What in your opinion will be an appropriate price policy if the demand reaches its saturation point and the substitute products are likely to enter the market? Explain with the help of examples.
=======================================
WHEN WE SAY ''DEMAND REACHING SATURATION''IT MEANS
DEMAND = SUPPLY.IF YOU INCREASE THE SUPPLY, THE CUSTOMERS WILL SEEK PRICE REDUCTION.IF YOU INCREASE THE DEMAND, THE SUBSTITUTES WILL CREEP IN.
========================================================
UNDER THIS CIRCUMSTANCE, YOU SHOULD GO FORVALUE-BASED PRICING
Value pricers adhere to the thinking that the optimal selling price is a reflection of a product or service's perceived value by customers, not just the company's costs to produce or provide a product or service. The value of a product or service is derived from customer needs, preferences, expectations, and financial resources as well as from competitors' offerings. Consequently, this approach calls for managers to query customers and research the market to determine how much they value a product or service. In addition, managers must compare their products or services with those of their competitors to identify their value advantages and disadvantages.Yet, value-based pricing is not just creating customer satisfaction or making sales because customer satisfaction may be achieved through discounting alone, a pricing strategy that could also lead to greater sales. However, discounting may not necessarily lead to profitability. Value pricing involves setting prices to increase profitability by tapping into more of a product or service's value attributes. This approach to pricing also depends heavily on strong advertising, especially for new products or services, in order to communicate the value of products or services to customers and to motivate customers to pay more if necessary for the value provided by these products or services.
==================================================
A NUMBER OF BUSINESS ELEMENTS YOU MUST REVIEWAT THIS JUNCTION,
BEFORE YOU TOUCH THE PRICE.PRICE IS THE LAST ELEMENT TO TINKER WITH.
DEMAND FORECASTING
—Keep it simple and accurate. Predict demand at any combination of product and location nodes. Demand Forecasting is designed and synchronized to produce the optimal forecast input for Inventory Optimization, financial or assortment planning. MULTI-CHANNEL PLANNING—Integrate planning for your web, catalog and store channels to reduce redundancy and increase efficiency, while still respecting the unique characteristics of each channel. FINANCIAL PLANNING
—Plan top-down or bottom-up with an easy to use, but powerful solution that manages multiple versions of plans across categories, channels and time periods. ASSORTMENT PLANNING—Customize assortments by channel. See buyers' changes immediately so you can reach your financial goals with the right mix of products.
ITEM PLANNING
—Synchronize with assortment planning to support time-phased planning and tracking for multiple key performance indicators. Micro-manage just your key items or use it to plan all your seasonal items to determine the optimum receipt flows.
PROMOTION PLANNING
—Coordinate all aspects of your promotions from one solution. Create, forecast and track events and feed anticipated lift to replenishment to avoid stock-outs on featured products. CUSTOMER CLUSTERING
--Group stores by performance, customer profile, size or any other characteristics to stay in touch with your customers' needs without adding hundreds of store planners.
Setting up assortments. Integrates with Assortment Planning to select, plan and manage different promotional product assortments for various locations. Maintaining data. Supports promotion-specific attributes and measures, including page number, display fixture, offer and price zone. Building channel-specific promotions. Tracks data based on each channel's unique criteria. Gaining visibility for your products. Consolidates plans for analysis and purchasing across channels. Managing promotion information. Uses a central repository for events, products, vendors and locations. Monitoring item demand. Automatically updates forecasts based on actual performance data. Maintaining optimum inventory. Automatically feeds orders to PLENISHMENT.
ANOTHER IMPORTANT FACTOR
-- Inventory Optimization can help you get it right, every time:REPLENISHMENT —Profitable replenishment is a strategic advantage. Selling generates revenue, but smarter replenishment generates profit. Turn over inventory faster by ensuring you have what you need, where you need it, when you need it.
MULTI ECHELON
—Manage forecasting and replenishment across all your distribution channels from one application.
VENDOR MANAGED INVENTORY
—Share your inventory data with suppliers so they can time shipments and manage production—and you can please customers.
COLLABORATION GATEWAY
—Give valued partners access to inventory, replenishment events and other specific data involving goods they sell to you or buy from you. Lifecycle Management unites three powerful modules to put you in control of the entire order management process.DISTRIBUTED ORDER MANAGEMENT
—Keep your supply and customer demand in profitable balance. REVERSE LOGISTICS MANAGEMENT
—Make returns efficient, accurate and easy. Capture customer information, track return reasons, and automatically select the optimal mode of transportation to make returns a source of valuable customer and quality information. Automate vendor rules and streamline return-to-vendor credit process. Improve vendor buyback, reduce cycle time and improve open-to-buy. CUSTOMER GATEWAY
—Let customers and stores place, track and confirm their orders on line, giving you proof of receipt, opportunities for feedback—and happier customers
Transportation Lifecycle Management connects your transportation network from procurement to delivery
TRANSPORTATION PLANNING AND EXECUTION
—Optimally manage all your transportation activities so you can coordinate, redirect and stay on schedule. Deliver superior service to your customers with full visibility and event management capabilities.
LOGISTICS GATEWAY
—Share critical data in real time to keep all your transportation partners working together. Enable communication with suppliers and carriers to request shipments, provide updates and settle financial concerns.
-------------------------------------------------------------
WITH THE ABOVE LISTED TECHNIQUES,
-you can remove the wastages.
-you can improve efficiency
-you can save costetc etc.
Friday, April 18, 2008
MS-09 Question 4
Perfect competition
is an economic model that describes a hypothetical market form in which no producer or consumer has the market power to influence prices. According to the standard economical definition of efficiency (Pareto efficiency), perfect competition would lead to a completely efficient outcome. The analysis of perfectly competitive markets provides the foundation of the theory of supply and demand. Perfect competition is a market equilibrium in which all resources are allocated and used efficiently, and collective social welfare is maximized.
CONDITIONS FOR PERFECT COMPETITION
Often models of perfect competition assume that some subset of the following six conditions be fulfilled. In such a market, prices would normally move instantaneously to economic equilibrium. It should be noted however that these represent sufficient, not necessary conditions.
1.Atomicity
An atomic market is one in which there are a large number of small producers and consumers on a given market, each so small that its actions have no significant impact on others. Firms are price takers, meaning that the market sets the price that they must choose.
2.Homogeneity
Goods and services are perfect substitutes; that is, there is no product differentiation. (All firms sell an identical product)
3.Perfect and complete information
All firms and consumers know the prices set by all firms .
4.Equal access
All firms have access to production technologies, and resources are perfectly mobile.
5.Free entry
Any firm may enter or exit the market as it wishes .
6.Individual buyers and sellers act independently
The market is such that there is no scope for groups of buyers and/or sellers to come together with a view to changing the market price (collusion and cartels are not possible under this market structure)
Behavioral assumptions of perfect competition are that:
Consumers aim to maximize utility
Producers aim to maximize profits.
==============================================
CHARACTERISTICS OF A PERFECT COMPETITION
1. Many suppliers each with an insignificant share of the market – this means that each firm is too small relative to the overall market to affect price via a change in its own supply – each individual firm is assumed to be a price taker
2. An identical output produced by each firm – in other words, the market supplies homogeneous or standardised products that are perfect substitutes for each other. Consumers perceive the products to be identical
3. Consumers have perfect information about the prices all sellers in the market charge – so if some firms decide to charge a price higher than the ruling market price, there will be a large substitution effect away from this firm
4. All firms (industry participants and new entrants) are assumed to have equal access to resources (technology, other factor inputs) and improvements in production technologies achieved by one firm can spill-over to all the other suppliers in the market
5. There are assumed to be no barriers to entry & exit of firms in long run – which means that the market is open to competition from new suppliers – this affects the long run profits made by each firm in the industry. The long run equilibrium for a perfectly competitive market occurs when the marginal firm makes normal profit only in the long term
6. No externalities in production and consumption so that there is no divergence between private and social costs and benefits
EXAMPLE OF PERFECT COMPETITION
-farm products marketing
-commodity marketing
-stock exchange securities marketing
=============================================================
THERE IS A VERY FINE/ THIN LINE BETWEEN
THE PERFECT COMPETITION AND THE PURE COMPETITION.
=============================================================
PURE COMPETITION
pure competition - A market model in which (1) a lower price is the only element that leads buyers to prefer one seller to another, and (2) the amount that each individual seller can offer constitutes such a small proportion that acting alone it is powerless to affect the price.
A market structure in which the following five criteria are met:
1. All firms sell an identical product.
2. All firms are price takers.
3. All firms have a relatively small market share.
4. Buyers know the nature of the product being sold and the prices
charged by each firm.
5. The industry is characterized by freedom of entry and exit.
EXAMPLE
-service businesses.
