SMU MBA ASSIGNMENTS

Answer:  The six—step approach that helps an organization perform effective forecasting are follows as:

Rule 1: Define a Cone of Uncertainty: As a decision maker, you ultimately have to rely on your intuition and judgment. There’s no getting around that in a world of uncertainty. But effective forecasting provides essential context that informs your intuition. 

It broadens your understanding by revealing overlooked possibilities and exposing unexamined assumptions regarding hoped-for outcomes. At the same time, it narrows the decision space within which you must exercise your intuition I visualize this process as mapping a cone of uncertainty, a tool I use to delineate possibilities that extend out from a particular moment or event. The forecaster’s job is codeine the cone in a manner that helps the decision maker exercise strategic judgment. Many factors go into delineating the cone of uncertainty, but the most important is defining its breadth, which is a measure of overall uncertainty. Other factors–relationships among elements, for example, and the ranking of possible outcomes–must also be considered in developing a forecast, but determining the cone’s breadth is the crucial first step. Imagine it is 1997, the Toyota Prius has just gone on sale in Japan, and you are forecasting the future of the market for hybrid cars in the United States. External factors to consider would be oil price trends and consumer attitudes regarding the environment, as well as more general factors such as economic trends. Inside the cone would be factors such as the possible emergence of competing technologies (for instance, fuel cells) and an increased consumer preference for small cars (such as the Mini). At the edge of the cone would be wild cards like a terrorist attack or a war in the Middle East. These are just a very few representative examples. (See the exhibit “Mapping the Cone of Uncertainty” for more on the process.)

 

 

Mapping the Cone of Uncertainty

A cone of uncertainty delineates the possibilities that extend out from a particular moment or event. The most important factor in mapping a cone is defining its breadth, which is a measure of overall uncertainty. In other words, the forecaster determines what range of events or products the cone should encompass. Drawing the cone is a dynamic process, and what we see here is just one iteration. Let’s take the case of robot products, a minicraze that has been emerging and subsiding since the mid-1980s. The events before 2007 indicate that activity in this area is building, and it seems only a matter of time before this industry takes off, in the same way PCs took off in the mid-1980s and the Web took off in the mid-1990s.In drawing this cone, my first step was to note the distinction between appliance-centric robots and entertainment-centric robots, represented by the dotted line across the middle of the cone. The closer to the dotted line a particular product or event is, the more it has in common with the category on the opposite side of the line. The DARPA Grand Challenges, which may end up as the indicators of robotic highway vehicles, are military projects and are thus located far from the dotted line in the middle of the cone.

Rule 2: Look for the S Curve

Change rarely unfolds in a straight line. The most important developments typically follow the S-curve shape of a power law: Change starts slowly and incrementally, putters along quietly, and then suddenly explodes, eventually tapering off and even dropping back down.

The mother of all S curves of the past 50years is the curve of Moore’s Law, the name given to Gordon Moore’s brilliant 1965 conjecture that the density of circuits on a silicon wafer doubles every 18 months. We can all feel the consequences of Moore’s Law in the extravagant surprises served up by the digital revolution swirling around us. Of course, the curve of Moore’s Law is still unfolding–it is still a “J”–with the top of the “S” nowhere in sight. But it wills flatten eventually, certainly with regard to silicon circuit density. Even here, though, engineers are sure to substitute denser circuit-carrying materials (like nano scale and biological materials) as each successive material reaches saturation, so the broadest form of the Moore’s Law curve (density regardless of the material) will keep climbing for some time to come. This distinction reveals another important feature of Scurves, which is that they are fractal in nature. Very large, broadly defined curves are composed of small, precisely defined and linked S curves. For a forecaster, the discovery of an emergent S curve should lead you to suspect a larger, more important curve lurking in the background. Miss the larger curve and your strategy may amount to standing on a whale, fishing for minnows.

