western Dedicated Freight Corridor from Dadri to Nava Sheva (total length 1483km) and the Eastern Dedicated Freight Corridor from Ludhiana to Dankuni (total length 1839km) is already underway. The construction of East-West Dedicated Freight Corridor , the North-South Dedicated Freight Corridor, the East Coast Dedicated Freight Corridor and the South-West Dedicated Freight Corridor is in the planning stage.

The mission of this major infrastructure push spearheaded by the government is to build a corridor with appropriate technology that enables Indian railways to regain its market share of freight transport by creating additional capacity, to set up Multi-modal logistic parks along the DFC to provide complete transport solution to customers and to support the government's initiatives toward ecological sustainability by encouraging users to adopt railways as the most environment friendly mode for their transport requirements. This would occur in tandem with the industrial corridors to be setup in India. 

An industrial corridor is a package of infrastructure spending allocated to a specific geographical area, with the intent to stimulate industrial development. An industrial corridor aims to crease an area with a cluster of manufacturing or other industry. Naturally, such corridors are often created in areas that have pre-existing infrastructure, such as ports, highways and railroads. The vision is to have a holistic network of high quality infrastructure, connectivity via all modes of transport accompanied by industrial clusters. These modalities are arranged such that an "arterial" modality, such as a highway or railroad, receives "feeder" roads or railways. Concerns when creating corridors including correctly assessing demand and viability, transport options for goods and workers, land values, and economic incentives for companies. Examples include the Delhi Mumbai Industrial Corridor Project and Chennai Bangalore Industrial Corridor.

Fig 3: The proposed Dedicated Freight Corridors 

All the major steps taken by government to improve infrastructure will ensure smooth transportation of goods with less bottlenecks. That will allow India to be a global player in field of supply chain management. It will also help in getting investment in manufacturing. The policy reforms are being overhauled through introduction of initiatives like Make in India, Skill India and digital India. 

About the Author:

The article is written by Gulshan Prakash. He is  PGP first year student at Indian Institute of Management Raipur.
 

OPERTUNE - 2015 :

OPEP_Operation and supply chain club , IIM Raipur congratulates the winners.

Thanks all for such an overwhelming participation

Big Data Analytics

Big data is collection of large and complex data that cannot be processed by using normal data processing systems. Analytics refers to getting meaningful patterns from an unstructured or semi-structured data. Now Big Data Analytics is processing large data (Unstructured, semi-structured and structured) to discover unknown correlations, hidden data patterns, consumer preferences, market trends and other useful business information.

The results of analysis can be used for improving operational efficiency, better targeting of potential customer, new avenues for revenue generation, gaining competitive edge over rival firms and better customer service. The main aim of big data analytics is to help various firms to take more informed business decisions by enabling analytics professionals and data scientists  to analyze and interpret huge volumes of data  which is untapped by traditional business intelligence programs.

                

Big data encompasses internet clicks information, server logs, Content in Social media,various Social network reports, ,mobile phone call records, customer survey responses and market research data etc. Usually only unstructured and semi-structured data is associated with big data, but consulting companies like Forrester and Gartner consider structured data also as a valid component of big data.

Technologies in Big Data Analytics

Some software tools that are used to analyze big data are based on predictive analytics .There are new class of technologies such as Hadoop and related tools such as MapReduce, YARN, Spark, Pig and NoSQL databases which can process Big data. These technologies form crucial part of an open source software that supports the processing of large and diverse data.

Hadoop

Hadoop is a open source Java-based programming framework that supports the processing of huge data sets in a distributed computing environment. It is part of Apache Software Foundation. It was inspired by Google's MapReduce, a software framework in which any application is divided into numerous small parts. These parts (also called fragments or blocks) can be run on any node (a connection point in a network) in the cluster.

Illustration of Big Data Analytics

         

                                       Big Data Analytics solution by LogicMatter

        Let us now illustrate how big data analytics solution is implemented using Hadoop. LogicMatter is a low cost Big Data Analytics solution provider.  Here traditional (e.g. ODS, EDW) and emerging (Hadoop MapReduce) analytical tools are combined to operate on big data. The data platform is built on the powerful and flexible Amazon Web Services Cloud platform. To capture, process, store and transform data, Hadoop is used with the LogicMatter-designed Analytical Data Store (ADS). File-based storage service of Hadoop is used by platform for flexible and fast data processing .This big data analytics platform enables the continuous delivery of both real-time and historical analytics via the popular Tableau.

The Analytics platform and solutions is built specifically to solve some complex customer problems such as clickstream analytics, video analytics, sales performance analysis, fraud detection and financial analytics.

Data Sources

This platform enables to collect, process, store, and transform both unstructured and structured data exclusively for analytical purposes. It can quickly process various varieties of unstructured data including  text, documents, weblogs, XML files, Excel, audio & video, call logs), clickstream, and event data. It can also simultaneously process structured data from familiar enterprise data sources such as CRM, ERP and SQL Databases.