-courier service
etc etc
is an economic model that describes a hypothetical market form in which no producer or consumer has the market power to influence prices. According to the standard economical definition of efficiency (Pareto efficiency), perfect competition would lead to a completely efficient outcome. The analysis of perfectly competitive markets provides the foundation of the theory of supply and demand. Perfect competition is a market equilibrium in which all resources are allocated and used efficiently, and collective social welfare is maximized.
CONDITIONS FOR PERFECT COMPETITION
Often models of perfect competition assume that some subset of the following six conditions be fulfilled. In such a market, prices would normally move instantaneously to economic equilibrium. It should be noted however that these represent sufficient, not necessary conditions.
1.Atomicity
An atomic market is one in which there are a large number of small producers and consumers on a given market, each so small that its actions have no significant impact on others. Firms are price takers, meaning that the market sets the price that they must choose.
2.Homogeneity
Goods and services are perfect substitutes; that is, there is no product differentiation. (All firms sell an identical product)
3.Perfect and complete information
All firms and consumers know the prices set by all firms .
4.Equal access
All firms have access to production technologies, and resources are perfectly mobile.
5.Free entry
Any firm may enter or exit the market as it wishes .
6.Individual buyers and sellers act independently
The market is such that there is no scope for groups of buyers and/or sellers to come together with a view to changing the market price (collusion and cartels are not possible under this market structure)
Behavioral assumptions of perfect competition are that:
Consumers aim to maximize utility
Producers aim to maximize profits.
==============================================
CHARACTERISTICS OF A PERFECT COMPETITION
1. Many suppliers each with an insignificant share of the market – this means that each firm is too small relative to the overall market to affect price via a change in its own supply – each individual firm is assumed to be a price taker
2. An identical output produced by each firm – in other words, the market supplies homogeneous or standardised products that are perfect substitutes for each other. Consumers perceive the products to be identical
3. Consumers have perfect information about the prices all sellers in the market charge – so if some firms decide to charge a price higher than the ruling market price, there will be a large substitution effect away from this firm
4. All firms (industry participants and new entrants) are assumed to have equal access to resources (technology, other factor inputs) and improvements in production technologies achieved by one firm can spill-over to all the other suppliers in the market
5. There are assumed to be no barriers to entry & exit of firms in long run – which means that the market is open to competition from new suppliers – this affects the long run profits made by each firm in the industry. The long run equilibrium for a perfectly competitive market occurs when the marginal firm makes normal profit only in the long term
6. No externalities in production and consumption so that there is no divergence between private and social costs and benefits
EXAMPLE OF PERFECT COMPETITION
-farm products marketing
-commodity marketing
-stock exchange securities marketing
=============================================================
THERE IS A VERY FINE/ THIN LINE BETWEEN
THE PERFECT COMPETITION AND THE PURE COMPETITION.
=============================================================
PURE COMPETITION
pure competition - A market model in which (1) a lower price is the only element that leads buyers to prefer one seller to another, and (2) the amount that each individual seller can offer constitutes such a small proportion that acting alone it is powerless to affect the price.
A market structure in which the following five criteria are met:
1. All firms sell an identical product.
2. All firms are price takers.
3. All firms have a relatively small market share.
4. Buyers know the nature of the product being sold and the prices
charged by each firm.
5. The industry is characterized by freedom of entry and exit.
EXAMPLE
-service businesses.
-courier service
etc etc
MS -09 Question 3
Suppose you are a sales manager of an organization manufacturing consumer durables. How will you analyze the demand? Explain how will this demand contribute to the business decision-making?
CONSUMER DURABLES MARKET DEMAND
-are a unique set of consumers' item.
-they are often seasonal items [ festive or climate]
-they are high ticket items
-the buying decisions are often difficult to predict
-service/ warranty is a key factor in buying.
-financial service too plays a key part in selling.
-sales are influenced by the growth in economy.
-sales are influences by consumers confidence in the economy.
-sales are influenced by the discretionary income also.
etc etc
===========================================
STEP 1
DEMAND FORECAST FOR THE PARTICULAR TYPE OF DURABLES.
Delphi METHOD.
The Delphi technique helps to capture the knowledge of diverse experts while avoiding the disadvantages of traditional group meetings. The latter include bullying and time-wasting.
To forecast with Delphi the administrator should recruit between five and twenty suitable experts and poll them for their forecasts and reasons. The administrator then provides the experts with anonymous summary statistics on the forecasts, and experts’ reasons for their forecasts. The process is repeated until there is little change in forecasts between rounds – two or three rounds are usually sufficient. The Delphi forecast is the median or mode of the experts’ final forecasts.
The forecasts from Delphi groups are substantially more accurate than forecasts from unaided judgement and traditional groups, and are somewhat more accurate than combined forecasts from unaided judgement.
Judgmental Decomposition METHOD.
The basic idea behind judgemental decomposition is to divide the forecasting problem into parts that are easier to forecast than the whole. One then forecasts the parts individually, using methods appropriate to each part. Finally, the parts are combined to obtain a forecast.
One approach is to break the problem down into multiplicative components. For example, to forecast sales for a brand, one can forecast industry sales volume, market share, and selling price per unit. Then reassemble the problem by multiplying the components together. Empirical results indicate that, in general, forecasts from decomposition are more accurate than those from a global approach . In particular, decomposition is more accurate where there is much uncertainty about the aggregate forecast and where large numbers (over one million) are involved.
Causal models METHODS.
Causal models are based on prior knowledge and theory. Time-series regression and cross-sectional regression are commonly used for estimating model parameters or coefficients. These models allow one to examine the effects of marketing activity, such as a change in price, as well as key aspects of the market, thus providing information for contingency planning.
To develop causal models, one needs to select causal variables by using theory and prior knowledge. The key is to identify important variables, the direction of their effects, and any constraints. One should aim for a relatively simple model and use all available data to estimate it . Surprisingly, sophisticated statistical procedures have not led to more accurate forecasts. In fact, crude estimates are often sufficient to provide accurate forecasts when using cross-sectional data .
=========================================
STEP 2
COMPANY SALES FORECASTING FOR COMPANY MODELS.
-FIELD SALES INPUTS BY TERRITORY.
-TIME SERIES PROJECTION.
-SALES FORECASTING BY PRODUCT MANAGERS,BASED ON
THE MARKETING INPUTS.
================================================
STEP 3
THE RESULTS OF STEP 1 AND STEP 2
WOULD GIVE
1.MARKET SHARE FORECAST.
2.SALES FORECAST - VOLUME UNITS/ DOLLARS.
=================================================
Explain how will this demand contribute to the business decision-making?
FOR BETTER BUSINESS DECISION MAKING
THE DEMAND ANALYSIS MUST COVER MOST OF
THE IMPORTANT ELEMENTS IN THE BUSINESS MANAGEMENT.
Demand Forecasting tackles four troublesome areas that, if not adeptly managed, can undermine the validity of a company's DEMAND forecasting and PLANNING processes.
Demand cleansing. Promotions, markdowns, weather and entry errors are just some of the factors that can distort your forecasts. Demand Forecasting has built-in demand cleansing so that forecasters won't be misled by these anomalies.
Seasonal profiling. How do you account for seasonal curves? Demand Forecasting helps you identify trends and seasonal patterns to get a clear picture of the selling curve for a specific time period, product and location. Seasonal profile management in Demand Forecasting automatically accounts for seasonal curves related to moving holidays, has advanced profiling science that selects and assigns the best profile from multiple profile/aggregation iterations and can optionally do an automatic refresh of profiles just before a SKU comes into its next season.
Demand Forecasting. Whether initializing a forecast for the first time, re-initializing after a structural change in demand history or periodically updating the forecast based on recent demand, Demand Forecasting uses our Universal Forecast Method™ that dynamically senses demand and adapts the proper forecasting components from multiple forecast methodologies to fit the demand signal that gives the best forecast.
Exception management. Without an efficient, proactive approach to resolve forecast errors, managing exceptions can be time-consuming and costly. Our Advanced Exception Management gives you the flexibility to adjust the logic and business rules that govern the creation and management of exceptions. This tool improves productivity by enabling automatic detection and self-correction of many problems.
**********************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Multi-Channel Planning ,
that gives you enterprise-wide visibility to what's happening in each channel.
Multi-Channel Planning greatly expands your profit potential because multi-channel shoppers spend four times more than single-channel shoppers. Capitalizing on a multi-channel strategy, therefore, is essential to the health of every business.
But that's tough to do when senior executives, lacking a holistic view, have to review performance to plan on a channel by channel basis, or lack the tools to manage individual channels effectively. Focusing on each channel often obscures focusing what is happening at the enterprise level. For example, management may not detect that customer service improvements in one channel are eroding customer service in another channel, or that a hot seller in one channel causes another to be out of stock.
Now, with one solution, you can plan and track individual channels and see a consolidated plan for all channels so you can effectively manage inventory at an enterprise level. Multi-Channel Planning's flexibility combines channels' specific structures, attributes and planning metrics with the ability to analyze and plan across channels when you need to.
