Demand forecasting plays an important role for every business and gives companies an opportunity to prepare for coming shifts in the market. The empirical findings of this study aim to support construction equipment manufacturers, distributors, and suppliers in apprehending the equipment market in more depth and foreseeing market demand to be able to adjust their business strategies and production capacities, allocate resources more efficiently, optimize the level of output and stock and, as a result, reduce associated costs, increase profitability and competitiveness. It is demonstrated that demand for construction equipment is heavily influenced by changes in economic conditions and country-specific economic indicators can serve as reliable input parameters to anticipate fluctuations in the construction equipment market. The Artificial Neural Networks (ANN) forecasting technique has been successfully employed to predict sales of construction equipment four quarters ahead in selected countries (Germany, The United Kingdom, France, Italy, Norway, Russia, Turkey and Saudi Arabia) with country related economic indicators used as an input.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-209044 |
| Date | January 2017 |
| Creators | Ihnatovich, Hanna |
| Publisher | KTH, Nationalekonomi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Examensarbete INDEK |
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