The data collection process is separated from transformation and analysis. It allows us to easily add data sources of unknown and known kind without impacting the analysis, a huge challenge with present analytics solutions. Transformation of data is delayed till you need to do the analysis wastage and reducing upfront costs.

Data Platform

The AWS data platform consists of two primary components:

 -Hadoop Cluster.

 -LogicMatter-designed ADS (Analytical Data Service).

The flexible and scalable Hadoop technology is used to collect both structured and unstructured data. The data collected is integrated, pre-processed and stored in ADS. The flat file-based storage system of Hadoop allows you to scale quickly as well as handle large amounts of known and unknown data. Hadoop is an integrated and intermediate data source and acts as a feeder to the ADS.The data from Hadoop is mapped and transformed to develop a data model. The model built iteratively and stored in the ADS forms the basis for a powerful analytics. ADS uses traditional data warehouse technology – Cubes, and OLAP. Hence, it supports all the powerful and traditional analytical techniques that you are used to (dashboards ,reports, scorecards etc.).     

Visualization

One of the unique design features of LogicMatter’s Big Data Analytics services is to enable continuous analytics both real-time and historical .As there is an integrated data discovery platform ,the visualization tool is directly connected to either Hadoop or ADS for development the analytics. Ad-hoc queries can be run against Hadoop for exploratory analytics and instant  data access . For the standard, canned reports and dashboards, you connect to the ADS to gain a historical perspective.

The data platform is so flexible that you can easily connect any of your favorite visualization tools (such as Qlikview ,Excel,).

Testimonials Of Big Data Analytics

There are several examples of how bigger, better, faster, stronger applications, analytics, sensors, and networks are creating results with big data today across various industries.

1. The Financial Services Industry

The financial services industry uses big data to make better financial decisions. Banking gaint Morgan Stanley ran into issues doing portfolio analysis on some traditional databases and now uses Hadoop to analyze investments on a larger scale and with better results. Hadoop is also used in the industry for sentiment analysis, financial trades and predictive analytics.

2. The Automotive Industry 

Ford’s modern hybrid Fusion model generates up to 25 GB of data per hour. Data obtained can be used to understand driving behaviors, reduce accidents, understand wear and tear to identify issues that lower maintenance costs, avoid accidents, and even confirm travelling arrangements.

3. Supply Chain And Logistics

Companies like Union Pacific Railroad use thermometers and ultrasound to capture data about their engines and send it for analysis to identify equipment at risk for failure if any. The world’s largest  multi-carrier network for the ocean shipping industry-INTTRA uses it’s OceanMetricsapplication to allow shippers and carriers to measure their own performance. Companies are also using telematics and big data to streamline trucking fleets . GE believes these types new capabilities can contribute $15 trillion to the global GDP by 2030 by using systematic and data-driven analysis.

4. Retail

Walmart is using big data from 10 different websites to feed shoppers transaction data into analytical devices. Sears and Kmart are trying to improve the personalization of marketing campaigns and offers with big data to compete better with Wal-Mart and Target.

Practical Big Data Benefits

Develop Target Markets

By analyzing the various customers purchasing orders, companies can now know better about customers who are buying their products. Companies can accordingly target on those customers.

Customize your website in real time

Thorough big data analytics companies can personalize their websites and portals based on gender, location and nationality of customers and offer them tailored recommendations .The best example for this is Amazon’s use  item-based, collaborative filtering (IBCF).Amazon uses

Features such as “Customers who bought this item also bought” and “frequently bought together” to reach more customers. Amazon could generate more revenue through these methods.

Create new revenue streams

The insights that a company obtain from analyzing market and consumers with Big Data are not just valuable to that company. Firms could sell them as non-personalized trend data to large industry players operating in the same segment and create a whole new revenue stream.

There are many companies like Bloomberg and Analytics Quotient which sell the analyzed information to other companies and generate revenues.

Reducing maintenance costs

Factories estimate that a certain type of equipment is likely to wear out after some years. So, they replace every piece of that technology within that many years. Big Data tools do away with such unpractical and costly practices. Massive amounts of data that they access and use and their unequalled speed can spot failing devices and predict the depreciation time. This results in a much more cost-effective replacement strategy for the utility as faulty devices are tracked a lot faster now.

Offering enterprise-wide insights

Previously when business users needed to analyze large amounts of varied data, they had to ask their IT colleagues for help as they themselves lacked the technical expertise. But by the time they received the requested information, it was no longer useful or even correct. Now With Big Data tools, the technical teams can do the groundwork and then build  algorithms for faster searches. They can develop systems and install interactive and dynamic visualization tools that allow business users to analyze, view and benefit from the crucial data.

Making Smart Cities

To deal with the consequences of their fast expansion, more  number of smart cities are indeed leveraging Big Data tools for the benefit of their citizens. Oslo in Norway, for instance, reduced street lighting energy consumption by 62% with a smart solution. The Memphis Police Department started using predictive software in 2006 and has been able to reduce serious crime by 30 %. Portland city in Oregon, used technology to optimize the timing of its traffic signals and was able to eliminate more CO2 emissions in just six years.