***********************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Financial Planning: Reconciling Goals across your business
this approach delivers these key benefits to the business:
Gain enterprise-wide visibility to product information. You can plan and forecast sales and inventory requirements across multiple levels of the category hierarchy, all the way down to the item level.
Maximize productivity. The solutions are easy to use with fast response times Your planners will be able to concentrate on planning and analysis instead of data gathering, data entry and data validation.
Get rapid results. The solutions can group, seed and reconcile plans dynamically for maximum, unconstrained planning flexibility.
Increase sales and margins. Now you have the means to react quickly to changing customer needs and ensure you have the right product, at the right price, in the right channels at the right time.
****************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Assortment Planning: Completing the Triple Play
this approach helps to align the organization's assortment strategy with its financial goals.
Properly executed, enterprise-level planning and budgeting invariably involves a great deal of top-down and bottom-up collaboration. -provides structures, views and measures specific to each demand channel's planning process. Then they align plans across channels, integrate key performance indicators and reconcile plans with corporate objectives.
Assortment Planning ensures you achieve the right mix of products for your customer in every channel and category.
The solution enables you to build and manage assortments using unlimited attributes, taking into account space capacity, display criteria, open to buy and customer preferences. Any changes to the assortment are automatically reconciled to attribute mix targets and financial goals.
THIS APPROACH HELPS TO
Manage the end-to-end process of building, managing and planning assortments for new and existing products (and their variations)
Keep up with assortment hierarchies, including start and end dates and unlimited assortment information
Track attribute mix versus target
Analyze best sellers from previous or similar assortments
Plan unique assortments to accommodate each location's specific situation
Plan placeholder and proxy items with like history
Plan and track items using multiple measures and versions and reconcile back to financial goals
Plan by average store, cluster or store in your retail channel
Plan by campaign, book or media drop for your direct channel
**********************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Item Planning
THIS delivers these benefits:
Gain enterprise-wide visibility to product information. You can plan and forecast sales and inventory requirements across multiple levels of the product hierarchy, all the way down to the SKU level.
Maximize productivity. The solution is easy to use with fast response times. Your staff will be able to concentrate on planning and analysis instead of data gathering, entry, grouping and reconciling.
Get rapid results. Group, seed and reconcile plans dynamically for maximum, unconstrained planning flexibility.
Increase sales and margins. Now you have the means to react quickly to changing customer needs and can ensure you have the right product, at the right price, in the right locations at the right time.
*********************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Promotion Planning: Ramp Up Profits By Slashing Errors
Promotion Planning gives you end-to-end control of every step in the process:
Forecasting. Initializes promotional forecasts using raw historical sales, eliminating the need for pre-existing promotion history.
Setting up assortments. Integrates with Assortment Planning to select, plan and manage different promotional product assortments for various locations.
Maintaining data. Supports promotion-specific attributes and measures, including page number, display fixture, offer and price zone.
Fine-tuning the forecast. Uses various "what-if" simulations to test strategies.
Building channel-specific promotions. Tracks data based on each channel's unique criteria.
Gaining visibility. Consolidates plans for analysis and purchasing across channels.
Managing promotion information. Uses a central repository for events, products, vendors and locations.
Monitoring item demand. Automatically updates forecasts based on actual performance data.
Maintaining optimum inventory. Automatically feeds orders to REPLENISHMENT SYSTEM.
***********************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Store Clustering:
Your planning process takes into account demand forecasts, top-down financial goals, bottom-up product plans, assortments and multiple channels.
What's missing? The customer! DEMAND planners are looking for ways to plug customer demographics and attributes into their forecasts, especially promotion and assortment plans.
Store Clustering enables you to develop plans that are truly aligned to your customers' preference, no matter where they shop. Without an army of planners, you'll be able to create plans that group similar locations by performance, size, climate, customer demographics, store format or other measures or attributes. This means you can stock each location in the cluster with merchandise generating the highest potential profits.
Provides flexible location attributing that can be managed within the solution itself for consistency and accuracy
Enables users to copy, edit and regenerate clusters as the business changes
Supports new stores so you can cluster based on like history
Interfaces with ASSORTMENT PLANNING so that you can build and manage assortments using unlimited attributes
Interfaces with PROMOTION PLANNING to streamline the planning and execution of promotional events
*****************************************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Inventory Optimization: Get It Right the First Time—And Every Time
Even small variances in inventory can have big repercussions in your supply chain. Caught with too little and you have rush charges, express delivery fees and unhappy customers. Order too much and you increase costs and risk write-offs for obsolete or expired goods. A view of your inventory across all channels ensures that your goods are where they're needed—not forgotten in a warehouse or reserved for stores while your Internet customers receive out-of-stock messages.
If inventory issues keep you from doing the best for your customers and your bottom line, Inventory Optimization can help you get it right, every time:
Replenishment—Profitable replenishment is a strategic advantage. Selling generates revenue, but smarter replenishment generates profit. Turn over inventory faster by ensuring you have what you need, where you need it, when you need it.
Multi-Echelon—Manage forecasting and replenishment across all your distribution channels from one application.
Vendor Managed Inventory—Share your inventory data with suppliers so they can time shipments and manage production—and you can please customers.
Collaboration Gateway—Give valued partners access to inventory, replenishment events and other specific data involving goods they sell to you or buy from you.
********************************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Order Lifecycle Management: Streamline the Order, Fulfillment and Returns Process
Distributed Order Management—Keep your supply and customer demand in profitable balance. A global view of inventory—at the supplier, in transit or at the warehouse—combines with cross-channel order reporting to enable you to deliver what your customers want, when they want it. Satisfy demand using the full supply pipeline by diverting in-flight inventory to where it's needed most: directly to customers, directly to stores, or to the appropriate distribution center based on real-time inventory positions. Manage inventory across channels by creating virtual divisions in the Distributed Order Management layer, alleviating cross-channel inventory complexity from distribution center operators.
Reverse Logistics Management—Make returns efficient, accurate and easy. Capture customer information, track return reasons, and automatically select the optimal mode of transportation to make returns a source of valuable customer and quality information. Automate vendor rules and streamline return-to-vendor credit process. Improve vendor buyback, reduce cycle time and improve open-to-buy.
Store/Customer Gateway—Let customers and stores place, track and confirm their orders on line, giving you proof of receipt, opportunities for feedback—and happier customers.
**********************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Transportation Lifecycle Management: From Bidding to Billing, See Where You Are and What You Need
Keep on Trucking... or Shipping or Flying on Time and within Budget
Transportation Procurement—From single lanes to entire networks, manage your bid process entirely online to obtain the right carriers, right modes at the best price.
Transportation Planning and Execution—Optimally manage all your transportation activities so you can coordinate, redirect and stay on schedule. Deliver superior service to your customers with full visibility and event management capabilities.
Logistics Gateway—Share critical data in real time to keep all your transportation partners working together. Enable communication with suppliers and carriers to request shipments, provide updates and settle financial concerns.
Fleet Management—Manage and optimize all assets of both private and dedicated fleet operations including drivers, tractors and trailers.
Audit Payment and Claims—Pay for only the services you used at the price you contracted. Identify overcharges, duplicate bills and other errors immediately. Manage both cargo and detention claims with carriers and suppliers.
Appointment Scheduling—Avoid charge-backs from Hours of Service violations and staff your warehouse appropriately by encouraging carriers to schedule deliveries online.
Yard Management—Know trailer positions and status instantly. Schedule arrivals by dock and reduce loading and unloading time.
***************************************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Distribution Management: Control and Collaboration from Supplier to Customer
Solutions for Every Link in a Competitive Supply Chain
Warehouse Management—Fine-tune your facility with a more efficient layout, well utilized resources, streamlined inventory and flawless order fulfillment.
Slotting Optimization—Match slots to demand, weight and other product characteristics for faster, more accurate picking resulting in improved productivity.
Labor Management—Standardize and track workforce performance throughout your operation. Reward quality and safety, boost productivity and forecast with better precision.
Billing Management—Assign and manage charges for virtually any warehouse event for a full understanding of your costs and profits. Track activities by unit or client.
Supplier Enablement—Extend powerful supply chain capabilities to your suppliers, and automate communications and record-keeping—all online.
Hub Management—Give hubs and providers instant visibility of orders, shipments and inventory. Streamline transport and inventory by managing partner-to-partner shipping.
CONSUMER DURABLES MARKET DEMAND
-are a unique set of consumers' item.
-they are often seasonal items [ festive or climate]
-they are high ticket items
-the buying decisions are often difficult to predict
-service/ warranty is a key factor in buying.
-financial service too plays a key part in selling.
-sales are influenced by the growth in economy.
-sales are influences by consumers confidence in the economy.
-sales are influenced by the discretionary income also.
etc etc
===========================================
STEP 1
DEMAND FORECAST FOR THE PARTICULAR TYPE OF DURABLES.
Delphi METHOD.