Rule 3: Embrace the Things That Don’t fit

The novelist William Gibson once observed:

“The future’s already arrived. It’s just not evenly distributed yet.” The leading-edge line of an emerging S curve is like a string hanging down from the future, and the odd event you can’t get out of your mind could be a weak signal of a distant industry-disrupting S curve just starting to gain momentum. The entire portion of the S curve to the left of the infection point is paved with indicators–subtle pointers that when aggregated become powerful hints of things to come. The best way for forecasters to spot an emerging S curve is to become attuned to things that don’t fit, things people can’t classify or will even reject. Because of our dislike of uncertainty and our preoccupation with the present, we tend to ignore indicators that don’t fit into familiar boxes. But by definition anything that is truly new won’t fit into a category that already exists. A classic example is the first sales of characters and in-game objects from the online game Ever Quest on eBay in the late 1990s. Though eBay banned these sales in 2001, they anticipated the recent explosive growth of commerce in Second Life, Linden Lab’s virtual world in which members create 3-Davatars (digital alter egos). Through the avatars, members engage in social activities, including the creation and sale of in-world objects in a currency (Linden dollars) that can be exchanged for real dollars through various means. Today there are approximately 12 million subscribers participating in virtual world simulations like Second Life, and they’re having an impact measurable in actual dollars. Real transactions connected with Second Life and other online simulations now are (conservatively) estimated at more than $1 billion annually. Where it ends is still uncertain, but it is unquestionably a very large S curve. More often.

Rule 4: Hold Strong Opinions  Weakly

One of the biggest mistakes a forecaster–or a decision maker–can make is to over rely on one piece of seemingly strong information because it happens to reinforce the conclusion he or she has already reached. This lesson was tragically underscored when nine U.S. destroyers ran aground on the shores of central California on the fog-shrouded evening of September 8, 1923. The lost ships were part of DesRon 11, a 14- ship squadron steaming from San Francisco to San Diego. Misled largely by overreliance on the commander’s dead-reckoning navigation, the squadron undershot the turn into the Santa Barbara Channel and instead ended up on the rocks at Point Pedernales, several miles to the northwest The squadron had navigated by dead reckoning for most of the trip, but as the ships approached the channel, the squadron’s commander obtained bearings from a radio direction station at Point Arguello. The bearing placed his ship, the Delphy, north of its dead reckoning position. Convinced that his dead reckoning was accurate, the commander reinterpreted the bearing data in a way that confirmed his erroneous position and ordered a sharp course change towards the rapidly approaching coast.

Rule 5: Look Back Twice as Far as You Look Forward

Marshall McLuhan once observed that too often people steer their way into the future while staring into the rearview mirror because the past is so much more comforting than the present. McLuhan was right, but used properly, our historical rearview mirror is an extraordinarily powerful forecasting tool. The texture of past events can be used to connect the dots of present indicators and thus reliably map the future’s trajectory– provided one looks back far enough. Consider the uncertainty generated by the post-bubble swirl of the Web, as incumbents like Google and Yahoo, emergent players, and declining traditional TV and print media players jockey for position. It all seems to defy categorization, much less prediction, until one looks back five decades to the emergence in the early 1950s of TV and the subsequent mass-media order it helped catalyze. The present moment has eerie parallels to that era, and inspection of those similarities quickly brings today’s landscape into sharp focus: We are in a moment when the old mass-media order is being replaced by a new personal-media order, and it’s not just the traditional media players that are struggling to adjust. The cutting-edge players of the information revolution, from Microsoft to Google, are pedaling every bit as hard. The problem with history is that our love of certainty and continuity often causes us to draw the wrong conclusions. The recent past is rarely a reliable indicator of the future–if it were, one could successfully predict the next 12 months of the Dow or Nasdaq by laying a ruler along the past 12 months and extending the line forward. But the Dow doesn’t behave that way, and neither does any other trend. Y

Rule 6: Know When Not to Make a Forecast

It is a peculiar human quality that we are at once fearful of–and fascinated by–change. It is embedded in our social vocabulary, as we often greet a friend with the simple salutation, “What’s new?” Yet it is a liability for forecasters to have too strong a proclivity to see change, for the simple fact is that even in periods of dramatic, rapid transformation, there are vastly more elements that do not change thannew things that emerge. Consider again that whirling vortex of the 1990s, the dot-com bubble. Plenty new was happening, but underlying the revolution were deep, unchanging consumer desires and ultimately, to the sorrow of many a start-up, unchanging laws of economics. By focusing on the novelties, many missed the fact that consumers were using their new broadband links to buy very traditional items like books and engage in old human activities like gossip, entertainment, and pornography. And though the future-lookers pronounced it to be a time when the old rules no longer applied, the old economic imperatives applied with a vengeance and the dot-com bubble burst just like every other bubble before it. Anyone who had taken the time to examine the history of economic bubbles would have seen it coming.

 

Q2. How CPFR model helps guiding Supply Chain partners in setting up their relationship and processes. 10 marks (300-400) words