Monday, September 1, 2014

Personal Computers: To be or not to be!

Forecasting is essential in every industry and for firms in the industry to be prepared to adapt to changing trends. The landscape of the tech. industry, one which has been among the fastest changing industries, was largely dominated by personal computers (PCs) for the past two decades. Of late, however, predictions of the demise of the PC industry have become quite common. Many analysts predict that PCs might get obsolete in the next 5 years, if not already. The recent decline in PC sales by 10% has strengthened this argument. The good news is that most of the large players are adaptive and have already begun R&D in tablets that are believed to be the future, which in turn are accelerating the PCs decline.

Declining sales alone do not mean that PCs are on the way out. Changes in consumer preferences and usage also have a role to play. Tablets and smartphones have overtaken the PC in delivering the convenience of browsing the web, checking emails and using a variety of other applications that do not require great processing capacity. When we look around, we can see most of us accessing a smart phone or a tablet to perform tasks, which were earlier reserved for PCs. The entertainment market is completely dominated by these substitutes, as they are able to offer much better customer experience with the help of accelerometer and gyroscope - features not available in a PC. The mobility advantage also gives these products an edge. Advancements in technology which allow consumers to stream entertainment channels on their tablets has further diminished the role PCs played earlier.

While the lay user might say that he does not need PCs anymore, they are eager to know how soon that might be. IDC has given an optimistic view that it will slow down in 5 years but the difference in their previous predictions were quite high compared to the reality which suggests that they might become obsolete sooner.

Thought the advancements in technology have been rapid, I still wonder if these substitutes can replace the PCs for high-performance applications. Even today, the PCs, with all their drawbacks, are the best bet to run reasonably heavy-data applications such as GIS and other programming applications. Do you think the tablets we hold in our hand can perform these tasks? Will the tablets just be limited to perform the simple tasks we use them for or will they have the capacity to perform heavy data tasks?


Diesel cars cost efficient and reliable than Hybrid cars

Diesel cars cost efficient and reliable than Hybrid cars

Even with Tesla Motors tasting success with its Model S luxury electric car by outselling its petrol-powered equivalents since its launch in 2012, the prospects for battery-powered vehicles generally may never shine quite as bright as expected with the adaptation of newly designed diesel powered cars. With all major companies like Mazda, Mercedes, Toyota, Mitsubishi and Audi entering into diesel cars production line, Americans at last will have the opportunity to experience what a really advanced diesel car is like and why Europeans opt for diesels over hybrids, plug-in electrics and even petrol-powered cars. Part of hesitancy stems from a general lack of understanding among American consumers about the benefits of modern clean diesel versus the old (loud and dirty) variety. So what makes diesel cars very attractive that automobile industry forecasts them competing equally with electric hybrids. The truth is that while there is a price premium associated with the initial purchase cost of diesel vehicles, they typically get 30% better gas mileage and flaunt superior torque numbers and reliability ratings. The automotive analysis firm Vincentric estimates that driving a diesel car will save $2,117 in fuel costs over one year assuming annual rate of 15,000 miles and that is not including the lower ownership costs with diesel cars than their conventional counterparts. GM with its model Chevrolet Cruze diesel will be the latest entrant into the race for many of diesel-powered cars.

Diesel car registration is raising by 30% since 2010 and GM forecasts that diesel cars and light trucks is likely to capture 10% of the US automobile market by 2020. GM, like any other automobile company, believes that this is the right time to enter and capture a growing market in diesel cars.  GM will be facing huge competition from hybrid cars. While both, diesel and hybrid cars are economic in terms of fuel efficiency but each has its own merits and demerits. Fuel efficiency has a causal relationship in increasing the demand for the hybrid car registration, which went up by 65% from 2010 to 2013, a similar trend can be expected for diesel cars as they have shown equivalent miles-per-gallon basis as many of the electric vehicles available today.

According to me, GM must take into consideration of 5 key concepts listed in Prater & Whitehead’s article on forecasting, before entering into this huge race on diesel car industry.
1)   Impact of technology: Whether the technology of diesel powered engines is better than petrol powered or hybrid car engines in terms of torque, fuel efficiency? Is there any choice and difference between auto & manual gears?
2)   Social Issues: Where does the ownership cost of diesel-powered engines fall compared to other two models? How easily accessible are diesel gas stations?
3)   Political Issue: What is the government policy on diesel as fuel availability and natural resources to support it? Are there any subsidies promoted by government to adopt cleaner –greener fuel driven cars, and is diesel car is one among them?
4)   Environmental Issues: What its value on air pollution control and how the government policies view them? How much of the population really concerned about driving the cleaner fuel driven cars?
5)   Legal Issue: Is there any legal issue such as patency, safety etc involved with the production of such vehicles?

