Supply Chain Management: Inventory and Demand


Inventory demand forecasting is a fundamental component of the SC management and the pedestal on which effective and efficient inventories control lies (Cooper et al., 2007). While establishing a supply chain that will have a guarantee of future sustainability and which will offer Vacumet coating company (VCC) a rather sustainable competitive edge, Crank and William must integrate technologically-aided models that offer the company the highest level of inventories demand forecasting accuracy and precision.

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From the scenario, it is apparent that the current supply chain at VCC is ineffective as far as forecasting of inventories demand is concerned a fact evidenced by the fact that the firm is often faced with materials excesses leading to extra inventories costs and wastages, or shortages that usually call for the last minute expediting as a last gasp measure to save the company from missing customers’ needs or market demand.

While shortages of inventories poses a danger to VCC (for failing to meet the market demand and customers need with a threat of customer dissatisfaction and ultimate increase in turnover), excess of inventories amounts to wastages and increment in inventories handling and other related costs. Furthermore, inaccurate demand forecasting and predictability will necessitate maintenance of buffer stock to safeguard against possible stock out, over and above the threat presented by the inevitable uncertainties of demand and supply (Colin, 2005). Since the VCC relies on inventories or components that are crucial for its operations.

It is important for Frank and William to note that extra stock holding will also culminate to additional costs, hence accurate demand forecasting is crucial. This section therefore presents Frank and William with feasible inventories demand forecasting models and approaches that will enable the company to maintain optimal inventories levels for ultimate SC effectiveness.

Inventories demand forecasting

As a preamble to developing a supply chain that will offer VCC with the highest degree of accuracy and precision in inventories demand forecasting, it is important for the supply chain managers and partners to understand and appreciate that success in the achievement of this objective rests on the level of technological integration in the SC. As such, the various forecasting methods that the company employs must be integrated with state- of- the -art technology (mainly computer- assisted models) so as to fit them with the highest level of accuracy in forecasting inventories demand. This is because such forecasting depends on accurate sales forecasts, the rate of inventories utilization and prediction of inventories availability oscillations/variations.

According to Colin (2005) accurate demand forecasting that is entrenched in intermittent demand forecasting technology present several advantages to the company. First, the latter asserts that accurate inventories demand forecasting assists organization to reduce its inventories levels and the related costs enhances customers’ service via enhancement of customers’ satisfaction through in time meeting of customers’ needs and promotes overall business control and efficiency in the supply chain management.

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Reduction of inventories and inventories cost is usually an imperative objective of supply chain or inventories manager, especially for those businesses which have not achieved a financial footing. Also, most organizations are operating in business environment faced with acutely hard financial times (Rooney & Bangert, 2001). According to the latter, it would be unstrategic for a business which is a financial struggler to hold finances in unnecessary inventories extras.

Accurate demand forecasting therefore will enable Vacumet Coating Company to maintain optimal levels of inventories making sure that the right amount of component are available where and when they are needed hence avoiding disruptions in the company operations. In some way reduction of inventories will aid the company in the minimization of inventories carrying costs. For the company (VCC), such accuracy in forecasting is inevitable since its operations solely rely on it i.e. its operations depend on crucial components.

In addition, adoption of technologically assisted inventories demand forecasting techniques will enable VCC not only to reduce the inventories and related costs, but also enhance its capability in meeting the growing market demand and exploit the opportunity presented by this increase. In addition, it will help VCC to gain unmatched business control and efficiency that comes with competitive edge over others in the industry.

Ability of the organization to accurately forecasting of inventories demand, especially in situations where the company’s inventories are slow moving will positively transform to business efficiency throughout the other business sector (Collins, 2005). Ideally, the latter will enhance planning other company’s sectors; enhance inventories management, production arrangements and supply planning thus culminating to efficiency and effectiveness in business assets planning and utilization, both in manufacturing finance and other business components in VCC.

Approaches (models) to demand forecasting

Although inventories managers are continuously developing modern models for improving accurateness in inventories demand forecasting, there are basically four conventional models that are used to forecast demand. These include quantitative approach, qualitative model, causal regression model, the time series model and simulations (Rooney& Bangert, 2001). However, immense improvements on these models, based on incorporation of modern technology (computer- assisted software) have been made in an effort to enhance the effectiveness, accuracy and precision of inventories demand forecasting.

