Improving demand forecasting practices in the industrial context

Demand forecasting is one of the fundamental managerial tasks. Most companies do not know their future demands, so they have to make plans based on demand forecasts. The literature offers many methods and approaches for producing forecasts. When selecting the forecasting approach, companies need to estimate the benefits provided by particular methods, as well as the resources that applying the methods call for. Former literature points out that even though many forecasting methods are available, selecting a suitable approach and implementing and managing it is a complex cross-functional matter. However, research that focuses on the managerial side of forecasting is relatively rare. This thesis explores the managerial problems that are involved when demand forecasting methods are applied in a context where a company produces products for other manufacturing companies. Industrial companies have some characteristics that differ from consumer companies, e.g. typically a lower number of customers and closer relationships with customers than in consumer companies. The research questions of this thesis are: 1. What kind of challenges are there in organizing an adequate forecasting process in the industrial context? 2. What kind of tools of analysis can be utilized to support the improvement of the forecasting process? The main methodological approach in this study is design science, where the main objective is to develop tentative solutions to real-life problems. The research data has been collected from two organizations. Managerial problems in organizing demand forecasting can be found in four interlinked areas: 1. defining the operational environment for forecasting, 2. defining the forecasting methods, 3. defining the organizational responsibilities, and 4. defining the forecasting performance measurement process. In all these areas, examples of managerial problems are described, and approaches for mitigating these problems are outlined.

Utilizing online demand generation in an engineering company

Työn tarkoituksena on selvittää miten sähköistä kysynnän herättämistä voidaan hyödyntää Mantsinen Group Ltd Oy:ssä siten, että sillä pystytään tukemaan myyntiä. Lisäksi sähköisen kysynnän herättämisen tehokkuutta tutkitaan, jotta saadaan selville onko se kannattavaa ja kuinka hyvin se sopii yritykselle. Kysynnän herättämisjärjestelmän käyttö on määritelty kirjallisuuteen perustuen ja sen jälkeen järjestelmän käyttö on aloitettu. Sähköisen kysynnän herättämisen tehokkuus mitataan kolmen kuukauden tarkastelujakson todellisella datalla. Sähköisen kysynnän herättämisen sopivuutta arvioidaan perustuen sen kustannustehokkuuteen ja tuloksiin. Työn tulokset osoittavat, että sähköinen kysynnän herättäminen on kannattavaa ja se sopii yritykselle. Sillä voidaan parhaiten tukea myyntiä järjestelmän tuottaessa laadukkaita myyntimahdollisuuksia tasaisena virtana myynnille. Myös aiemmin manuaalisesti tehtyjä työtehtäviä voidaan automatisoida ja näin vähentää myyjien työtaakkaa.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

Seeder-fertilizer machine: energetic demand as a function of vegetal covering handling and manure deposition shank depth in no-tillage system

The purpose of this work was evaluating the energetic demand of a seeder-fertilizer machine as a function of the type and handling of vegetal covering culture and of the fertilizer deposition shank depth. A Valtra BM100 tractor was used implemented to pull a high precision seeder-fertilizer machine with four ranks of seeding, spaced 0.9 m for maize culture. Experiment was conducted with design in randomized blocks in factorial plots, in the Laboratory of Machines and Agricultural Mechanization experimental area (LAMMA) of UNESP-Jaboticabal, using two covering cultures (black-mucuna and crotalaria), three handlings of this covering, two mechanical (straw crusher and roller knife) and one chemical (pulverization of herbicide), performed 120 days after seeding of covering cultures and three depths of fertilizer deposition shank, completing 18 treatments, with four repetitions, totaling 72 observations. Parameters of displacement speed, gliding, force on traction bar, peak force, power on pulling bar and fuel consumption were evaluated. It was possible to conclude that force on traction bar was less for depths of 0.11 and 0.14 m of fertilizer plough shank, the same occurring for peak force, power on traction bar and volumetric consumption. The specific consumption was lower at a depth of 0.17 m of fertilizer plough shank. Covering cultures and their handlings did not interfere in the performance of machines under inquiry.

