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Review of Literature
This section explores selected journal articles in relation to the research objectives outlined in the introductory part of the research paper. The articles are critically reviewed to determine the key components and factors in forecasting, the forecasting techniques and models discussed, proposed and applied and the challenges of implementation in warehousing forecasting demand management. Furthermore, the research approach and methodology used by the studies will be evaluated to assess the assimilation of information and knowledge development.
Forecasting in Supply chain and Demand Management
Supply chain management (SCM) has become a critical concept in business management and consists of activities such as consists of sourcing, materials management, manufacturing support, and distribution management (Albarune and Habib, 2015) and revolves around managing material and information flow (Dugic and Zaulich, 2011). Supply chain management must anticipate the customer demand with products that in real sense have special character of qualities that fit in relation to their future area of use as well as being available at the time when the customer needs it (Dugic and Zaulich, 2011).
One of the critical strategies in customer demand management is forecasting; the concept defined as the prediction or estimation of an actual value in a future time period or for another situation (Albarune and Habib, 2015). Forecasting is an important determinant of operational performance (Acar and Gardner Jr, 2012) and is far most at the beginning of activities of SCM which initiates the all other actions.
Forecasting impacts and benefits are spread over across various departments and industries with the warehousing and distribution industry using forecasting as a planning tool (Albarune and Habib, 2015) and in rational decision-making based on numerical forecasted values. Its benefits are also diverse within Albarune and Habib (2015) asserting its impact in fulfilment of the customer requirements with some models giving better customer service than others (Acar and Gardner Jr, 2012), reducing risk and in improvement of supply chain process as well as inventory costs by increasing marginal improvements in average accuracy( ). Forecasts further add value by having place, possession and quantity utility (Dugic and Zaulich, 2011) and have daily impacts on different strategic, operational and tactical levels of a company’s forecasting needs (Fildes et al., 2019).
Optimal gain from use of forecasts depends on several key factors and element but mostly the choice of the forecast method and techniques with respect to the business model is the most crucial factor. In order to select the best model, the best practices, components and concepts should identify and aggregated in the best way possible. Briefly discussed below are the resource materials under critical review.
In the first journal article, Albarune and Habib (2015) examines the concept of forecasting in SCM and discusses the roles of Material Management (MM) and the marketing department team in forecast and proposes a forecasting management model that improves on spare parts forecasting and demand management based on Products’ character classification and demand pattern.
The second resource under review is the book chapter consisting of a cumulative of articles on various concepts and topics on business forecasting by the editors Gilliland et al. (2016) and provides general guidance on important considerations in the practice of business forecasting. The chapter covers the essential elements, uncertainty, measurements of forecastability and provides guidelines for improving forecast accuracy.
Third, a master’s thesis by Dugic and Zaulich (2011) conducts a case study on a furniture manufacturing and distribution company, IKEA, to identify the forecasting technique and model used and the findings to make an analysis of associated challenges and recommendations to a better forecasting performance. The manual and automatic system used by IKEA where planners demand and Sales Response System respectively forecast demand and strategically meet the demand requirements as well as make daily adjustments to increase forecasting accuracy.
Fourth, an extensive article by Fildes et al. (2019) explores the forecasting factors and problems faced by large-scale retailer alongside the on the strategic, tactical and operational level of forecasting needs as well as the market level, chain level, and store level aggregate sales forecasting based on production units, location, time buckets or promotion. Furthermore, different forecasting methods and benchmarks are discussed at product levels and a comparison on forecasting accuracy conducted
Lastly, Acar and Gardner Jr (2012) designs a forecasting model for a global manufacturer using a mixed-integer program that forecasts demand to give values for production, inventory, and transportation planning with the aim of minimizing total supply chain costs. The model takes tactical planning of (Fildes et al., 2019) at various levels of production, exponential smoothing methods explained in Dugic and Zaulich (2011), different initialization and fitting procedures and a trade-off analysis to evaluate the operational performance of the forecast model.
Key Components and Factors
Various components and factors that are imperative for the effective functional of warehousing and distribution firms and business with regard to the forecasting aspect has been identified in literature subject to their respective forecast and business models. For instance, (Corr, 2012) outlines seven elements namely the time frame for analysis, direction based on the baseline, magnitude in terms of distribution, probabilistic point or range value, range, statistical confidence levels, and historical forecast error for similar forecasts that every forecast should consider for it be valuable. Dugic and Zaulich (2011) also asserts that the most important attributes when selecting the best forecasting method as reliability, time, cost and the ability to identify market changes. Other key components and factors are discussed below.