For instance, the new inventory demand forecasting technology that was recently developed by smart software, Inc the smart Willemain forecasting method has offered organizations with a technological advancement towards the achievement of organizational wide inventory demand forecasting, planning and inventory optimization capabilities (Collins. 2005). According to Collins (2005) users of this forecasting software have testified having experienced close to 100 percent accuracy in inventories demand forecasting, obtained massive savings in inventories cost and acquired unmatched enhancement of the levels of customers’ service and satisfaction.

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Time series

This is an approach to inventories demand forecasting that uses simple moving averages of inventories for particular periods of time to forecast future inventories needs for the organization. Such averages include, daily, weekly monthly or quarterly averages and which provides basic data for the model (Collins, 2005). As such, use of time series to forecast inventories demand relies on statistical calculations, hence a historical and traditional method of inventory demand forecasting. According to Collins (2005) time series relies on historical or rather known data to predict the future or unknown data.

While using time series to forecasting demand therefore, the inventory planners will use past inventory usage data to forecast the future inventory needs for the company. In addition, quantitative models not only support the simulation models to aid variations in time series and increase future demand predictability for the organization, but also offer simple statistical models such as time series to forecast immediate future likely occurrences within the supply chain (Rooney & Bangert, 2001).

Moreover, this approach uses exponential moving averages, trend seasonality regression models, soothing averages and weighted moving averages to forecast inventories demand, thus increasing the level of accuracy and precision in demand forecasting. The time series model has four basic elements which include; seasonality, trends, cycles, and random variations (Collins, 2005).


This is an approach to demand forecasting that uses consumer demand and consumption patterns, based on consumer choices that culminate to the overall demand so as to forecast future inventories requirements. Although the traditional models that used simulations to forecasting inventories demand were challenging (since they required a lot of data), the introduction and use of varied processes and procedures, coupled with integration of modern technology in the model has greatly lessened the massive data need, hence making it easier to use while retaining its high level of demand forecasting accuracy and precision (Collins, 2005).

As a result, Frank and William can adopt simulations model to enhance the VCC supply chain inventories demand forecasting efficiency but must be sure to not only apply the modern versions of the model but also to integrate state-of- the art technology so as to attain optimal results.

Qualitative models

Qualitative approach utilizes various models which includes but not limited to management judgments, the experts’ opinions, the sales force composite among others based on the previous experience on integration with the market to forecast the firms’ future inventories requirements for the organization (Rooney & Bangert, 2001, Collins 2005). Consequently, this model is risky since there is a great likelihood of bias and prejudices, leading to wrong predictions coupled with low accuracy. This is because it relies on no past data hence the outcome of the forecast have little apprehensiveness or believability

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Causal regression

In this method of demand forecasting, the independent variables are held with the same weight as the causal variables thus making is subjective (Collins, 2005). While the independent factors are those in control of the organizations, such as production capacity, the causal variable are beyond the control of the organizations such as the SC fluctuations due the uncertainty of demand and supply, inevitable inventories shortage and increasing costs as a result of rise in global prices.

Although VCC can use any of the models summarized to forecast its inventories requirements, it is advisable to use simulations or time series/ qualitative models or a combination of both, since they have a higher degree of accuracy and believability compared to causal regression and qualitative models.

Furthermore, it is easier and practical to amalgamate technology with qualitative and simulation models compared to the others which may be unfeasible. However, the situation in Vacumet Coating Company would be best solved using smart Willemain forecasting method that combines both quantitative (time series forecasting method and modern (state of the art) technology that has helped fit the model with the highest level of precision, accuracy and efficiency in demand forecasting that the world has ever seen.

In fact, the model has been appraised as having given the inventories planner near unit (100%) accuracy in inventories demand forecasting. In addition, it comes with a score of other advantages over other models which includes and not limited to increased business control and unmatched reduction in inventories costs.


Ballou, R. H. (2004). Business logistics/supply chain management: Upper Saddle River, NJ: Prentice Hall.

Carsten Barth, (2007), The Importance and Development of A Supply Chain Management. Harvard Business School Publishing.

Cooper C et al (2007) Supply Chain Management, More That A New Name For Logistics Inc The International Journal of Logistics Management Vol. 8 pp 1-14.

Colin D. Lewis (2005) Demands Forecasting and Inventory Control: A Computer Aided Learning Approach ISBN: 978-0-471-25338-9.

Jonathan Wright et al (2007) The Sustainable Supply Chain: Balancing Cost, Customer Service and Sustainability to Achieve High Supply Chain Performance source. Web.

Rooney C and Bangert, C. (2001), Developing the Right Approach to Requirements Planning under ERP.

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