The Delphi technique helps to capture the knowledge of diverse experts while avoiding the disadvantages of traditional group meetings. The latter include bullying and time-wasting.
To forecast with Delphi the administrator should recruit between five and twenty suitable experts and poll them for their forecasts and reasons. The administrator then provides the experts with anonymous summary statistics on the forecasts, and experts’ reasons for their forecasts. The process is repeated until there is little change in forecasts between rounds – two or three rounds are usually sufficient. The Delphi forecast is the median or mode of the experts’ final forecasts.
The forecasts from Delphi groups are substantially more accurate than forecasts from unaided judgement and traditional groups, and are somewhat more accurate than combined forecasts from unaided judgement.
Judgmental Decomposition METHOD.
The basic idea behind judgemental decomposition is to divide the forecasting problem into parts that are easier to forecast than the whole. One then forecasts the parts individually, using methods appropriate to each part. Finally, the parts are combined to obtain a forecast.
One approach is to break the problem down into multiplicative components. For example, to forecast sales for a brand, one can forecast industry sales volume, market share, and selling price per unit. Then reassemble the problem by multiplying the components together. Empirical results indicate that, in general, forecasts from decomposition are more accurate than those from a global approach . In particular, decomposition is more accurate where there is much uncertainty about the aggregate forecast and where large numbers (over one million) are involved.
Causal models METHODS.
Causal models are based on prior knowledge and theory. Time-series regression and cross-sectional regression are commonly used for estimating model parameters or coefficients. These models allow one to examine the effects of marketing activity, such as a change in price, as well as key aspects of the market, thus providing information for contingency planning.
To develop causal models, one needs to select causal variables by using theory and prior knowledge. The key is to identify important variables, the direction of their effects, and any constraints. One should aim for a relatively simple model and use all available data to estimate it . Surprisingly, sophisticated statistical procedures have not led to more accurate forecasts. In fact, crude estimates are often sufficient to provide accurate forecasts when using cross-sectional data .
=========================================
STEP 2
COMPANY SALES FORECASTING FOR COMPANY MODELS.
-FIELD SALES INPUTS BY TERRITORY.
-TIME SERIES PROJECTION.
-SALES FORECASTING BY PRODUCT MANAGERS,BASED ON
THE MARKETING INPUTS.
================================================
STEP 3
THE RESULTS OF STEP 1 AND STEP 2
WOULD GIVE
1.MARKET SHARE FORECAST.
2.SALES FORECAST - VOLUME UNITS/ DOLLARS.
=================================================
Explain how will this demand contribute to the business decision-making?
FOR BETTER BUSINESS DECISION MAKING
THE DEMAND ANALYSIS MUST COVER MOST OF
THE IMPORTANT ELEMENTS IN THE BUSINESS MANAGEMENT.
Demand Forecasting tackles four troublesome areas that, if not adeptly managed, can undermine the validity of a company's DEMAND forecasting and PLANNING processes.
Demand cleansing. Promotions, markdowns, weather and entry errors are just some of the factors that can distort your forecasts. Demand Forecasting has built-in demand cleansing so that forecasters won't be misled by these anomalies.
Seasonal profiling. How do you account for seasonal curves? Demand Forecasting helps you identify trends and seasonal patterns to get a clear picture of the selling curve for a specific time period, product and location. Seasonal profile management in Demand Forecasting automatically accounts for seasonal curves related to moving holidays, has advanced profiling science that selects and assigns the best profile from multiple profile/aggregation iterations and can optionally do an automatic refresh of profiles just before a SKU comes into its next season.
Demand Forecasting. Whether initializing a forecast for the first time, re-initializing after a structural change in demand history or periodically updating the forecast based on recent demand, Demand Forecasting uses our Universal Forecast Method™ that dynamically senses demand and adapts the proper forecasting components from multiple forecast methodologies to fit the demand signal that gives the best forecast.
Exception management. Without an efficient, proactive approach to resolve forecast errors, managing exceptions can be time-consuming and costly. Our Advanced Exception Management gives you the flexibility to adjust the logic and business rules that govern the creation and management of exceptions. This tool improves productivity by enabling automatic detection and self-correction of many problems.
**********************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Multi-Channel Planning ,
that gives you enterprise-wide visibility to what's happening in each channel.
Multi-Channel Planning greatly expands your profit potential because multi-channel shoppers spend four times more than single-channel shoppers. Capitalizing on a multi-channel strategy, therefore, is essential to the health of every business.
But that's tough to do when senior executives, lacking a holistic view, have to review performance to plan on a channel by channel basis, or lack the tools to manage individual channels effectively. Focusing on each channel often obscures focusing what is happening at the enterprise level. For example, management may not detect that customer service improvements in one channel are eroding customer service in another channel, or that a hot seller in one channel causes another to be out of stock.
Now, with one solution, you can plan and track individual channels and see a consolidated plan for all channels so you can effectively manage inventory at an enterprise level. Multi-Channel Planning's flexibility combines channels' specific structures, attributes and planning metrics with the ability to analyze and plan across channels when you need to.
***********************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Financial Planning: Reconciling Goals across your business
this approach delivers these key benefits to the business:
Gain enterprise-wide visibility to product information. You can plan and forecast sales and inventory requirements across multiple levels of the category hierarchy, all the way down to the item level.
Maximize productivity. The solutions are easy to use with fast response times Your planners will be able to concentrate on planning and analysis instead of data gathering, data entry and data validation.
Get rapid results. The solutions can group, seed and reconcile plans dynamically for maximum, unconstrained planning flexibility.
Increase sales and margins. Now you have the means to react quickly to changing customer needs and ensure you have the right product, at the right price, in the right channels at the right time.
****************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Assortment Planning: Completing the Triple Play
this approach helps to align the organization's assortment strategy with its financial goals.
Properly executed, enterprise-level planning and budgeting invariably involves a great deal of top-down and bottom-up collaboration. -provides structures, views and measures specific to each demand channel's planning process. Then they align plans across channels, integrate key performance indicators and reconcile plans with corporate objectives.
Assortment Planning ensures you achieve the right mix of products for your customer in every channel and category.
The solution enables you to build and manage assortments using unlimited attributes, taking into account space capacity, display criteria, open to buy and customer preferences. Any changes to the assortment are automatically reconciled to attribute mix targets and financial goals.
THIS APPROACH HELPS TO
Manage the end-to-end process of building, managing and planning assortments for new and existing products (and their variations)
Keep up with assortment hierarchies, including start and end dates and unlimited assortment information
Track attribute mix versus target
Analyze best sellers from previous or similar assortments
Plan unique assortments to accommodate each location's specific situation
Plan placeholder and proxy items with like history
Plan and track items using multiple measures and versions and reconcile back to financial goals
Plan by average store, cluster or store in your retail channel
Plan by campaign, book or media drop for your direct channel
**********************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Item Planning
THIS delivers these benefits:
Gain enterprise-wide visibility to product information. You can plan and forecast sales and inventory requirements across multiple levels of the product hierarchy, all the way down to the SKU level.
Maximize productivity. The solution is easy to use with fast response times. Your staff will be able to concentrate on planning and analysis instead of data gathering, entry, grouping and reconciling.
Get rapid results. Group, seed and reconcile plans dynamically for maximum, unconstrained planning flexibility.
Increase sales and margins. Now you have the means to react quickly to changing customer needs and can ensure you have the right product, at the right price, in the right locations at the right time.
*********************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Promotion Planning: Ramp Up Profits By Slashing Errors
Promotion Planning gives you end-to-end control of every step in the process:
Forecasting. Initializes promotional forecasts using raw historical sales, eliminating the need for pre-existing promotion history.
Setting up assortments. Integrates with Assortment Planning to select, plan and manage different promotional product assortments for various locations.
Maintaining data. Supports promotion-specific attributes and measures, including page number, display fixture, offer and price zone.
Fine-tuning the forecast. Uses various "what-if" simulations to test strategies.
Building channel-specific promotions. Tracks data based on each channel's unique criteria.
Gaining visibility. Consolidates plans for analysis and purchasing across channels.
Managing promotion information. Uses a central repository for events, products, vendors and locations.
Monitoring item demand. Automatically updates forecasts based on actual performance data.
Maintaining optimum inventory. Automatically feeds orders to REPLENISHMENT SYSTEM.
***********************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Store Clustering:
Your planning process takes into account demand forecasts, top-down financial goals, bottom-up product plans, assortments and multiple channels.
What's missing? The customer! DEMAND planners are looking for ways to plug customer demographics and attributes into their forecasts, especially promotion and assortment plans.
Store Clustering enables you to develop plans that are truly aligned to your customers' preference, no matter where they shop. Without an army of planners, you'll be able to create plans that group similar locations by performance, size, climate, customer demographics, store format or other measures or attributes. This means you can stock each location in the cluster with merchandise generating the highest potential profits.