Collaborative Forecasting.This concept concerned with departments working together to facilitate effective forecasting and is an important tool in meeting lumpy demand (Dugic and Zaulich, 2011). It reduces the risks from making inaccurate focuses by facilitating flow of adequate information in a timely and convenient manner. Schedules regarding when the meetings and information exchange should occur are created and support communication systems established (Fildes et al., 2019). Furthermore, structuring the organization and departments based on collaborative management principles such as value adding manager-subordinates relationship boosts the trust among staff and creates a productive workplace (Albarune and Habib, 2015).
Forecast Errors and Uncertainties.Forecasts cannot be utterly correct, and every forecast should include an estimate of error (Gilliland et al., 2016). Forecast errors exists when actual results differ from the projected value (Albarune and Habib, 2015), when forecasts are presented in the form of a single event or a single number (Gilliland et al., 2016), when actual demand is uncertain, and due to truncation of distribution values (Acar and Gardner Jr, 2012). Aaccording to (Boylan, 2003), forecast errors need to have an upper bound and lower bound error metrics for to generating exception reports and inform corrective actions in the process of forecastability. Proposed methods to reduce forecast errors uncertainties include improve statistical forecasting methods, revising judgmental statistical forecast and identifying more forecastable series (Dugic and Zaulich, 2011; Boylan, 2003), use of probabilistic forecasts tools such as prediction intervals, use of fan charts and probability density charts as better tools (Goodwin, 2014), sharing real time data and information across the SCM network using electronic communication technology as propagated by the CPFR model (Albarune and Habib, 2015),
Forecastability.The term is associated with the mitigation of forecast errors and is an important factor if the uncertainties and forecast accuracy is to be improved. The term may refer to the degree of accuracy when forecasting a time series, the smallest level of forecast error achievable (Corr, 2012) and the comparisons of forecast accuracy with a benchmark (Schubert, 2012). The forecastability values are essential in establishing comparability through benchmarking and use of metrics to size the forecast error (Dugic and Zaulich, 2011).
Demand Management.This is the anticipation and effort to approximate the customer demand for the product and using this information to make organization decisions with the aim of not only meeting the demands but also reducing demand for least profitable items and processing demand for the most profitable (Dugic and Zaulich, 2011). The ability to accurately forecast the demand is critical to the survival and growth of a retail chain because many operational decisions such as pricing, space allocation, availability, ordering and inventory management are directly related to its demand forecastt (Fildes et al., 2019).
According to Albarune and Habib (2015), the forecast managers have to ensure demand is met and should hence have alternative plans in case of any deviations in initial forecasts. In addition, sharing of knowledge about consumers, technology, logistics challenges and opportunities with other member in the supply chain allows timely response to customer demand without a compromise in customer service (Dugic and Zaulich, 2011). Also, it is essential that the business understand the difference between true and constrained demand as this influences the respective forecasts. True demand is largely unobservable and requires forecasting right at the beginning of the planning process (Fildes et al., 2019) while the constrained demand that is obtained by considering the limitations of goods and services provision in response to demand allows for forecasting in the light of these limitations (Gilliland, 2010).
Product Categorization. Accurate and effective demand management relies on customized forecasting where products are categorized into so that respective demand patterns (smooth, erratic, low turnover, slightly sporadic, and strongly sporadic) can be established (Albarune and Habib, 2015). Besides being used in demand management, product categorization and classification is also important for balancing between customer service and value creation, easing of forecasting process, keeping inventories at predetermined levels and to be able to make an analysis of the company’s assortment (Dugic and Zaulich, 2011).
Forecasting Techniques and Models
A variety of forecasting techniques are discussed through out literature. Dugic and Zaulich (2011) notes that the techniques are categorized into qualitative, extrinsic, and intrinsic methods while other authors group them into qualitative and quantitative techniques (Albarune and Habib, 2015). Qualitative techniques are mostly used for large product categories and base the customer demand on judgment and perception such as experts’ opinion, market research analysts and the Delphi method. On the other hand, extrinsic techniques base their forecast on external events and information affecting the demand and are suitable for product group or product families while the intrinsic technique use historical data to predict future values and mostly uses quantitative forecast models (Dugic and Zaulich, 2011).
Various models are also discussed, and others proposed in the journal articles and the book chapter under review.
