Demand Forecasting Influence on Marketing Performance of Business Organization
Chapter One
General Objective
The study’s general objective was to find out the contribution of demand planning towards improving marketing performance.
Specific Objectives
The study sought to achieve the following specific objectives.
To find out the role of forecasting in inventory optimization.
To find out the role of supplier collaboration in inventory optimization.
To find out the role of demand forecasting’s influence on marketing cost performance.
CHAPTER TWO
LITERATURE REVIEW
Introduction
This chapter reviews relevant scholarly articles and literature on achievement of Supply Chain efforts contributions majorly being forecasting, supplier partnerships and other key aspects of demand forecasting. This chapter examines the position occupied supply chain efficiency in Strategic Supply Chain Management and the implications towards achievement of the Supply Chain goals.
Forecasting on Inventory Optimization
In their study, Albarune and Habib (2015) observe that forecasting is the epicenter of all Supply Chain Management activities which triggers all other activities both within and outside the organization. Albarune and Habib (2015) conclude that forecasting is the key driving factor in planning and decision making in SCM as well as enterprise level and that companies that companies that perform exceptionally well rely on true numerical value of forecasting to make decisions in capacity building, resource allocation, expansion, process scheduling among others.
Demand and Supply Forecasting
Slack, Chambers and Johnston (2010) define Demand forecasting as the investigation of an organisations’ demand for an SKU to include current and future demand. They further define Supply forecasting as the collection of data about current producers and suppliers and technological and external trends that are likely to affect supply.
Bull Whip Effect
Wilmjakob (2014), observes that Bull Whip Effect describes the increasing variability of demand in a supply chain and is usually observed at the interfaces between the partners during the transition of demand, and is caused by separate determination of demand. This leads to tremendous inefficiencies.
Brent (2014), indicates that Bull Whip effect is easily experienced where in looking at firms further back in the supply chain, inventory swings in larger and larger waves in response to customer demand with the largest wave of the whip hitting the supplier of raw materials. Due to this effect, supply-chain players have opted to build and maintain inventory buffers or safety stock to cover for such swings in orders.
Simply said, the Bullwhip effect occurs due to demand variability increases as orders move up the supply chain away from the retail customer, and small changes in consumer demand can result in large variations in orders placed upstream.
CHAPTER THREE
RESEARCH METHODOLOGY
Introduction
The general objective of this study was to find out whether and the contribution of demand forecasting influence on supply chain efficiency. This chapter provides the master plan that the researcher will use to conduct the study. It describes the research design, the population, the sampling design, the data collection method, research procedures and data analysis methods.
Research Design
Kumar (2011) defines research design as the blueprint for the collection, measurement and analysis of data. It sets out the specific details of the objectives of the study and explains how the researcher will achieve the objectives of the study. The researcher adopted descriptive and explanatory research designs. Descriptive research design is one that seeks to portray an accurate representation of persons, events or situations while an explanatory research design is one that establishes a causal relationship between variables. (Saunders, Lewis, and Thornhill, 2019). The dependent variable in this study is marketing performance defined by Marketing costs reduction and improved customer service while the independent variable is demand forecasting defined by supply chain forecasting and partnering.
Population and Sampling Design
Population
According to Cooper and Schindler (2010) a population is the total collection of elements about which the researcher wishes to make some inferences. It is a collection of all the units of concern that the researcher intends to conduct a study on within a specific problem space.
The target population for this study was employees holding Supply Chain positions in the 80 Fast Moving Consumer Goods organisations located within Abuja County.
CHAPTER FOUR
RESULTS AND FINDINGS
Introduction
The general objective of the study was to determine the contribution of demand forecasting influence on improving Marketing performance of business organizations in Nigeria. In this chapter, the findings are analyzed and presented according to the specific objectives. The first section presents the analysis of demographic characteristics of supply chain players in the FMCGs in Nigeria. The second section analyzes the role of forecasting in improving Marketing performance of business organizations in Nigeria and the third section analyzes the contribution of supply chain collaboration towards improving marketing performance of business organizations in Nigeria. Finally, the finding concerning the contribution of demand forecasting to improving Marketing performance in FMCGs in Nigeria is presented. A summary of the major findings is also provided at the end of the chapter. Seventy one out of 80 questionnaires that were administered were successfully filled and returned giving an 88% response rate which was sufficient for the study.
CHAPTER FIVE
DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS
Introduction
The previous chapter analyzed the findings of the study. In this chapter, the researcher discusses the overall findings in line with the theoretical framework with the aim of answering the research questions as well as the research purpose. The chapter also presents conclusions and recommendations from the current study and recommends future possible studies.