Provides flexible location attributing that can be managed within the solution itself for consistency and accuracy
Enables users to copy, edit and regenerate clusters as the business changes
Supports new stores so you can cluster based on like history
Interfaces with ASSORTMENT PLANNING so that you can build and manage assortments using unlimited attributes
Interfaces with PROMOTION PLANNING to streamline the planning and execution of promotional events
*****************************************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Inventory Optimization: Get It Right the First Time—And Every Time
Even small variances in inventory can have big repercussions in your supply chain. Caught with too little and you have rush charges, express delivery fees and unhappy customers. Order too much and you increase costs and risk write-offs for obsolete or expired goods. A view of your inventory across all channels ensures that your goods are where they're needed—not forgotten in a warehouse or reserved for stores while your Internet customers receive out-of-stock messages.
If inventory issues keep you from doing the best for your customers and your bottom line, Inventory Optimization can help you get it right, every time:
Replenishment—Profitable replenishment is a strategic advantage. Selling generates revenue, but smarter replenishment generates profit. Turn over inventory faster by ensuring you have what you need, where you need it, when you need it.
Multi-Echelon—Manage forecasting and replenishment across all your distribution channels from one application.
Vendor Managed Inventory—Share your inventory data with suppliers so they can time shipments and manage production—and you can please customers.
Collaboration Gateway—Give valued partners access to inventory, replenishment events and other specific data involving goods they sell to you or buy from you.
********************************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Order Lifecycle Management: Streamline the Order, Fulfillment and Returns Process
Distributed Order Management—Keep your supply and customer demand in profitable balance. A global view of inventory—at the supplier, in transit or at the warehouse—combines with cross-channel order reporting to enable you to deliver what your customers want, when they want it. Satisfy demand using the full supply pipeline by diverting in-flight inventory to where it's needed most: directly to customers, directly to stores, or to the appropriate distribution center based on real-time inventory positions. Manage inventory across channels by creating virtual divisions in the Distributed Order Management layer, alleviating cross-channel inventory complexity from distribution center operators.
Reverse Logistics Management—Make returns efficient, accurate and easy. Capture customer information, track return reasons, and automatically select the optimal mode of transportation to make returns a source of valuable customer and quality information. Automate vendor rules and streamline return-to-vendor credit process. Improve vendor buyback, reduce cycle time and improve open-to-buy.
Store/Customer Gateway—Let customers and stores place, track and confirm their orders on line, giving you proof of receipt, opportunities for feedback—and happier customers.
**********************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Transportation Lifecycle Management: From Bidding to Billing, See Where You Are and What You Need
Keep on Trucking... or Shipping or Flying on Time and within Budget
Transportation Procurement—From single lanes to entire networks, manage your bid process entirely online to obtain the right carriers, right modes at the best price.
Transportation Planning and Execution—Optimally manage all your transportation activities so you can coordinate, redirect and stay on schedule. Deliver superior service to your customers with full visibility and event management capabilities.
Logistics Gateway—Share critical data in real time to keep all your transportation partners working together. Enable communication with suppliers and carriers to request shipments, provide updates and settle financial concerns.
Fleet Management—Manage and optimize all assets of both private and dedicated fleet operations including drivers, tractors and trailers.
Audit Payment and Claims—Pay for only the services you used at the price you contracted. Identify overcharges, duplicate bills and other errors immediately. Manage both cargo and detention claims with carriers and suppliers.
Appointment Scheduling—Avoid charge-backs from Hours of Service violations and staff your warehouse appropriately by encouraging carriers to schedule deliveries online.
Yard Management—Know trailer positions and status instantly. Schedule arrivals by dock and reduce loading and unloading time.
***************************************************************************************************************************
WHILE DEMAND FORECASTING/ PLANNING,
CONSIDER
Distribution Management: Control and Collaboration from Supplier to Customer
Solutions for Every Link in a Competitive Supply Chain
Warehouse Management—Fine-tune your facility with a more efficient layout, well utilized resources, streamlined inventory and flawless order fulfillment.
Slotting Optimization—Match slots to demand, weight and other product characteristics for faster, more accurate picking resulting in improved productivity.
Labor Management—Standardize and track workforce performance throughout your operation. Reward quality and safety, boost productivity and forecast with better precision.
Billing Management—Assign and manage charges for virtually any warehouse event for a full understanding of your costs and profits. Track activities by unit or client.
Supplier Enablement—Extend powerful supply chain capabilities to your suppliers, and automate communications and record-keeping—all online.
Hub Management—Give hubs and providers instant visibility of orders, shipments and inventory. Streamline transport and inventory by managing partner-to-partner shipping.
MS-09 Question1. Critically discuss different methods of demand forecasting.
Critically discuss different methods of demand forecasting.
Methods Based on Judgment
Unaided judgment METHOD.
It is common practice to ask experts what will happen. This is a good procedure to use when • experts are unbiased • large changes are unlikely • relationships are well understood by experts (e.g., demand goes up when prices go down) • experts possess privileged information • experts receive accurate and well-summarized feedback about their forecasts.
Prediction markets METHOD.
Prediction markets, also known as betting markets, information markets, and futures markets have a long history.
Some commercial organisations provide internet markets and software that to allow participants to predict.Consultants can also set up betting markets within firms to bet on such things as the sales growth of a new product. PREDICTIONS can produce accurate sales forecasts when used within companies. However, there are no empirical studies that compare forecasts from prediction markets and with those from traditional groups or from other methods.
Delphi METHOD.
The Delphi technique helps to capture the knowledge of diverse experts while avoiding the disadvantages of traditional group meetings. The latter include bullying and time-wasting. To forecast with Delphi the administrator should recruit between five and twenty suitable experts and poll them for their forecasts and reasons. The administrator then provides the experts with anonymous summary statistics on the forecasts, and experts’ reasons for their forecasts. The process is repeated until there is little change in forecasts between rounds – two or three rounds are usually sufficient. The Delphi forecast is the median or mode of the experts’ final forecasts. The forecasts from Delphi groups are substantially more accurate than forecasts from unaided judgement and traditional groups, and are somewhat more accurate than combined forecasts from unaided judgement.
Structured analogies METHOD.
The outcomes of similar situations from the past (analogies) may help a marketer to forecast the outcome of a new (target) situation. For example, the introduction of new products in the markets can provide analogies for the outcomes of the subsequent release of similar products in other countries. People often use analogies to make forecasts, but they do not do so in a structured manner. For example, they might search for an analogy that suits their prior beliefs or they might stop searching when they identify one analogy. The structured-analogies method uses a formal process to overcome biased and inefficient use of information from analogous situations. To use the structured analogies method, an administrator prepares a description of the target situation and selects experts who have knowledge of analogous situations; preferably direct experience. The experts identify and describe analogous situations, rate their similarity to the target situation, and match the outcomes of their analogies with potential outcomes in the target situation. The administrator then derives forecasts from the information the experts provided on their most similar analogies. Structured analogies are more accurate than unaided judgment in forecasting decisions .
Game theory METHOD.
is a way to obtain better forecasts in situations involving negotiations or other conflicts. BUT IT IS NOT A RELIABLE METHOD.
Judgmental Decomposition METHOD.
The basic idea behind judgemental decomposition is to divide the forecasting problem into parts that are easier to forecast than the whole. One then forecasts the parts individually, using methods appropriate to each part. Finally, the parts are combined to obtain a forecast. One approach is to break the problem down into multiplicative components. For example, to forecast sales for a brand, one can forecast industry sales volume, market share, and selling price per unit. Then reassemble the problem by multiplying the components together. Empirical results indicate that, in general, forecasts from decomposition are more accurate than those from a global approach . In particular, decomposition is more accurate where there is much uncertainty about the aggregate forecast and where large numbers (over one million) are involved.
Expert systems METHOD.
As the name implies, expert systems are structured representations of the rules experts use to make predictions or diagnoses. For example, ‘if local household incomes are in the bottom quartile, then do not supply premium brands’. The forecast is implicit in the foregoing conditional action statement: i.e., premium brands are unlikely to make an acceptable return in the locale. Rules are often created from protocols, whereby forecasters talk about what they are doing while making forecasts. Where empirical estimates of relationships from structured analysis such as econometric studies are available, expert systems should use that information. Expert opinion, conjoint analysis, and bootstrapping can also aid in the development of expert systems. Expert systems forecasting involves identifying forecasting rules used by experts and rules learned from empirical research. One should aim for simplicity and completeness in the resulting system, and the system should explain forecasts to users. Developing an expert system is expensive and so the method will only be of interest in situations where many forecasts of a similar kind are required. Expert systems are feasible where problems are sufficiently well-structured for rules to be identified. Expert systems forecasts are more accurate than those from unaided judgement.
