Demand Forecasting on Human Resource Supply Chain and Capacity - Research Paper Example

Demand Forecasting on Human resource, Supply-Chain and Capa Word Count: 1197 Words According to the Garage DoorNews, Amarr Garage Doors have been in business since the late 1980s. Since then, they have seen both strong and weak years and learnt how to try and stabilise sales as well as mitigating the impact of recessionary years. Over the years Amarr Doors has done well. For example, when the sales decreased, the decline was managed allowing the company to remain moderately profitable in tough times. While the general belief and hope is that the market conditions will catalyse the growth of business, the reverse will lead to laying off of employees or not hiring others. So, how do companies prepare for growth while still acknowledging that this forecast might not materialise? How do these estimates, which are a rough estimate of the actual demand or sometimes the mirror image of the market conditions, affect human resource, capacity, and the supply-chain management? How does this forecast influence the flexibility of these three important factors in manufacturing whether weekly, monthly or annually? The paper discusses the role of Operations Management, its framework and concepts while developing an appreciation for the operational processes, techniques, planning, and control systems. It also demonstrates the application of appropriate measures and the techniques for planning and production control. It is further classified under the three items for discussion for precision and clarity. Keywords: Supply chain management, forecast, demand amplification, intelligent decision systems, risk management, human resource management, trend analysis, and auto-id. Demand Forecasting on Human resource, Supply-Chain and Capacity Introduction According to the U.S. Census Bureau and the Department of Housing and Urban Development, the residential statistics for June 2013 showed that privately-owned housing units were at a seasonally adjusted annual rate of 911,000. This was 7.5% below the May rate of 985,000 but stood at 16.1% above the June 2012 estimate of 78500. The single-family authorisations were at 624,000, which was a 1.2 percent increase from the 620,000 in May. The industry is a replica of other organisations in terms of the forecasts from previous years to current times, and from current years to the future. In Ontario Canada, the houses were trending at 184,514 units in June compared to 184,087 in May. This is according to statistics from the Canada Mortgage and Housing Corporation (CMHC). According to Mathieu Laberge, the Deputy Chief Economist at CMHC, they use this trend to measure as a complement to the monthly SAAR of housing to account for considerable swings in monthly estimates and obtain a more complete picture of the state of the housing market. In some situations, however, Mathieu acknowledges that the data or projections might be misleading (The Garage Door News, 2013). So, what is forecasting? According to Boyle, Humphreys, and McIvor (2008), forecasting is the interaction between the decision makers’ perceptual and cognitive processes in respect to the objective characteristics of the environment. Boyle, Humphreys, and McIvor (2008), opine that forecasting can be viewed as the bridge between uncertainty and the risk, as well as he existing market conditions. Forecasting is closely linked with intelligent decision making because it may peel away some degrees of uncertainty while also increasing the risk of inventory to organisations. Forecasting and human resource One of the key goals of human resource planning is to have the right number of people with the right experiences, competencies and skills, in the right jobs, and at the right time and cost. Forecasting or not, it will ensure that the business production requirements are met in an effective and efficient manner as having too many employees breeds problems such as the risk of high labour expenses, lay-offs and downsizing (Yurkiewicz, 2000). On the other hand, having too few employees causes problems such as the risk of unmet production requirements, high overtime costs, and the challenges of accessing instant human resources needed to execute a certain task (Bayraktar et al., 2008). While human resource planning links people to an organisation’s mission, vision and objectives, strategic plan and the budgetary resources, its critical component remains forecasting. As pointed above, human resource may have an inherent degree of inflexibility occasioned in their utilisation and development. While the Joe Wilde Company has made efforts to ensure that each of their technicians earns a certification from the Institute of Door Dealer Education and Accreditation (IDEA), the garage door techniques can take the certification once they have a minimum of two years of field experience. The company, which is a leading provider of exterior home improvement services such as garage doors, openers, railings, and garage screens acknowledges that