Assignment 1 Operations Management Topic = Walmart Submitted to = Harmeet Kohli Submitted by = Ravinder Kumar (991462522) Sultan Singh (991457744)   Elements of demand forecasting Three elements of demand forecasting are as follow: 1. Data = Demand Forecasting is additionally in view of the past and past information. It is useful in creating measurable figure for old and new items. Walmart is said to be extraordinary compared to other association who read their recorded information in a best way and take after the future request as per it. 2. Suppliers = Suppliers plays a vital role in demand forecasting for Walmart. For example – Walmart not make its own products. It purchases products from makers and they supply direct to their stores. So, for those products manufactures are responsible to make it available at their stores on particular time. 3. Exact information = Demand forecasting of Walmart is extremely precise, this is motivation behind why it is best production network store in entire world. In the event that they appraises their request over, at that point they could endure the expansion in expenses of work and capacity. In the event that they assesses their request low, at that point their notoriety would be influenced and they couldn’t meet the client needs. Moreover, in the event that they manages low quality products and wastage of a few merchandise, their net revenue would be influenced. Difference between Fundamental and technical forecasting Fundamental forecasting = Investigation endeavoring to foresee future outcomes that includes looking at connections of trade rates and monetary factors. Essential estimating utilizes subjective and quantitative angles which may influence rates of trade. This incorporates political variables and macroeconomic information (Singh, 2014) Technical forecasting = Specialized anticipating intends to utilize the past information to shape applications and models which can be utilized to make expectations for estimating. Authentic information or moving examples are included while shaping forecasts in this technique. (Bass, 2015) The main distinction among both of these forecasting’s is how to gather data and how it is examined for the forecast. For instance, Technical forecasting generally depends upon past information and details to make an expectation while fundamental forecasting relies on the subjective and quantitative terms even it can be from present as well. Seven Steps of Forecasting 1. Determine the use of forecast = The first step is to determine the use of the forecast. What will it be used for and or who needs the forecast. In the Walmart Company, they forecasting system would be used for the production floor, to be able to show what good will be sell and when. It would be used to meet customer demands. 2. Select the items to be forecasted = Keep in mind that figures are made so as to get ready for what’s to come. To do as such, we need to choose what figures are really required. This isn’t as straightforward as it sounds. For instance, Walmart is a superstore; subsequently it’s anything but difficult to state the things on the conjecture are the distinctive kinds of day by day required great. They could likewise go more in detail and select the distinctive division and areas be chosen to be determined. 3. Determine the time horizon for the forecast = The third step of the forecasting is the time horizon. It means that what time it will take to forecast each product and service. It is very essential part of the forecasting because it will affect the forecasting. So it is very important to know that what time will be taken for the forecasting. 4. Select the forecasting methods = Once the information have been assessed, the following stage is to choose a fitting estimating model. As we will see, there are two models to browse. Anticipating techniques are regularly either qualitative or quantitative. At the end of the day, producing directors may perform determining exercises in light of their own judgment and experience, or they may make utilization of estimating procedures in view of factual information. Walmart would utilize, quantitative determining which would be founded on history and detail which will indicate precise insights and courses of events. 5. Gather the data = Stage five is the social affair of the information to make the estimate. What kind of authentic information and insights is there that can be contribution to the figure, what part numbers are there, are their courses of events this information should be observed and deciphered to have the capacity to concoct a nitty gritty estimate. 6. Make the forecast = the subsequent stage six is really the formation of the forecast. This progression represents itself with no issue; this is the place the estimate is made. What programming will be utilized, what sort of graphs and additionally tables will be utilized. 7. Validate and implement result = The last advance, seven, is the place the information is approved and after that executed into the framework. This will be a progressing venture as approving the new forecasting will set aside some opportunity to check whether it is working, it should be looked into to make sure the data is right. (megha08, 2011) Time series forecasting Time series forecasting is of 4 types 1. Trend = Trend variation is either increasing or decreasing. Walmart request and deals for the most part relies on innovation, population, age and culture and so on. It can be estimated by thinking about quite a while in contact. 2. Cyclical = This segment relies on business cycle for forecasting. The forecast for the items or administration’s request continued fluctuating relied on financial or political elements. It can be determined from numerous years. 3. Seasonal = Demand of some products increases or decreases according to the seasons so Walmart sell those product which are highly demanded in that season because of this reason its sales increase or decrease in that particular season. 4. Random = This is the part which happens due to unexpected events. For Walmart it will be like any late supply of a product for supplier. It is the most neglected part. Calculations WALMART NET SALES WORLDWIDE FROM 2014 TO 2017 (in US dollars) Step 1 For 2014 January – March sales = 100 April-June sales=118 July- September sales = 112 October- December=143.08 Historical demand of this year =118.27 For 2015 January – March sales=120 April-June sales=100 July- September sales =120 October- December=142.23 Historical demand of this year=120.56 For 2016 January – March sales=110 April-June sales=100 July- September sales=148.61 October- December=120 Historical demand for this year=119.65 For 2017 January – March sales=120 April-June sales=130 July- September sales=130 October- December=101.32 Historical demand for this year=120.33 Average demand of all seasons=473.08+482.233+478.61+481.32/4 =1915.24/4 =478.81 Seasonal index Seasonal Index=actual demand/average demand For 2014 Quater1 100/118.27=0.85 Quater2 118/118.27=0.99 Quarter3 112/118.27=0.94 Quater4 143.08/118.27=1.21 For 2015 Quater1 120/120.56=0.99 Quater2 100/120.56=0.83 Quarter3 120/120.56=0.99 Quater4 142.23/120.56=1.18 For 2016 Quarter 1 110/119.65=0.92 Quater2 100/119.65=0.85 Quarter3 148.61/119.65=1.24 Quater4 120/119.65=1 For 2017 Quarter 1 130/120.33=1.08 Quater2 120/120.33=0.99 Quarter3 130/120.33=1.08 Quater4 101.32/120.33=0.84 Months 2015 2016 2017 Average historical Average monthly Seasonal index For next year January 38 40 39 39 39.9 0.977 38.98 February 37 39 41 39 39.9 0.977 38.98 March 39 42 38 39.6 39.9 0.992 39.58 April 42 41 42 41.6 39.9 1.042 41.57 May 40 37 40 39 39.9 0.977 38.98 June 43 40 39 40.6 39.9 1.017 40.57 July 43 40 41 41.3 39.9 1.035 41.29 August 40 42 40 40.6 39.9 1.017 40.57 September 38 38 39 38.3 39.9 0.959 38.26 October 39 41 37 39 39.9 0.977 38.98 November 42 40 42 41.3 39.9 1.035 41.29 December 41 38 43 40.6 39.9 1.017 40.57 (statista, n.d.) Total demand for next year will be $479.62 U.S billion dollars. E. For October season = 479.62/12*0.977 =39.04 For November season =479.62/12* 1.035 = 41.36 For December season = 479.62/12*1.017 = 40.64   References Bass, E. (2015). Foreign Exchange Derivative Market. Retrieved from http://slideplayer.com: http://slideplayer.com/slide/2817532/\ megha08. (2011, august 21). step of forecasting. Retrieved from slideshare.net: https://www.slideshare.net/arunkumarkgr1/class-notes-forecasting Singh, J. (2014, June 21). Forecasting exchange rates. Retrieved from slideshare.ne: https://www.slideshare.net/Jaswindersingh18/forecasting-exchange-rates statista. (n.d.). Walmart’s net sales worldwide from 2006 to 2017 (in billion U.S. dollars). Retrieved from statista.com: https://www.statista.com/statistics/183399/walmarts-net-sales-worldwide-since-2006/