Summary of the Study
The general objective of the study was to find out the contribution of demand forecasting towards improving marketing performance. Specifically, the study sought to achieve the following specific objectives; To find out the role of forecasting towards inventory optimization; To find out the role of supplier partnering towards inventory optimization; To find out the role of demand forecasting towards Marketing cost performance.
The researcher adopted descriptive and explanatory research designs. The target population for this study was Fast Moving Consumer Goods organizations located within Abuja and Kiambu County. The researcher adopted a stratified sampling technique. The target sample size for this study was 80 individuals holding positions in the target organizations. A structured questionnaire was used for data collection. Both descriptive statistical techniques and inferential techniques were employed for analysis. Inferences were drawn using correlation and data was presented in tables, pie and bar charts.
In terms of forecasting, the findings showed that most of the FMCGs conducted forecasting in their supply chains with a majority having the forecasting period longer than 6 months. There was a significant positive correlation between forecasting and marketing performance. Further, the study found that forecasting led to the reduction in inventory holding in the organization as well as reduced out of stock situations. The study also found that marketing inventory and operations planning improves the accuracy of forecasts generated and guides the inventory quantities to carry at a given time.
Concerning supplier collaboration, the study found that there was a significant positive correlation between supplier collaboration and marketing performance, forecasting and demand forecasting. Also, the study found that supply chain collaboration increases supply chain reliability and ensures suppliers remain in stock.
In terms of demand forecasting, the study found that marketing performance was positively significantly correlated to Forecasting, Supplier Collaboration and Demand forecasting. From the study, demand forecasting reduces marketing costs, enables better visibility of costs, clears mismatch of processes along the chain and reduces uncertainties along the supply chain.
Discussion of Findings
Forecasting on Inventory Optimization
From the findings the study found out that majority of the organizations perform forecasting in their supply chains. This is confirmed by most of the respondents having indicated that supply chains in their organizations perform forecasting and of the respondents who indicated that their organizations’ supply chain perform forecasting again a majority indicated the forecasting duration between 6-12 months.
Further, the findings from this study showed that forecasting leads to a reduction in inventory holding in the organizations. This is confirmed in most of the respondents having agreed that forecasting in their organizations has led to the quantities held in the supply chain by SKU in the portfolio. This was expected as forecasting leads to a better knowledge of future stock requirements hence supplier chain players do not need to hold excess inventory. This finding was consistent with Fritsch (2015) recommendation that forecasting can support organization’s strategic goals of ordering enough to meet ongoing customer demand while not carrying too much extra inventory to keep costs under control.
Also, the study found out that that there is a direct link between inventory forecasting and inventory optimization. This is confirmed by most of the respondents having agreed that inventory forecasting in their organizations supply chains has led to the inventory optimization along the supply chain inventory points. This indicates that organizations can hold just enough as customers’ need and do not go out of stock. This finding correlate to Kot, Grondys, and Szopa (2011) conclusion that inventory forecasting and optimization are directly correlated with inventory forecasting enhancing inventory optimization.
Also, the study found out that forecasting reduces out of stock situations along the supply chain. This was demonstrated by most respondents agreeing that forecasting reduces the number and intensity of cases of stock outs in the supply chain. This finding collates with Ahmed ( 2016) recommendation that organizations need to realize the importance of inventory forecasting, even if they are working in JIT System or with long lead time suppliers as it enables them to arrange raw materials in anticipation of actual customer orders hence minimizing out of stock instances.
The study revealed that Marketing inventory and Operations Planning accurately guides the inventory quantities to carry at a given time as depicted by most of the respondents who agreed that in their organisations, SI&OP provided a base as to what inventory levels to hold by SKU. This is aligned with Fritsch (2015) findings that SI&OP enables management to establish the desired levels of customer service, inventory levels, and production plans which guides the organization toward managing their business proactively towards optimal performance.
From the study, Marketing inventory and that Operations Planning improves the accuracy of forecasts generated as depicted by most of the respondent’s agreement. This is aligned to Kaipia, Holmstrom, Småros, and Rajala (2017) conclusion that SI&OP improves the accuracy of forecasts by incorporating information from many sources.
The study findings collate with literature review by Brian and Henry (2014) where they concluded that forecasting brought benefits such as avoidance of overproduction and reducing inventory levels required.