it might take several months to complete the process of selection, placing, and training an average employee with the process taking longer with higher-echelon personnel. The decisions of the personnel recruitment are strategic and in most instances, produce long-lasting effects (The Garage Door News, 2013). Forecasting also affects the contingent workforce that essentially encompasses workers who are not regular or full-time employees of the company (Stratton & Warburton, 2003). When the demand is expected to soar, the company might increase the number of temporary, seasonal, contractual, and the intern employees as compared to when it is expected to be low. As it is, the management ought to estimate the future labour availabilities and needs as well as the future demand in specific numbers and types of employees. In the garage doors case study, the internal supply forecasts rely heavily on the organisation-specific variables such as the retirement rates, transfers, turnovers and promotions. It will also depend on the external factors like the forecasts on product or service demand, uncertainties such as consumer behaviour, technology, as well as, the general economic environments. Product forecasting influences the HR forecast that in turn is carried out qualitatively and quantitatively. Himola (2007) says that qualitative forecasts are educated guesses or the estimates by individuals using techniques like the Delphi Technique, replacement planning or the allocation planning. Yurkiewicz (2000) recommends that the forecasting estimates be based on the revised forecasts, identification of the current job incumbents as well as the age, performance potential and judgment on labour supply and demand. The quantitative methods used in forecasting labour in line with the product demand are the regression model and the time-series model, where the fluctuations are projected through isolation of trend, seasonal and cyclical effects. According to Yurkiewicz (2000), forecasters can employ either of the two techniques or both. Factors to consider in Human Resource forecasts: 1. The organisation environment: in this scenario, organisations that operate in environments that are stable can use statistical inferences with ease as compared to their unstable counterparts. As such, an organisation operating in unstable environments might suffer from over-contracting or under contracting of HR personnel that will in turn affect the operations (Ward, Bechet, & Tripp, 1994; Duane, 1996). 2. Perceived uncertainty in the labour markets and the economy: it includes an increased number of turn-overs in the workforce, as well as, disturbances occasioned in the wage rates. Thus, while forecasting, upward and downward trends should be accounted for to escape hiring less or more. Other factors include competition and the organisation size in HR analysis and operations management decisions. The following figure summarizes HR projections in an organisation. Figure 1: The figure illustrates the HR personnel and forecasting, (Ward, Bechet & Tripp, 1994). Forecasting and supply chain management In spite of the rapid changes in the supply chain management and the logistics, inefficiencies still resurface. In SCM, demand amplification, otherwise known as the Bullwhip effect, is as important as any component of forecasting. While forecasts in product demand has, in recent cases, been influenced by globalisation and the crucial role of information technology, the small demand changes in the customer phases can be attributed to the sudden peaks and down turns in demand, the inventory holdings as well as corresponding impact on the production and delivery (Duane, 1996). In times of upswings, the operations management should put in mind that operations will cost a premium for the manufacturing and distribution where orders might increase exponentially and might also minimise the productivity development and increase the waste levels. Then, the risks coined to production and transportation delays will be considerably high. The recent emphasis on outsourcing, as well as, large-scale utilisation of the low cost sourcing will in most cases also effect on demand amplification with most organisations building their supply chain to react or conform to the market changes via more localised supply networks (Hilmola, 2007; Stratton & Warburton, 2003). Arguably, manufacturing input inventories, work in process (WIP) and raw materials fluctuate in recessionary periods whereas the end-item fluctuate less. In most organisations, the logic of demand amplification in supply chain management is evident in economic cycles with order backlog, amount of employment, amount of employment and existing inventory holdings being used to run simulation trials to forecast in different levels of the economy’s cycles (Albertson & Aylen, 2003). In most cases, different supply chain phases make use of different competing supplies to access cost efficiency while keeping the true demand confidential. Lam and Postle (2006) observed that some customers are