Simulated interaction METHOD
Simulated interaction is a form of role playing for predicting decisions by people who are interacting with others. It is especially useful when the situation involves conflict. For example, one might wish to forecast how best to secure an exclusive distribution arrangement with a major supplier. To use simulated interaction, an administrator prepares a description of the target situation, describes the main protagonists’ roles, and provides a list of possible decisions. Role players adopt a role and read about the situation. They then improvise realistic interactions with the other role players until they reach a decision; for example to sign a trial one-year exclusive distribution agreement. The role players’ decisions are used to make the forecast. Forecasts from simulated interactions were substantially more accurate than can be obtained from unaided judgement. Simulated interaction can also help to maintain secrecy. Information on simulated interaction is available from conflictforecasting.com.
Intentions and expectations surveys METHOD.With intentions surveys, people are asked how they intend to behave in specified situations. In a similar manner, an expectations survey asks people how they expect to behave. Expectations differ from intentions because people realize that unintended things happen. For example, if you were asked whether you intended to visit the dentist in the next six months you might say no. However, you realize that a problem might arise that would necessitate such a visit, so your expectations would be that the event had a probability greater than zero.
Expectations and intentions can be obtained using probability scales . The scale should have descriptions such as 0 = ‘No chance, or almost no chance (1 in 100)’ to 10 = ‘Certain, or practically certain (99 in 100)’. To forecast demand using a survey of potential consumers, the administrator should prepare an accurate and comprehensive description of the product and conditions of sale. He should select a representative sample of the population of interest and develop questions to elicit expectations from respondents. Bias in responses should be assessed if possible and the data adjusted accordingly. The behaviour of the population is forecast by aggregating the survey responses.
Conjoint analysis METHOD.
By surveying consumers about their preferences for alternative product designs in a structured way, it is possible to infer how different features will influence demand. Potential customers might be presented with a series of perhaps 20 pairs of offerings. For example, various features of a personal digital assistant such as price, weight, battery life, screen clarity and memory could be varied substantially such that the features do not correlate with one another. The potential customer is thus forced to make trade-offs among various features by choosing one of each pair of offerings in a way that is representative of how they would choose in the marketplace. The resulting data can be analysed by regressing respondents’ choices against the product features. The method is based on sound principles, such as using experimental design and soliciting independent intentions from a sample of potential customers. Unfortunately however, there do not appear to be studies that compare conjoint-analysis forecasts with forecasts from other reasonable methods.
Methods requiring quantitative data
Extrapolation METHOD
Extrapolation methods use historical data on that which one wishes to forecast. Exponential smoothing is the most popular and cost effective of the statistical extrapolation methods. It implements the principle that recent data should be weighted more heavily and ‘smoothes’ out cyclical fluctuations to forecast the trend. To use exponential smoothing to extrapolate, the administrator should first clean and deseasonalise the data, and select reasonable smoothing factors. The administrator then calculates an average and trend from the data and uses these to derive a forecast Statistical extrapolations are cost effective when forecasts are needed for each of hundreds of inventory items. They are also useful where forecasters are biased or ignorant of the situation . Allow for seasonality when using quarterly, monthly, or daily data. Most firms do this . Seasonality adjustments led to substantial gains in accuracy in the large-scale study of time series .
Quantitative analogies METHOD.
Experts can identify situations that are analogous to a given situation. These can be used to extrapolate the outcome of a target situation. For example, to assess the loss in sales when the patent protection for a drug is removed, one might examine the historical pattern of sales for analogous drugs. To forecast using quantitative analogies, ask experts to identify situations that are analogous to the target situation and for which data are available. If the analogous data provides information about the future of the target situation, such as per capita ticket sales for a play that is touring from city to city, forecast by calculating averages. If not, construct one model using target situation data and another using analogous data. Combine the parameters of the models, and forecast with the combined model.
Rule-based forecasting METHODS
Rule-based forecasting (RBF) is a type of expert system that allows one to integrate managers’ knowledge about the domain with time-series data in a structured and inexpensive way. For example, in many cases a useful guideline is that trends should be extrapolated only when they agree with managers’ prior expectations. When the causal forces are contrary to the trend in the historical series, forecast errors tend to be large . Although such problems occur only in a small percentage of cases, their effects are serious. To apply RBF, one must first identify features of the series using statistical analysis, inspection, and domain knowledge (including causal forces). The rules are then used to adjust data, and to estimate short- and long-range models. RBF forecasts are a blend of the short- and long-range model forecasts. RBF is most useful when substantive domain knowledge is available, patterns are discernable in the series, trends are strong, and forecasts are needed for long horizons. Under such conditions, errors for rule-based forecasts are substantially less than those for combined forecasts . In cases where the conditions were not met, forecast accuracy is not harmed.
Neural nets METHODS
Neural networks are computer intensive methods that use decision processes analogous to those of the human brain. Like the brain, they have the capability of learning as patterns change and updating their parameter estimates. However, much data is needed in order to estimate neural network models and to reduce the risk of over-fitting the data .There is some evidence that neural network models can produce forecasts that are more accurate than those from other methods . While this is encouraging, our current advice is to avoid neural networks because the method ignores prior knowledge and because the results are difficult to understand.
Data mining METHODS
Data mining uses sophisticated statistical analyses to identify relationships. It is a popular approach. Data mining ignores theory and prior knowledge in a search for patterns. Despite ambitious claims and much research effort, we are not aware of evidence that data mining techniques provide benefits for forecasting.
Causal models METHODS.
Causal models are based on prior knowledge and theory. Time-series regression and cross-sectional regression are commonly used for estimating model parameters or coefficients. These models allow one to examine the effects of marketing activity, such as a change in price, as well as key aspects of the market, thus providing information for contingency planning. To develop causal models, one needs to select causal variables by using theory and prior knowledge. The key is to identify important variables, the direction of their effects, and any constraints. One should aim for a relatively simple model and use all available data to estimate it . Surprisingly, sophisticated statistical procedures have not led to more accurate forecasts. In fact, crude estimates are often sufficient to provide accurate forecasts when using cross-sectional data .
=======================================================
General principles •
Managers’ domain knowledge should be incorporated into forecasting methods.
• When making forecasts in highly uncertain situations, be conservative. For example, the trend should be dampened over the forecast horizon.
• Complex methods have not proven to be more accurate than relatively simple methods. Given their added cost and the reduced understanding among users, highly complex procedures cannot be justified.
• When possible, forecasting methods should use data on actual behaviour, rather than judgments or intentions, to predict behaviour.
• Methods that integrate judgmental and statistical data and procedures (e.g., rule-based forecasting) can improve forecast accuracy in many situations.
• Overconfidence occurs with quantitative and judgmental methods.
• When making forecasts in situations with high uncertainty, use more than one method and combine the forecasts, generally using simple averages.
Methods based on judgment
• When using judgment, rely on structured procedures such as Delphi, simulated interaction, structured analogies, and conjoint analysis.
• Simulated interaction is useful to predict the decisions in conflict situations, such as in negotiations. • In addition to seeking good feedback, forecasters should explicitly list all the things that might be wrong about their forecast. This will produce better calibrated prediction intervals.
Methods based on statistical data
• With the proliferation of data, causal models play an increasingly important role in forecasting market size, market share, and sales.
• Methods should be developed primarily on the basis of theory, not data.
Finally, efforts should be made to ensure forecasts are free of political considerations in a firm. To help with this, emphasis should be on gaining agreement about the forecasting methods. Also, for important forecasts, decisions on their use should be made before the forecasts are provided. Scenarios are helpful in guiding this process.
Methods Based on Judgment
Unaided judgment METHOD.
It is common practice to ask experts what will happen. This is a good procedure to use when • experts are unbiased • large changes are unlikely • relationships are well understood by experts (e.g., demand goes up when prices go down) • experts possess privileged information • experts receive accurate and well-summarized feedback about their forecasts.
Prediction markets METHOD.
Prediction markets, also known as betting markets, information markets, and futures markets have a long history.
Some commercial organisations provide internet markets and software that to allow participants to predict.Consultants can also set up betting markets within firms to bet on such things as the sales growth of a new product. PREDICTIONS can produce accurate sales forecasts when used within companies. However, there are no empirical studies that compare forecasts from prediction markets and with those from traditional groups or from other methods.
Delphi METHOD.
The Delphi technique helps to capture the knowledge of diverse experts while avoiding the disadvantages of traditional group meetings. The latter include bullying and time-wasting. To forecast with Delphi the administrator should recruit between five and twenty suitable experts and poll them for their forecasts and reasons. The administrator then provides the experts with anonymous summary statistics on the forecasts, and experts’ reasons for their forecasts. The process is repeated until there is little change in forecasts between rounds – two or three rounds are usually sufficient. The Delphi forecast is the median or mode of the experts’ final forecasts. The forecasts from Delphi groups are substantially more accurate than forecasts from unaided judgement and traditional groups, and are somewhat more accurate than combined forecasts from unaided judgement.
Structured analogies METHOD.