Accounting all the above concepts, GM should forecast the demand for the diesel-powered cars using Quantitative, Causal, Time Series and Simulation methodologies.

While forecasting about the diesel car’s future, I compared the article on Apple’s failure to keep up the sales forecast for IPhones even with their adequately supported supply chain management. Similar situation may arise with GM, if it just believes in the fuel efficiency stand of the diesel cars and neglect the legal and political issues. The very fact that diesel cars have been a big hit with customers in Europe and Asian countries for a long time but never adopted in US shows there are many other political and legal issues that needs to be factored in. With many big companies, like BMW, Toyota, Mercedes backing up and lobbying the government to promote diesel cars, GM needs to follow the quantitative, qualitative and causal approach for an efficient supply chain management to meet customers demand with adequate tools & resources. The era of diesel cars is not far away and with their big advantage will be that they will come with none of the range anxiety and recharging difficulties to worry about.

This GM venture poses some of the questions such as :
1)   How will GM handle the competition with hybrid vehicles market demands?
2)   How will GM able to keep the supply chain efficient while also keeping their prices low in order to capture the market of price sensitive customers?
3)   How will GM able to cope with impact of technology in near future with better passenger vehicle options?
4)   How will it able to capture the market while also increase their revenues with diesel powered engines?
5)   What are the strategies to reduce the price for diesel powered engines while keeping up with the quality?

My Sources:

Article by Edmund Prater and Kim Whitehead on Forecasting, HBS, Feb 2013.

Forecasting for the Pharmaceutical Industry

Last week, the articles and readings focused on the use of forecasting to meet customer demand in a timely manner. Overestimation can lead to high costs, high inventory and underestimation can lead to a low customer satisfaction which can be bad for a company’s reputation and credibility. Forecasting is done by companies in every segment to ensure that they have just enough supply to meet the customer demand. Global companies use sophisticated softwares while small companies use statistical tools like moving averages and exponential smoothing to include seasonality.  (1)

Pharmaceutical industry is highly regulated throughout the world. They have to comply with WHO standards along with their own local pharmaceutical and manufacturing standards. Additionally, pharmaceutical supply chain forecasts are complicated and unpredictable so managing forecasts is really important in this business. According to a report by Accenture- improved demand forecasting will help reduce inventory by $46B worldwide. Two basic problems faced in this industry are: lack of collaborative practices with downstream customers and poor forecasting quality. These two problems can lead to excessive inventory and missed forecasts as sales and operational planning are affected. (2) Transporting medicine is also a complicated process. There are strict requirements for each product with regards to their temperature control and the vehicles in which they can be transported. Routes need to be mapped in advance to take weather and bad roads into account. Therefore, poor forecasts can add additional transportation costs as well.

Every link in the supply chain depends on forecasts and therefore it is a vital process. A cardinal sin in this industry is to run out of stock- which can lead to replacement by another supplier. Improving a single step in Supply Chain Management-Forecasting can really lead to profitable outcomes with increased customers. This can be better explained with an example: Cipla Medpro is South Africa’s fastest growing pharmaceutical company and among the top key market leaders. When the company started, they had limited products and it was easier for them to forecast using excel spreadsheets. As they scaled up, it became increasingly difficult for them to have accurate forecasts. They therefore implemented a software, which helped them better manage their inventory and meet customer expectations along with increasing operational efficiency. (3)

What’s interesting to find is how small companies and start-ups determine their inventory levels and be at par with industry benchmarks as they don’t have enough capital for software implementation. Would they still use excel spreadsheets and forecast or are there any other options available they can use for accurate forecasts which are cost efficient?