References:

http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics

http://dermatological/big-data-analytics-services-solutions/

http://blog.pivotal.io/pivotal/news-2/20-examples-of-getting-results-with-big-data

http://en.wikipedia.org/wiki/Apache_Hadoop

 The article is written by B.kiran kumar.He is currently a PGP first year student of IIM Raipur.He has 3.9 years of experience at virtusa.

Inventory Management at Amazon

A Brief about E-Commerce Industry:Types of Business Models:

• Buy and Sell: Products are purchased by the company and stacked in warehouses. These products are displayed on the website.
• MarketPlace: Products from multiple vendors are displayed in the website. Company take care of marketing and transactions and shipping, doesn’t hold the inventory.

Industry Analysis and Market Details:

According to a survey by ASSOCHAM E-Commerce is expected to reach USD 56 Billion by 2023.

• More than half of the total 1.2 billion population of India falls under the ‘below 25 years of age’ bracket. 
• 65% of India’s population, representing the working age group of 15 to 64 years, would aid the further growth of e-commerce, driven by their rising disposable income. Notably, discretionary spending in India is expected to jump to 70% by 2025 from 52% in 2005. 

                                               SWOT Analysis Of E-Commerce Industry

Amazon:Introduction

• Sells products such as books, DVDs, Electronics online in more than 60 Countries.
• Uses Amazon-to-Buyer Sale Approach 
• Multi level E-Commerce Company.
• Operates 7 websites that support their business operation globally and offers 20 million items for sale.

     Amazon uses Multi-Tier Inventory model where information flow takes place from tier-1(Amazon Distribution centre) to tier-3(vendors, manufacturers etc) and physical flow takes place from Tier-3 to Tier-1.

How Amazon deals the supply chain with fluctuating demand? 

Amazon.com carries high-demand title in inventory, whereas it purchases low-demand titles from distributor in response to a customer orders.Managing inventory is one of the most important tasks of a retailing company. If there are not enough goods in stock some of the customers might be disappointed. Stocking too many will reduce the profit margins.

 So Amazon Inventory management aimed at following strategies:

• Maintain inventory of millions of items.
• Shipment within one week.
• Have a clear understanding of customer’s delivery needs.
• Coordinate with wholesale suppliers and independent producers to make available to customers both current and the soon to be released books.
• Provide two day delivery on most orders.
• Allow customers to query the status of their purchases and track their own shipments.

Amazon Inventory management- Technology usage

• The Central Amazon Data warehouse is made up of 28 Hewelett Packard servers, with four CPUs per node, running Oracle 9i database software.
• The architecture handles millions of back-end operations and third party seller queries.

Amazon has Strategic Alliances with many vendors for procuring various products.

• Ashford.com( Online retailing of luxury and premium products )
• Drugstore.com (Online retail and information source for health, beauty, wellness, personal care and pharmacy).

Implementation of Inventory Management:

• Amazon aimed at ‘hassle-free operations’, customer satisfaction, time and cost efficiency.
• Amazon managed to reduce the size of its inventories because of efficiently managing the warehouse.
• Careful decision about ‘product’, ‘supplier’ & ‘distribution centre’ i.e. ‘which product to buy’, ‘from where’ & ‘which centre it would send its product to’.
• Huge investment in infrastructure (revamped the layout of its warehouse) and technology (refining its software helped in demand forecasting)
• Aimed at cutting down expenses via outsourcing some of the routine activities.
• Partnered with other companies for shipping the inventory.

When it managed its own inventory, Amazon earned the reputation of providing superior customer service. Despite this it decided to outsource inventory management and adopted following stratagies:

• Amazon decided to outsource its inventory management with a reason to earn more profits.
• Keeping a stock of frequently purchased/ popular items.
• Acted as a trans-shipment centre between distributors to the customer.
• Main Distributors:      Ingram Micro – whole sale distributor, handled books & computer                                             Cell Star – handled cell phone sales
• In August 2001, Amazon entered into an agreement with Ingram Micro Inc (largest wholesale dealer of electronic goods & SCM services) to provide logistics & order fulfilment services for desktops, laptops etc at computer store at Amazon.com. The aim was to maximize operating efficiencies, streamline supply chain logistics and reduce inventory costs.
• In 2001, Amazon decided to include products of competing retailers and some used items on their website.
• Customer could now verify the prices of Amazon’s product vis-à-vis those of other retailers.
• Reduction in the cost of advertisement of there low pricing of products as customers can compare now.
• In 2003, Amazon handled the orders for Borders, Target, Circuit City, Toys “R” Us.
• Amazon only handled the net orders, the companies handled the inventory.
• Services proved to be immensely profitable for Amazon.

The Article is written by B.kiran.He is currently studying PGP 1st year at Indian Institue of Management.