unwilling to share information about a certain product while divulging of information might result in inflated purchase prices should the suppliers decide to form cartels. The forecasting, therefore, requires a game theory approach, as well as, other statistical models like the autoregressive moving models (ARMA) where a positive value is assigned to the last observed demand and a negative one to older observations, and the classical linear regression models (CLRM). In the CLRM, decision makers assume homoscedasticity or a constant variance for the different observations in the supply chain. However, in multi-stage supply chains, these may be heteroskedastic with the error associated with a manufacturer’s forecast of finished goods sales having a higher variance. Operations managers are encouraged to use tracking technologies while carrying out forecasts in order to capture unique identification of the physical objects and create a “digital” supply chain that provides information in real time (Himola, 2007). The adoption of auto-id also makes it possible to decipher the demand of an item, and forecast on it, instantaneously rather than the traditional paradigm of waiting for the batch updates or weekly and monthly ones, thus, reducing the error term in the supply-chain management. Capacity As mentioned above, a company should be able to link up its forecasts to capacity planning, production planning, and the purchases. It is also important to note that capacity is closely linked to supply chain operations. In our garage door productions example, the most obvious connection between the factory and the forecast analysis requires the following operational policies: running the factories at full capacity, maintaining the output rates constant during ramp-up and maturity, as well as, avoiding back orders. In the garage door example, the issue of capacity is crucial (Yurkiewicz, 2000; Duane, 1996). While most people are moving from the traditional paradigm where they only used garage doors for their garages, it is uncertain whether others will adopt it for decorative purposes too. During forecasts, the inventory buffer established during the ramp-up should be kept at a sufficient level that will promote maximum possible output but not resulting in unnecessary inventory holding costs. Operations managers should employ Pareto analysis, frequency analysis, as well as, econometric models and regression tools to come up with feasible capacity projections that balance the operations equation (Duane, 1996; Albertson & Aylen, 2003). Thus, players such as key suppliers, the internal operations, the key customers should be considered. In forecasting, a simple procedure would be to form partnerships between the sales and purchasing to enhance accuracy because procuring the right amounts of raw materials at the correct time, with the right information to the key suppliers will ensure a systematic paradigm that sustains productivity. Conclusion While forecasting is not always correct, the paper has discussed its effects on human resource, the supply chain, as well as, capacity. It has also pointed out valuable tips that operations managers can improve on to synchronise booms and recessionary periods while maintaining the right workforce, right raw materials, absorbing shocks in the supply chain and meeting varied customers’ demands in line with their tastes and preferences. References Albertson, K., and Aylen, J., 2003. Forecasting the behavior of manufacturing inventory. Int. J Forecasting, 19(2), pp. 299-311. Duane, M. J., 1996. Customized human Resource Planning. Westport: Quorum Books. Boyle, E., Humphreys, P., and McIvor, R., 2008. Reducing supply chain environmental uncertainty through e-intermediation: An organization theory perspective. International Journal of Production Economics, 114(1), pp. 347-362. Bayraktar, E., Koh, S. C. L, Gunasekaran, A., Sari, K., and Tatoglu, E., 2008. The role of forecasting on Bullwhip effect for E-SCM applications. International Journal of Production Economics, 113(1), pp. 193-204. Hilmola O-P., 2007. Stock market performance and manufacturing capability of the fifth long-cycle industries. Futures, 39(4), pp. 393-407. Lam J. K.C., and Postle R., 2006. Textile and apparel supply chain management in Hong Kong. International Journal of Clothing Science and Technology, 18(4), pp. 265-277. Stratton, R., and Warburton R. D. H., 2003. The strategic integration of agile and lean supply. International Journal of Production Economics, 85(2), pp. 183-298. The Garage Door News, 2013. Garage Doors Through Different Eyes. [Online] Available at: http://dixiedoorinc.com/garage-doors-through-different-eyes/ [Accessed 27 December 2014]. Ward, D., Bechet, T. P., and Tripp, R., 1994. Human Resource Forecasting and Modelling. NY: The Human Resource Planning Society. Yurkiewicz, J., 2000. Forecasting 2000: Predicting which product is best for your needs may be a daunting task. [Online] Available at: http://www.orms-today.org/orms-2-00/forecast.html [Accessed 27 December 2014]. Read More