Effect of time-temperature binomial in obtaining biochemical oxygen demand of different wastewaters

Studies on the effects of temperature and time of incubation of wastewater samples for the estimation of biodegradable organic matter through the biochemical oxygen demand (BOD), that nowadays are rare, considering that the results of the classic study of STREETER & PHELPS(1925) have been accepted as standard. However, there are still questions how could be possible to reduce the incubation time; whether the coefficient of temperature (θ) varies with the temperature and with the type of wastewater and if it approaches 1.047. Aiming the elucidation of these questions, wastewater samples of dairy, swine and sewage treated in septic tanks were incubated at temperatures of 20, 30 and 35 °C, respectively for 5, 3.16 and 2.5 days. From the parameter of deoxygenation coefficient at 20 °C (k20), θ30 and θ35 were calculated. The results indicated that θ values changes with the type of wastewater, however does not vary in the temperature range between 30 and 35 °C, and that the use of 1.047 value did not implied significant differences in obtaining k in a determined T temperature. Thus, it is observed that the value of θ can be used to estimate the required incubation time of the samples at different temperatures.

Energy demand in citrus production under varied operational efficiency values

Citrus orchards are very important in Brazil, especially in São Paulo State, where occupy an area of 600,000 ha approximately. To identify sustainability degree of citrus production system, an energy analysis allows evaluating efficiency of direct and indirect applied inputs. Thus, this study aimed to evaluate citrus production system under energetic point of view, in which invested energy is paid back with citrus production; being compared within three scenarios for operational field efficiency. As result, by sensitivity analysis was determined that fuel was the main energy demander, followed by pesticides and fertilizers. In operational work capacity analysis, all combinations between efficiency (minimum, typical and maximum) and yield levels became positive in the seventh year, except for the combination minimum efficiency and 10 % less yield, positive in the eighth year. The best combination (maximum efficiency and 10 % more yield) has promoted investment payoff around the sixth and seventh year. By this study, it is possible to determine the total energy demand to produce citrus and indentify the applied inputs that need more attention by the decision-makers. Labor and seedlings can be ommited for further studies with citrus, since they were irrelevant. Management of agricultural machinery may pose an important role on decreasing environmental impact of citrus production.

Stochastic variability of output levels in a pulp mill

Quite often, in the construction of a pulp mill involves establishing the size of tanks which will accommodate the material from the various processes in which case estimating the right tank size a priori would be vital. Hence, simulation of the whole production process would be worthwhile. Therefore, there is need to develop mathematical models that would mimic the behavior of the output from the various production units of the pulp mill to work as simulators. Markov chain models, Autoregressive moving average (ARMA) model, Mean reversion models with ensemble interaction together with Markov regime switching models are proposed for that purpose.

Driver of Demand in Cargo and Passenger Traffic Between Penta Ports

Maritime transport moves around 6 billion tonnes of freight every year. The freight consists of liquid bulks (45%), dry bulks (23%) and general cargo (32%). Freight traffic and transports chains vary according to region, commodity and the origin and the destination of freight. In the European Union the ports sector handles over 90% of the trade with third countries. The share of intra-EU trade is approximately 30% of the total transportation and the number of passengers is over 200 million every year. The Baltic Sea has more than 50,000 vessels a year pass the Skaw at the northernmost tip of Denmark on their way into or out of the Baltic. Roughly 60% to 70% of these vessels are cargo vessels and 17% to 25% tankers. Ports and maritime transport play a crucial role in global commerce today. Today’s business environment is changing rapidly, and the constant changes create challenges for the transport industry and maritime traffic. Ports have to adapt to continuous changes in economic structures, logistics demands, and people’s travel and leisure patterns. In order to ensure the competitiveness of sea connections, the ports need to fully enhance multilateral cross-border understanding and cooperation. In this report the focus is on liner traffic between five ports in the Central Baltic Region: Stockholm, Tallinn, Helsinki Turku and Naantali. The report defines the drivers of the demand for cargo and passenger traffic and highlights the most important factors. The economic situation and foreign trade of each county are elaborated on with detailed information about the flows of traffic between the five ports. Based on expert interviews, the main characteristics of each port, including strengths and weaknesses, are presented. The report is based on primary and secondary data. Primary data was received through interviews and mail surveys. Secondary data was attained through a literature research, statistics, data given by the PENTA ports and webpages. The report is divided into two main parts: the drivers creating the demand for transport and the results of current cargo and passenger flows between PENTA ports.