1.       Importance of Forecasting in supply chain management –
3.       Improve your supply chain forecasting-

McDonalds - Demand Forecasting and Supply Chain Management

In this age of globalization, corporations are trying to achieve more “bang for the buck” from their global supply chains. Organizations are using demand driven forecasting techniques in place of previous predictive methods. Demand driven forecasting allows companies to manufacture the right kind of products in appropriate volumes to satisfy consumer demand while best utilizing their resources. [4] Forecasting methods use previous consumer data to predict the demand for a particular product taking into consideration the various social and seasonal factors. This reduces variability, overhead costs and allows efficient inventory management.

McDonald’s is amongst the top 10 Supply Chain systems in the world. It is one of the most popular fast food restaurants and receives instant recognition in almost all countries of the world. It has over 30,000 restaurants in 119 countries and serves around 50 million people every day. McDonalds has customized its products according to different countries and their respective taste preferences. The food options available in a McDonald’s restaurant in India is very diverse to the one available in US. 100% of McDonald’s supply chain is outsourced and the company does not have any factories or manufacturing plants. [2] 

One of the major challenges faced by McDonalds is stock management and reducing waste. This is accurately done by
  • Forecasting demand
  • Accurately stocking raw materials

In the earlier business model of McDonald’s each store ordered its own raw materials based on local knowledge and previous customer data. One of the major drawbacks to this approach was that it did not incorporate school holidays, national promotional schemes or seasonal trends. In 2004, McDonald’s implemented a central stock management department known as “Restaurant Supply Planning Department”. Continuous communication between individual restaurants and the central restaurant supply planning team helps to manage stock efficiently and meet the forecasted demand. Forecasting is done based upon:
  •  Store specific product data
  • National causal factors like school holidays or national promotions.
  • Local information from store managers. For example local holidays, promotions or seasonal trends.
  • Weather information
Various causal factors are included in the calculation of forecasts so that they can accurately predict the sales at each store and maintain the inventory accordingly. Promotional campaigns and local adaptations of individual franchises are also factored in while forecasting sales. Including basic weather information of a particular region also increases the accuracy of prediction. For example, McFlurrys and ice creams are sold more in the summer as compared to the winter. Also, the sales for these products are higher in regions like India which are warmer as compared to the US. [2]

McDonald’s achieves its KPI “No item may ever be out of stock” by leveraging several supply chain principles. It uses a forecasting application called JDA Manguistics 7 which uses point of sale data, stock levels at individual restaurants, inventory and shipments and the product list as input to predict sales.[1] McDonald’s also applies customer analytics to better predict the sales. It segregates the customers into categories like end customers and owner operators (franchises). Mystery shops, 800 calls and other programs are implemented to analyze how well a particular store is doing from a customer perspective. This in turn helps to predict the sales at that particular store and improve customer relationship. [5]

Despite having numerous advantages, demand driven forecasting burdens the global suppliers to meet exact demands. [4] If the corporation’s global suppliers are not able to deliver on time owing to transportation delays, political instability or other custom issues then the organization faces the risk of not fulfilling its promise to its customers. Driving the global supply chains based on demand forecasting reduces operational costs but also increases the risk of total breakdown. Hence, the question arises as to what is optimal balance between risk and reward of demand forecasting?


Forecasting in the Vaccine Production Industry

            The readings this week shed light on the importance of forecasting in accurately meeting market demand in a timely fashion. Most industries produce goods year round and forecast demand for these goods at various different times of year. However, vaccine production is one industry in which the good is only available seasonally and manufacturers work year round to efficiently and effectively deliver these products.
            Flu vaccination production begins eight months before the vaccine is released for public use due to a complicated manufacturing procedure involving breeding the WHO recommended strains of virus to create antigens that can be isolated and packaged for consumer use [1].  Vaccinations for the flu often see a spike in demand during the typical flu seasons, and therefore the World Health Organization can work with pharmaceutical companies to create vaccines in a timely fashion.
            However, in some cases such as with H1N1 in 2010, widespread patient panic can heavily increase the demand for vaccines that may have been forecast differently. For this reason, companies must ensure that every vaccine manufactured is made with enough supply to meet the highest potential customer demand or at least have enough substitutes that patients can be treated [2]. According to the CDC, roughly 135-139 million doses of vaccine were produced for the 2013-2014 flu season, which is less than half of the current US population [3]. 
            Although the demand for vaccinations is highest annually in October and November, production continues until January [3]. The World Health Organization reports that despite vaccine forecasting being a largely consistent process, large variability can occur due to human error such as improper storage and inefficient stock management [4]. Vaccines that are not used in a current flu season must be disposed and destroyed, as they will no longer be effective in defending against the next season’s strain of the virus. In the event of the 2010 H1N1 vaccine, this meant almost 30% of all vaccines produced had to be destroyed by the manufacturers [1].
            I found the process of destroying unused vaccines to be a very interesting ethical consideration, when comparing this surplus with the shortage of vaccines in third world countries around the world. Considering that the same vaccine recommendations are made by the WHO in a global capacity, I wonder why excess (unexpired vaccines) can’t be sent to other countries as demand in the USA tapers off. Putting aside distribution cost considerations, this seems like a much more useful way to get rid of surplus vaccines. However, further research suggested that this would push funding away from vaccine delivery for more lethal diseases such as measles[5]. In light of this constraint, I put forth two questions:

When considering forecasting vaccines and the potential savings if the eventually discarded doses were never even created, is there a more accurate way to predict for market goers that ethically choose to not have their families vaccinated or simply skip the vaccine for other reasons (over 50% of the current population)?

For a company like GlaxoSmithKline(GSK), one of the leaders in vaccine production, are they ethically obligated to create a large surplus in the event that everyone may choose to get a vaccine this season or should they be allowed to stick with business sensibility and only supply what is predicted through time series forecasting?

Interest Rates and Supply Chain Management

Interest Rates and Supply Chain Management

To optimize the practice of forecasting, firms must acknowledge the potential for economic or political issues to affect the supply chain process. Generally, any business involved in filling orders for goods and services operates as part of a supply chain. Each link on the supply chain must carry out a specific task in order to deliver products to customers. Because it is consumer oriented, supply chain management is inherently linked to aggregate demand. Changes in economic conditions that create fluctuations in aggregate demand will have a direct impact on supply chain management tasks. For example, if the government increases taxes, there will be a corresponding decrease in net income and aggregate demand, resulting in less production from supply chains. As such, to me it seems that conditions that increase aggregate demand, such as price drops and lower interest rates, should be considered when creating a successful supply-chain forecast model.

Recently, changes in the market for loanable funds market have created uncertainty in the future of aggregate demand. The Federal Reserve has begun putting in motion methods to scale back its Quantitative Easing strategy in response to improvements in the economy. This implies an increase in the federal funds rate, and subsequent rise in interest rates. Rising interest rates imply a fall in net investment and aggregate consumption. Looking forward, from the perspective of supply chain management, it will be relevant to take changing interest rates into account when building forecast models.

In isolation, a forecast model predicting future interest rates can be made using a simple regression model. Specifically, the interest rate of 3-month Treasury Bills (R) can be predicted as a function of the index of industrial production (IP), the rate of growth on the money supply (GM2t = (M2t – M2 t-1/ M2t-1)), and the lagged rate of wholesale price inflation (GPWt = (PWt – PWt-1/ PW t-1)). A multiple regression model can be generated based on these parameters:
Rt = a + b1IP + b2M2t + b3GPWt-1 + e1. Specifically, over the past three months interest rates have wavered between .01 and .04 percent, and forecasts seem to imply that this trend will continue:

More generally, the predicted interest rate can be incorporated in a multiple linear regression forecasting sales of a given product/aggregate demand. However, in order for such a model to prove successful, it must be constantly updated with recent data, as interest rates are particularly prone to sudden changes at any given time. This will help facilitate a continual improvement of the forecasting processes by allowing for adjustment in temporary forecast errors.

In order to conduct a successful forecast and/or causal hypothesis, it is necessary to rely both on forecast models and human intuition. A successful forecaster will both detect a trend and speculate as to why it is happening. However, the future economic climate is heavily dependent on current expectations and media projections. So, I wonder how a supply chain manager can make a successful forecast decision considering several future signals that may contradict each other (i.e. how do they separate the “signal from the noise”). Additionally, multicollinearity is a common problem that forecasters who rely on multiple linear regression analysis seem to run into. How can successful forecasters determine what specific parameters to include in a forecast while avoiding redundancy or including variables that are irrelevant?

Pindyck, Robert, and Daniel Rubinfeld. Econometric Models and Economic Forecasts. Irwin/McGraw-Hill. 1998.
Silver, Nate. The Signal and the Noise: Why so Many Predictions Fail-But some don’t. The Penguin Press. New York. 2012.