Albarune and Habib (2015) proposes a forecasting management model that aims to overcome the loopholes in workflow due to behavioural user attitudes that bring distrust and poor coordination among departments. To overcome the limitation of forecasting practices identified by the authors, the model applies the concepts of product categorisation (Dugic and Zaulich, 2011), and has value addition and follow up in every steps of any activity for essence of quality control.
In discussing the concept of forecast ability, Gilliland et al. (2016) discusses various approaches to forecasting. One, forecasting software is using automatic model-selection procedures has been used to give an immediate lower bound for error reduction in addition to use as a benchmark for manual models (Boylan, 2003). Second, in finding a more forecastable series to forecast, Boylan (2003) also proposes the use of an autoregressive model of order one where current demand is related to the demand in the previous period and hierarchical models to address seasonality in demand. Fildes et al (2019) discusses three product hierarchies that can be used for planning forecasts namely SKU level, brand level, and category level for weekly, promotional planning and assortment forecasts respectively.
In the consideration of the principles and concept of demand management and its associated influential drivers of demand such as stock outs, intermittence, seasonality, calendar events, weather, promotions and social media reviews, Fildes et al. (2019) reviews different classes of models at product level demand subject to product hierarchies, namely univariate, multivariate and econometric methods. Since the uni variate methods do not consider external factors such as price changes and promotions as drivers of demand, they are only suitable for higher aggregation demand forecasting. It is therefore necessitous that base-times and judgmental adjustments (Boylan, 2003’s third strategy to better forecasts) are applied to lift effect of the most recent price reduction and/or promotion, and also the judgements made by the managers (Fildes et al., 2019) and the logistics departments (Dugic and Zaulich, 2011). The author also discusses econometric models mostly linear and dynamic regression models which take into account the mentioned factors of demand with advantages of the models being “simple, easy and fast to fit” (p 37).
Lastly Acar and Gardner Jr (2012) incorporates Boylan (2003)’s strategy that selecting the best forecast method is crucial for forecast ability and uses the trade-off analysis technique as well as the one-step errors from Morlidge (2013)’s Naive forecast method to get scaled error measures for different forecasting methods and hence determine the most suitable forecasting model.
Challenges
Various challenges have been identified in the above reviewed materials. Albarune and Habib, (2015)’s qualitative model has trust issues among users due to behavioural attitudes, difficulties due to use of revised forecast values in the freezing period, and lack of skills. Also, according to (Dugic and Zaulich, 2011), demand management is met by issues of lack of synchronization between departments, overemphasis on forecast demands and supply-demand misalignment making forecasting difficult. Benchmarking which plays a vital role in forecastability to minimize errors is also a challenge due to limited knowledge on the metrics (e.g. lead time and level of aggregation) used to model the benchmark and incomparability data from forecasts surveys due to differences in industry and product, granularity, forecast horizon, forecast process, and the business model (Kolassa, 2008).
References
- Acar, Y. and Gardner Jr, E.S., 2012. Forecasting method selection in a global supply chain. International Journal of Forecasting, 28(4), pp.842-848.
- Albarune, A.R.B. and Habib, M.M., 2015. A study of forecasting practices in supply chain management. International Journal of Supply Chain Management, 4(2), pp.55-61.
- Boylan, J., 2009. Toward a more precise definition of forecastability. Foresight: the International Journal of Applied Forecasting, (13), pp.34-40.
- Corr, C.K.R., 2012. What Demand Planners Can Learn from the Stock Market. The Journal of Business Forecasting, 31(3), p.21.
- Dugic, M. and Zaulich, D., 2011. Forecasting system at IKEA Jönköping.
- Fildes, R., Ma, S. and Kolassa, S., 2019. Retail forecasting: research and practice.
- Gilliland, M., Tashman, L. and Sglavo, U., 2016. Business Forecasting: Practical Problems and Solutions. John Wiley & Sons.
- Gilliland, M., 2010. Defining’ Demand’ for Demand Forecasting. Foresight: The International Journal of Applied Forecasting, (18), pp.4-8.
- Goodwin, P., 2014. Getting real about uncertainty. Foresight: The International Journal of Applied Forecasting, (33), pp.4-7.
- Kolassa, S., 2008. Can we obtain valid benchmarks from published surveys of forecast accuracy? Foresight, 11, pp.6-14.
- Morlidge, S., 2013. How good is a’ good’ forecast? Forecast errors and their avoidability. Foresight: The International Journal of Applied Forecasting, (30), pp.5-11.
- Schubert, S., 2012. Forecastability: A new method for benchmarking and driving improvement. Foresight: The International Journal of Applied Forecasting, (26), pp.7-15.
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