The outcomes of similar situations from the past (analogies) may help a marketer to forecast the outcome of a new (target) situation. For example, the introduction of new products in the markets can provide analogies for the outcomes of the subsequent release of similar products in other countries. People often use analogies to make forecasts, but they do not do so in a structured manner. For example, they might search for an analogy that suits their prior beliefs or they might stop searching when they identify one analogy. The structured-analogies method uses a formal process to overcome biased and inefficient use of information from analogous situations. To use the structured analogies method, an administrator prepares a description of the target situation and selects experts who have knowledge of analogous situations; preferably direct experience. The experts identify and describe analogous situations, rate their similarity to the target situation, and match the outcomes of their analogies with potential outcomes in the target situation. The administrator then derives forecasts from the information the experts provided on their most similar analogies. Structured analogies are more accurate than unaided judgment in forecasting decisions .
Game theory METHOD.
is a way to obtain better forecasts in situations involving negotiations or other conflicts. BUT IT IS NOT A RELIABLE METHOD.
Judgmental Decomposition METHOD.
The basic idea behind judgemental decomposition is to divide the forecasting problem into parts that are easier to forecast than the whole. One then forecasts the parts individually, using methods appropriate to each part. Finally, the parts are combined to obtain a forecast. One approach is to break the problem down into multiplicative components. For example, to forecast sales for a brand, one can forecast industry sales volume, market share, and selling price per unit. Then reassemble the problem by multiplying the components together. Empirical results indicate that, in general, forecasts from decomposition are more accurate than those from a global approach . In particular, decomposition is more accurate where there is much uncertainty about the aggregate forecast and where large numbers (over one million) are involved.
Expert systems METHOD.
As the name implies, expert systems are structured representations of the rules experts use to make predictions or diagnoses. For example, ‘if local household incomes are in the bottom quartile, then do not supply premium brands’. The forecast is implicit in the foregoing conditional action statement: i.e., premium brands are unlikely to make an acceptable return in the locale. Rules are often created from protocols, whereby forecasters talk about what they are doing while making forecasts. Where empirical estimates of relationships from structured analysis such as econometric studies are available, expert systems should use that information. Expert opinion, conjoint analysis, and bootstrapping can also aid in the development of expert systems. Expert systems forecasting involves identifying forecasting rules used by experts and rules learned from empirical research. One should aim for simplicity and completeness in the resulting system, and the system should explain forecasts to users. Developing an expert system is expensive and so the method will only be of interest in situations where many forecasts of a similar kind are required. Expert systems are feasible where problems are sufficiently well-structured for rules to be identified. Expert systems forecasts are more accurate than those from unaided judgement.
Simulated interaction METHOD
Simulated interaction is a form of role playing for predicting decisions by people who are interacting with others. It is especially useful when the situation involves conflict. For example, one might wish to forecast how best to secure an exclusive distribution arrangement with a major supplier. To use simulated interaction, an administrator prepares a description of the target situation, describes the main protagonists’ roles, and provides a list of possible decisions. Role players adopt a role and read about the situation. They then improvise realistic interactions with the other role players until they reach a decision; for example to sign a trial one-year exclusive distribution agreement. The role players’ decisions are used to make the forecast. Forecasts from simulated interactions were substantially more accurate than can be obtained from unaided judgement. Simulated interaction can also help to maintain secrecy. Information on simulated interaction is available from conflictforecasting.com.
Intentions and expectations surveys METHOD.With intentions surveys, people are asked how they intend to behave in specified situations. In a similar manner, an expectations survey asks people how they expect to behave. Expectations differ from intentions because people realize that unintended things happen. For example, if you were asked whether you intended to visit the dentist in the next six months you might say no. However, you realize that a problem might arise that would necessitate such a visit, so your expectations would be that the event had a probability greater than zero.
Expectations and intentions can be obtained using probability scales . The scale should have descriptions such as 0 = ‘No chance, or almost no chance (1 in 100)’ to 10 = ‘Certain, or practically certain (99 in 100)’. To forecast demand using a survey of potential consumers, the administrator should prepare an accurate and comprehensive description of the product and conditions of sale. He should select a representative sample of the population of interest and develop questions to elicit expectations from respondents. Bias in responses should be assessed if possible and the data adjusted accordingly. The behaviour of the population is forecast by aggregating the survey responses.
Conjoint analysis METHOD.
By surveying consumers about their preferences for alternative product designs in a structured way, it is possible to infer how different features will influence demand. Potential customers might be presented with a series of perhaps 20 pairs of offerings. For example, various features of a personal digital assistant such as price, weight, battery life, screen clarity and memory could be varied substantially such that the features do not correlate with one another. The potential customer is thus forced to make trade-offs among various features by choosing one of each pair of offerings in a way that is representative of how they would choose in the marketplace. The resulting data can be analysed by regressing respondents’ choices against the product features. The method is based on sound principles, such as using experimental design and soliciting independent intentions from a sample of potential customers. Unfortunately however, there do not appear to be studies that compare conjoint-analysis forecasts with forecasts from other reasonable methods.
Methods requiring quantitative data
Extrapolation METHOD
Extrapolation methods use historical data on that which one wishes to forecast. Exponential smoothing is the most popular and cost effective of the statistical extrapolation methods. It implements the principle that recent data should be weighted more heavily and ‘smoothes’ out cyclical fluctuations to forecast the trend. To use exponential smoothing to extrapolate, the administrator should first clean and deseasonalise the data, and select reasonable smoothing factors. The administrator then calculates an average and trend from the data and uses these to derive a forecast Statistical extrapolations are cost effective when forecasts are needed for each of hundreds of inventory items. They are also useful where forecasters are biased or ignorant of the situation . Allow for seasonality when using quarterly, monthly, or daily data. Most firms do this . Seasonality adjustments led to substantial gains in accuracy in the large-scale study of time series .
Quantitative analogies METHOD.
Experts can identify situations that are analogous to a given situation. These can be used to extrapolate the outcome of a target situation. For example, to assess the loss in sales when the patent protection for a drug is removed, one might examine the historical pattern of sales for analogous drugs. To forecast using quantitative analogies, ask experts to identify situations that are analogous to the target situation and for which data are available. If the analogous data provides information about the future of the target situation, such as per capita ticket sales for a play that is touring from city to city, forecast by calculating averages. If not, construct one model using target situation data and another using analogous data. Combine the parameters of the models, and forecast with the combined model.
Rule-based forecasting METHODS
Rule-based forecasting (RBF) is a type of expert system that allows one to integrate managers’ knowledge about the domain with time-series data in a structured and inexpensive way. For example, in many cases a useful guideline is that trends should be extrapolated only when they agree with managers’ prior expectations. When the causal forces are contrary to the trend in the historical series, forecast errors tend to be large . Although such problems occur only in a small percentage of cases, their effects are serious. To apply RBF, one must first identify features of the series using statistical analysis, inspection, and domain knowledge (including causal forces). The rules are then used to adjust data, and to estimate short- and long-range models. RBF forecasts are a blend of the short- and long-range model forecasts. RBF is most useful when substantive domain knowledge is available, patterns are discernable in the series, trends are strong, and forecasts are needed for long horizons. Under such conditions, errors for rule-based forecasts are substantially less than those for combined forecasts . In cases where the conditions were not met, forecast accuracy is not harmed.
Neural nets METHODS
Neural networks are computer intensive methods that use decision processes analogous to those of the human brain. Like the brain, they have the capability of learning as patterns change and updating their parameter estimates. However, much data is needed in order to estimate neural network models and to reduce the risk of over-fitting the data .There is some evidence that neural network models can produce forecasts that are more accurate than those from other methods . While this is encouraging, our current advice is to avoid neural networks because the method ignores prior knowledge and because the results are difficult to understand.
Data mining METHODS
Data mining uses sophisticated statistical analyses to identify relationships. It is a popular approach. Data mining ignores theory and prior knowledge in a search for patterns. Despite ambitious claims and much research effort, we are not aware of evidence that data mining techniques provide benefits for forecasting.
Causal models METHODS.
Causal models are based on prior knowledge and theory. Time-series regression and cross-sectional regression are commonly used for estimating model parameters or coefficients. These models allow one to examine the effects of marketing activity, such as a change in price, as well as key aspects of the market, thus providing information for contingency planning. To develop causal models, one needs to select causal variables by using theory and prior knowledge. The key is to identify important variables, the direction of their effects, and any constraints. One should aim for a relatively simple model and use all available data to estimate it . Surprisingly, sophisticated statistical procedures have not led to more accurate forecasts. In fact, crude estimates are often sufficient to provide accurate forecasts when using cross-sectional data .
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General principles •
Managers’ domain knowledge should be incorporated into forecasting methods.
• When making forecasts in highly uncertain situations, be conservative. For example, the trend should be dampened over the forecast horizon.
• Complex methods have not proven to be more accurate than relatively simple methods. Given their added cost and the reduced understanding among users, highly complex procedures cannot be justified.
• When possible, forecasting methods should use data on actual behaviour, rather than judgments or intentions, to predict behaviour.