Business on demand and its mobile solution - study with SAP ByDesign

Business On-Demand solutions are widely used by SMEs in the world today. When I started working in SAP, SAP had just launched its first version mobile solutions for Business On-Demand solutions. SAP ByDesign mobile solution is great, but I believe we could do something even better on mobile phones. My job is focusing on mobile application development. Therefore, I have lots of thoughts about how we could make the mobile solutions better serve desktop solutions and how to distinguish the mobile solutions. Finally I decide to have a further research into this area. The purpose of this thesis is trying to find out how to improve the mobile solutions for Business On-Demand, find out its benefit and limits, and distinguish SaaS mobile solutions from desktop ones. In order to conduct this research, I had some online literature search to find out the Business On-Demand market and major players in this area. I compare the materials from public internet with the ones that are used internally in SAP. I had some interviews with SAP solution manager and SAP‟s potential customers. I finally made some pro-posals for mobile SaaS solutions which I believe will make the solutions better present and much helpful to the customers.

The demand for global student talent: Capitalizing on the value of university-industry collaboration

The university sector in Europe has invested money and effort into the internationalization of higher education. The benefits of internationalizing higher education are fuelled by changing global values, choices and practices. However, arguments that serve the internationalization of higher education tend to stress either local organizational or individual interests; seldom do they emphasize the societal benefits. This dissertation investigates how collaboration between university and industry facilitates a shift in thinking about attracting and retaining global student talent, in terms of co-creating solutions to benefit the development of our knowledge society. The macro-structures of the higher education sector have the tendency to overemphasize quantitative goals to improve performance verifiability. Recruitment of international student talent is thereby turned into a mere supply issue. A mind shift is needed to rethink the efficacy of the higher education sector with regard to retaining foreign student talent as a means of contributing to society’s stock of knowledge and through that to economic growth. This thesis argues that academic as well as industrial understanding of the value of university-industry collaboration might then move beyond the current narrow expectations and perceptions of the university’s contribution to society’s innovation systems. This mind shift is needed to encourage and generate creative opportunities for university-industry partnerships to develop sustainable solutions for successful recruitment of foreign student talent, and thereby to maximize the wealth-creating potential of global student talent recruitment. This thesis demonstrates through the use of interpretive and participatory methods, how it is possible to reveal new and important insights into university-industry partnering for enhancing attraction and retention of global student talent. It accomplishes this by expressly pointing out the central role of human collaborative experiencing and learning. The narratives presented take the reader into a Finnish and Dutch universityindustry partnering environment to reflect on the relationship between the local universities of technology and their operational surroundings, a relationship that is set in a context of local and global entanglements and challenges.

Designing a Demand Forecasting Process in the Fast Moving Consumer Goods Context

The purpose of this thesis was to study the design of demand forecasting processes. A literature review in the field of forecasting was conducted, including general forecasting process design, forecasting methods and techniques, the role of human judgment in forecasting and forecasting performance measurement. The purpose of the literature review was to identify the important design choices that an organization aiming to design or re-design their demand forecasting process would have to make. In the empirical part of the study, these choices and the existing knowledge behind them was assessed in a case study where a demand forecasting process was re-designed for a company in the fast moving consumer goods business. The new target process is described, as well as the reasoning behind the design choices made during the re-design process. As a result, the most important design choices are highlighted, as well as their immediate effect on other processes directly tied to the demand forecasting process. Additionally, some new insights on the organizational aspects of demand forecasting processes are explored. The preliminary results indicate that in this case the new process did improve forecasting accuracy, although organizational issues related to the process proved to be more challenging than anticipated.

Value added logistics in supply and demand chain : COSTFIX : New operation model for maintenance material logistics in paper industry

This research report illustrates and examines new operation models for decreasing fixed costs and transforming them into variable costs in the field of paper industry. The report illustrates two cases – a new operation model for material logistics in maintenance and an examination of forklift truck fleet outsourcing solutions. Conventional material logistics in maintenance operation is illustrated and some problems related to conventional operation are identified. A new operation model that solves some of these problems is presented including descriptions of procurement and service contracts and sources of added value. Forklift truck fleet outsourcing solutions are examined by illustrating the responsibilities of a host company and a service provider both before and after outsourcing. The customer buys outsourcing services in order to improve its investment productivity. The mechanism of how these services affect the customer company’s investment productivity is illustrated.

Stochastic approximation methods in stochastic optimization

Stochastic approximation methods for stochastic optimization are considered. Reviewed the main methods of stochastic approximation: stochastic quasi-gradient algorithm, Kiefer-Wolfowitz algorithm and adaptive rules for them, simultaneous perturbation stochastic approximation (SPSA) algorithm. Suggested the model and the solution of the retailer's profit optimization problem and considered an application of the SQG-algorithm for the optimization problems with objective functions given in the form of ordinary differential equation.

Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.