Supplier Collaboration on Inventory Optimization
From the findings the study found out that almost all of the respondents agreed that supplier partnering leads to increases supply chain reliability. This is in line with Faertes (2015) conclusion that partnering with the other players in the supply chain guarded against compromise of security of supply such as infrastructure related failures, crisis scenarios, financial and scarcity issues, which could be addressed and treated and contingency plan conceived with due anticipation. This creating a possibility to address probabilities of occurrence to all of them and to evaluate the associated impacts and gathering suitable expertise. This in turn ensures security of supply and general reliability of the supply chain.
Consistent with Zsidisin and Smith (2015) conclusion that although cost reduction was the original impetus behind the implementation of ESI within supply chains, the study showed that majority of the respondents agreed that Early Supplier Involvement increases supply chain reliability by substantially reducing supply risk associated with products and suppliers in new product development.
From the findings, the study found that Collaborative Planning Forecasting and Replenishment ensures suppliers remain in stock as depicted by a strong agreement by most of the respondents in relation to the supply chain in which their organisations are in involved. This is aligned to Kim and Mahoney (2016) conclusion that extensive information sharing and joint decision making under the CPFR arrangement further improved mutual understanding and visibility into partners’ interdependent activities in the vertical chain. CPFR could improve not only operational efficiency but also specialization incentives for mutual economic benefits, which constitutes a relational contract in the vertical chain.
The findings further established that Electronic Data Interchange influences supply chain reliability to a large extent as depicted by agreement by majority of the respondents in relation to the supply chain their organisations are involved in. This corroborates Lim and Palvia (2011) findings that provides strong evidence of a positive relationship between use of EDI and improved customer service. Specifically, five of the six components of customer service showed marked improvement with the use of EDI.
Additionally, the study found that electronic data interchange, Early Supplier Involvement, Supplier Development and Collaborative Planning Forecasting and Replenishment influenced the reliability of supply chains. This is derived from most respondents having indicated that electronic data interchange, Early Supplier Involvement, Supplier Development and Collaborative Planning Forecasting and Replenishment influenced the reliability of supply chains influenced the reliability of supply chains their organizations were involved in to a large extent.
The study findings align with Slone, Dittmann, and Mentzer (2010) conclusion that Supply Chain Collaboration benefits include but are not limited to improved efficiency and effectiveness in helping all the supply chain players meet their customer demands, grow markets, and increase competitive market share. Similar findings were arrived at
Demand forecasting influence on Marketing cost performance
On demand forecasting and marketing performance the study found out that demand forecasting is critical in reduction of marketing costs and that demand forecasting enables better visibility of marketing costs optimization; that demand forecasting reduces mismatch of processes along the supply chain and that demand forecasting reduces uncertainties along the supply chain.
The study found that demand forecasting is critical in reduction and visibility of marketing costs as depicted by most of the respondents having agreed that demand forecasting enabled reduction of costs and improved visibility of costs in the supply chain their organisations were involved in. This is in line with O’Byrne (2011) conclusion that organisational profits can rocket upward if organisations achieve sufficient savings in marketing costs through keeping costs down and reliability up by designing the supply chain network to minimize product handling.
From the findings, the study found that demand forecasting reduces mismatch of processes along the supply chain as majority of the respondents indicated that demand forecasting had reduced mismatch of processes along the supply chains in which their organisations were involved in. This correlates to the conclusion by Vitasek, Manrodt and Kelly (2013) that organisations can improve operational efficiencies in their supply chain by optimizing service levels and at the same time keeping inventories to a minimum. Service-level expectations are managed by communicating lead-time and other variables on the order form. Additionally, organizations can use statistical analyses that combine both volume and variability to set safety-stocks levels that are most efficient to meet customer service goals.
Concerning reduction of uncertainties along the supply chain, the study found that demand forecasting reduces uncertainties along the supply chain with majority of the respondents indicating that uncertainties in the supply chains their organisations’ are involved in were reduced due to demand forecasting techniques. This is in line with Angkiriwang , Pujawan and Santosa (2013) conclusion that the proactive nature of supply chain flexibility through safety stock holding, capacity buffer, supplier backups and safety lead times can allow organizations to redefine market uncertainties or influence what customers have come to expect from a particular industry. These will enable organizations to achieve higher service levels, efficient resource utilization and responsiveness.
The findings also indicated that Marketing inventory and Operations Planning influences marketing costs to a large extent. Forecast Reviews as a factor that influences marketing costs to a large extent and that Market intelligence and information sharing, Materials Requirement Planning and Supply chain integration influence marketing costs
The study findings collate with literature review by Moser, Isaksson and Seifer (2018) conclusion that good demand forecasting enhances profitability through avoidance of unnecessary costs.