• Methods that integrate judgmental and statistical data and procedures (e.g., rule-based forecasting) can improve forecast accuracy in many situations.
• Overconfidence occurs with quantitative and judgmental methods.
• When making forecasts in situations with high uncertainty, use more than one method and combine the forecasts, generally using simple averages.
Methods based on judgment
• When using judgment, rely on structured procedures such as Delphi, simulated interaction, structured analogies, and conjoint analysis.
• Simulated interaction is useful to predict the decisions in conflict situations, such as in negotiations. • In addition to seeking good feedback, forecasters should explicitly list all the things that might be wrong about their forecast. This will produce better calibrated prediction intervals.
Methods based on statistical data
• With the proliferation of data, causal models play an increasingly important role in forecasting market size, market share, and sales.
• Methods should be developed primarily on the basis of theory, not data.
Finally, efforts should be made to ensure forecasts are free of political considerations in a firm. To help with this, emphasis should be on gaining agreement about the forecasting methods. Also, for important forecasts, decisions on their use should be made before the forecasts are provided. Scenarios are helpful in guiding this process.
Saturday, April 12, 2008
Kickstart installation of Linux
Using kickstart, a system administrator can create a single file containing the answers to all the questions that would normally be asked during a typical Red Hat Linux installation.
Steps
1. Creating the Kickstart File :The kickstart file is a simple text file, containing a list of items.
(a) Command Section:
- autostep(optional)
- auth or authconfig (required)
- bootloader (required)
- clearpart (optional)
- device (optional)
- deviceprobe (optional)
- driverdisk (optional)
- firewall (optional)
- install (optional)
- interactive (optional)
- keyboard (required)
- lang (required)
- langsupport (required)
- lilo (replaced by bootloader)
- lilocheck (optional)
- logvol (optional)
- mouse (required)
- network (optional)
- part or partition (required for installs, ignored for upgrades)
- raid (optional)
- reboot (optional)
- rootpw (required)
- skipx (optional)
- text (optional)
- timezone (required)
- upgrade (optional)
- xconfig (optional)
- volgroup (optional)
- zerombr (optional)
- %include
(b) Package Selection
(c) Pre-installation Script:You can add commands to run on the system immediately after the ks.cfg has been parsed. This section must be at the end of the kickstart file (after the commands) and must start with the %pre command.
(d) Post-installation Script :This section must be at the end of the kickstart file and must start with the %post command.
2. Making the Kickstart File Available
A kickstart file must be placed in one of the following locations:
On a boot diskette
On a boot CD-ROM
On a network
3. Make the installation tree available. An installation tree is a copy of the binary Red Hat Linux CD-ROMs with the same directory structure. If you are performing a CD-based installation, insert the Red Hat Linux CD-ROM #1 into the computer before starting the kickstart installation.
If you are performing a hard-drive installation, make sure the ISO images of the binary Red Hat Linux CD-ROMs are on a hard drive in the computer.
If you are performing a network-based (NFS, FTP, or HTTP) installation, you must make the installation tree available over the network.
4. Starting a Kickstart Installation: To begin a kickstart installation, you must boot the system from a Red Hat Linux from following
(a) Boot diskette
(b) CD-ROM #1 and Diskette
(c) With Driver Disk
Kickstart Configurator
Kickstart Configurator allows you to create a kickstart file using a graphical user interface, so that you do not have to remember the correct syntax of the file.
To use Kickstart Configurator, you must be running the X Window System. To start Kickstart Configurator, select the Main Menu Button (on the Panel) => System Tools => Kickstart, or type the command /usr/sbin/redhat-config-kickstart.
Thursday, April 3, 2008
Samba server
Samba server uses TCP/IP protocol that is installed on host server. When correctly configured, it allows that host to interact with a Windows client or server although it a windows file or print server. With SAMBA you can make your linux computer a part of microsoft based network.
Requirements
/etc/samba/smb.conf
Procedure : Install the required packages
#rpm -ivh samba* --force --aid
Now open the configuration file in vi editor
#cd /etc/samba
#vi smb.conf
At line 41 remove semicolon and
host allow=192.168.0.0/24 (allows permission to whole 192.168.0.0 network)
Now Go to end of the configuration file
Copy from lines 265 to 274 (Copy 8yy) and paste below last line. The whole paragraph may be edited as below.
[share1]
comment= ;write the comments
path= /home/bilton ;path of the directory to be shared
public=no
writable=yes
printable=yes
create mask=0765 ;UMask value for the files created
Now restart teh service
#service smb restart
At client side
Install the package samba-client
#rpm -ivh samba-client* --force --aid
#smbmount //192.168.0.1 /home/bilton /sambabilton -O username=michael
password :
(the above line mounts the shared directory /home/bilton on samba server(192.168.0.1) on local system director /sambabilton , the username used for connecting samba server is michael. Then a password is prompted for the particular user michael.
This is the basic configuration i hve written for using samba server. Further any help required is fully encouraged.
Requirements
- samba-common-3.0.10-1.4E.i386.rpm
- samba-3.0.10-1.4E.i386.rpm
- samba-client-3.0.10-1.4E.i386.rpm
/etc/samba/smb.conf
Procedure : Install the required packages
#rpm -ivh samba* --force --aid
Now open the configuration file in vi editor
#cd /etc/samba
#vi smb.conf
At line 41 remove semicolon and
host allow=192.168.0.0/24 (allows permission to whole 192.168.0.0 network)
Now Go to end of the configuration file
Copy from lines 265 to 274 (Copy 8yy) and paste below last line. The whole paragraph may be edited as below.
[share1]
comment= ;write the comments
path= /home/bilton ;path of the directory to be shared
public=no
writable=yes
printable=yes
create mask=0765 ;UMask value for the files created
Now restart teh service
#service smb restart
At client side
Install the package samba-client
#rpm -ivh samba-client* --force --aid
#smbmount //192.168.0.1 /home/bilton /sambabilton -O username=michael
password :
(the above line mounts the shared directory /home/bilton on samba server(192.168.0.1) on local system director /sambabilton , the username used for connecting samba server is michael. Then a password is prompted for the particular user michael.
This is the basic configuration i hve written for using samba server. Further any help required is fully encouraged.
File sharing using NFS in linux
NFS : Sharing of directories/files between linux-linux, linux-unix, unix-linux is achived by NFS.
The NFS environment contains following components
NFS Server :
A system that contains the file resources to be shared with other sytem on the network.
NFS client
A System that mounts the file resources shared over the network and present the file resources as if they are local.
Requirements
1. portmap-4.0-63.i386.rpm
2.nfs-utils-1.0.6-46.i386.rpm
Configuration file : /etc/exports
Daemons :
1. nfsd
2.mountd
3.statd
4.lockd
Procedure :
1. Server side.
#rpm -ivh nfs-utils* --force --aid
#rpm -ivh portmap* --force --aid
2. How to configure nfs server. Open the file /etc/exports in vi editor
#vi /etc/exports
/home/bilton
add the location of the directory/file(for example to share /home/bilton directory add the entry as above)
#service portmap restart
#service nfs restart
Note : Whenever you edit /etc/exports please restart the services.
3. Client side.
#mkdir /nfsbilton
#service portmap restart
#mount 192.168.0.1:/home/bilton /nfsbilton
Here i have mounted shared directory(/home/bilton) which is on nfs server(192.168.0.,1) on my local system dirctory /nfsbilton.
Now you can view the shared directory under /nfsbilton as though the shared directory is residing on your local system.
Note : Although nfs server is not used widely compared to samba server, i still suggest you to know how nfs server works.
In my next blog i will write how to configure samba server.
The NFS environment contains following components
NFS Server :
A system that contains the file resources to be shared with other sytem on the network.
NFS client
A System that mounts the file resources shared over the network and present the file resources as if they are local.
Requirements
1. portmap-4.0-63.i386.rpm
2.nfs-utils-1.0.6-46.i386.rpm
Configuration file : /etc/exports
Daemons :
1. nfsd
2.mountd
3.statd
4.lockd
Procedure :
1. Server side.
#rpm -ivh nfs-utils* --force --aid
#rpm -ivh portmap* --force --aid
2. How to configure nfs server. Open the file /etc/exports in vi editor
#vi /etc/exports
/home/bilton
add the location of the directory/file(for example to share /home/bilton directory add the entry as above)
#service portmap restart
#service nfs restart
Note : Whenever you edit /etc/exports please restart the services.
3. Client side.
#mkdir /nfsbilton
#service portmap restart
#mount 192.168.0.1:/home/bilton /nfsbilton
Here i have mounted shared directory(/home/bilton) which is on nfs server(192.168.0.,1) on my local system dirctory /nfsbilton.
Now you can view the shared directory under /nfsbilton as though the shared directory is residing on your local system.
Note : Although nfs server is not used widely compared to samba server, i still suggest you to know how nfs server works.
In my next blog i will write how to configure samba server.
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