Conclusions
Forecasting on Inventory Optimization
Forecasting in supply chains leads to a reduction in inventory holding at the specific inventory holding locations in a supply chain by ensuring that overstocks are kept low at any time. Forecasting also ensures that out of stock situations are kept to the minimum to count and reduces the length of out of stock period. There is a direct link between inventory forecasting and inventory optimization with forecasting leading to lean inventory holding across the organization. Therefore, customer orders are met when placed and supply chain inventory holding costs are kept low. Marketing inventory and Operations planning accurately guides the inventory quantities to carry at a given time and improves the accuracy of forecasts generated as SI&OP gathers information and guiding data from many sources resulting in close to accurate forecasts.
Supplier Collaboration on Inventory Optimization
Supply chain collaboration leads to inventory optimization as partnering among the supply chain players increases supply chain reliability through prevention against compromise of security of supply such as infrastructure related failures, crisis scenarios, financial and scarcity issues and ensuring anticipation of issues likely to arise. Further Early Supplier involvement increases the reliability of supply chains through reduction of supply risk associated with products and suppliers in new product development. The study concludes that supplier partnering leads to increased supply chain reliability and that Early Supplier Involvement increases supply chain reliability and that Collaborative Planning Forecasting and Replenishment ensures suppliers remain in stock. The study further concludes that Electronic Data Interchange influences supply chain reliability to a large extent and that Early Supplier Involvement as a factor that influences supply chain reliability.
Demand forecasting influence on Marketing performance
On demand forecasting and marketing performance the study concludes that demand forecasting is critical in reduction of marketing costs and that demand forecasting enables better visibility of marketing costs optimization; that demand forecasting reduces mismatch of processes along the supply chain and that ddemand pplanning reduces uncertainties along the supply chain.
Additionally, the study concludes that Marketing inventory and Operations Planning influences marketing costs to a large extent. Forecast Reviews as a factor that influences marketing costs to a large extent and that Market intelligence and information sharing, Materials Requirement Planning and Supply chain integration influence marketing costs
Finally, on the correlation between marketing performance and the studied variable, the study concludes that the marketing performance is positively significantly correlated to
Forecasting, Supplier Collaboration and Demand forecasting as shown by spearman’s rho correlation as reported by positively and significant correlation coefficients.
Recommendations
Recommendations for Improvement 5.5.1 Forecasting on Inventory Optimization
The study recommends forecasting in inventory since forecasting is the epicenter of all Supply Chain Management activities which triggers all other activities both within and outside the organization. Forecasting is the key driving factor in planning and decision making in SCM as well as enterprise level and that companies that companies that perform exceptionally well rely on true numerical value of forecasting to make decisions in capacity building, resource allocation, expansion, process scheduling among others.
Supplier Collaboration on Inventory Optimization
The study further recommends operations and supply chain strategy which is a key saving opportunity beginning with analyzing the service needs of customers and implementing a demand-planning strategy then developing product movement protocols based on customer segmentation. The more streamlined operations are, the more efficient a business will likely be.
Demand forecasting influence on Marketing performance
Finally, the study recommends enlist Routine Demand Forecasting as a key strategy for organizations seeking to reduce their marketing costs since using manually edited, arithmetic or stochastic forecasting models to reduce forecast error will reduce overstock, backorders, and the need for lateral or reverse logistics, holding inventory levels closest to only that which is required to support the desired customer service level. Editing history to eliminate nonrecurring promotions and to compensate for out-of-stock situations is key.
Recommendation for Further Studies
Like all other studies, this study was not without its limitations. One of the key limitations to the study was about its scope, which was limited to FMCGs in Kiambu and Abuja counties. This is likely to invite a bias in representing the FMCGs in Nigeria because those included in the sample arguably had established and improved supply chain structures, a factor which itself, has an influence on performance. Another study which considers the supply chain structure and learning in supply chains should be carried out to corroborate the results of this study.
In retrospect, the study also had other methodological drawbacks such as its sample size, which, although representative of the target population, may not accurately represent the entire population of the FMCGs in Nigeria. In addition, other organizations to which marketing performance is critical were not represented, thus, it became difficult to make comparisons. Therefore, while this study is sufficient as far as the case study and the objectives were concerned, a future study which addresses these methodological gaps is necessary to validate, or otherwise, the outcomes of this study.
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