BULLWHIP EFFECT ON INVENTORY MANAGEMENT IN KENYAS ...

Journal of Supply Chain Management, Volume 3, Issue 5, 2020; www.grandmarkpublishers.com

BULLWHIP EFFECT ON INVENTORY MANAGEMENT IN KENYAS’ PARASTATALS

AT NEW KENYA COOPERATIVE CREAMERIES LIMITED

Sostanis Okoth, Dr Pamela Getuno

2020

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BULLWHIP EFFECT ON INVENTORY MANAGEMENT IN KENYAS’ PARASTATALS

AT NEW KENYA COOPERATIVE CREAMERIES LIMITED

1A student in Master in Procurement and Logistics Jomo Kenyatta University of

Agriculture and Technology

2Senior Lecturer in the College of Human Resource Management of Jomo Kenyatta

University of Agriculture and Technology

Abstract

The dairy sector had suffered so much due to lack of commitment to timely funding of materials

procurement, poor material planning, poor inventory control, purchasing problems, quality

control problems; stores control problems, material movement and surplus disposal problems.

Lack of integrated inventory management has affected performance at New KCC hence reduced

profits in the downstream chain hence leading to loss of chain profits. New KCC has been

affected by poor inventory management related cases leading to low performance which caused

erratic deliveries in the firm, late deliveries and inflexibility hence affecting customer satisfaction

within their downstream chain. The study seeks to establish the Bullwhip effect on inventory

management of the New Kenya Cooperative Creameries Limited. The specific objectives are to

establish the effect of demand forecast updating, order batching, price fluctuations, shortage

gaming and rationing on the inventory management at the New Kenya Cooperative Creameries

Limited. The study used Institutional theory which influences organizational outcomes by

imposing constrains on firms’ operations. The Supply Chain Operations Reference theory

provides a unique framework that links performance metrics, processes, best practices, and people

into a unified structure. Theory of Change provides a comprehensive picture of the early- and

intermediate-term changes in a given institution. Dynamic Capability theory helps identify the

factors likely to impact enterprise performance. This study used a descriptive research design. The

target population of this study are the 303 employees of New Kenya Cooperative Creameries

Limited based in the head office in Nairobi City, Nairobi County. The sampling frame was sales

and marketing; procurement; directors, finance, communication, supply chain, transport managers

& supervisors; and production managers & supervisors at the New Kenya Cooperative

Creameries Limited head office in Nairobi and stratified sampling technique was used. The study

utilized primary data and data was collected using questionnaires as data collection instrument.

The study carried out a pilot study to test reliability and validity of the data collected. The study

selected a pilot group of 10 individuals from the target population. Data analysis was done with

the use of SPSS version 23 and presented using percentages, tabulations, means and other central

tendencies. Content analysis was used to analysed the qualitative data. The relationship between

the four independent variables and the dependent variables was tested using multiple regression

analysis and the significance of the relationship was tested using ANOVA analysis. The Study

opines that demand forecast updating, order batching, price fluctuations, shortage gaming and

rationing significantly affects inventory management at the New Kenya Cooperative Creameries

Limited.

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1 INTRODUCTION

The Bullwhip Effect is an observed phenomenon in forecast-driven distribution channels. The

effect indicates a lack of synchronization among supply chain members. Even a slight change in

customer sales ripples backward in the form of amplified oscillations upstream, resembling the

result of a flick of a bullwhip handle (Lee, Padmanabhan & Whang, 2016). The concept has its

roots in Forrester's Industrial Dynamics (Forrester, 2011). Since customer demand is rarely

perfectly stable, businesses must forecast demand in order to properly position inventory.

Variability coupled with time delays in the transmission of information up the supply chain and

time delays in manufacturing and shipping goods down the supply chain create the Bullwhip

Effect (Lee, Padmanabhan & Whang, 2016).

Wisner and Leong (2011) define inventory management is the process of efficiently overseeing

the constant flow of units into and out of an existing inventory. This process usually involves

controlling the transfer in of units in order to prevent the inventory from becoming too high, or

dwindling to levels that could put the operation of the company into jeopardy. Agus and Noor

(2010) proper inventory management also seeks to control the costs associated with the inventory,

both from the perspective of the total value of goods included and the tax burden generated by the

cumulative value of the inventory.

Managing inventory efficiently has become an important operational weapon for products and

service firms wishing to survive the competitive pressures. Most of these firms hold inventory so

as to meet their customers’ needs. Inventory therefore constitutes the most significant part of

current assets of these firms and because of the relative largeness of inventories maintained by the

firms, a considerable amount of fund is being committed to holding inventory (Sharma, 2016). It

thus becomes essential to deploy cutting-edge techniques to manage inventories so as to avoid

lost sales, costs of changing production rates, overtime costs, sub-contracting, unnecessary cost of

sales and backorder penalties during periods of peak demand (Chen, Murray & Owen,

2015).Inventory management has enabled firms to have adequate quantities of high quality items

available to serve customer needs, while also minimize the costs of carrying inventory (Brigham

& Gapenski, 2013). Many firms have not yet established how much to invest in inventories and

the right inventory levels to hold so as satisfy customers. Too much inventory consumes physical

space, creates a financial burden, and increases the possibility of damage, spoilage and loss. On

the other hand, too little inventory often disrupts manufacturing operations, and increases the

likelihood of poor customer service. Effort must be made by management to decide on the

optimum investment in inventory since it costs more money to tie down capital in excess

inventory (Lysons & Farrington, 2016).

The Research Problem

Mutwol (2015) studied the impact of the collapse of KCC and found that the sector had suffered

so much due to lack of commitment to timely funding of materials procurement, poor material

planning, poor inventory control, purchasing problems, quality control problems, stores control

problems, material movement and surplus disposal problems. Lack of integrated inventory

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management has affected performance at New KCC hence reduced profits in the downstream

chain hence leading to loss of chain profits (Otieno, 2016). In 2014 New KCC was affected by

poor inventory management related cases leading to low performance which caused erratic

deliveries in the firm, late deliveries and inflexibility hence affecting customer satisfaction within

their downstream chain (Mutwol, 2015). The volumes of milk in Kenya increased from 5 billion

in year 2015 to 5.4 billion litres in the country in the year 2016 New KCC (2016) thus there is

need for proper management in the industry.

Initially the problem that faced companies was the bullwhip effect which is variation in demand

and goods produced for stocking in large warehouses. This might not have been a good strategy

since it was prone to too much inventory against unforecasted demand (Christopher, 2015). The

excess inventory would easily lead to higher inventory holding costs and risks including possible

obsolescence. However, today the reversed bullwhip effect seems to be the major problem facing

firms. (Stock, Greis, & Kasarda, 2014). Effective management of bullwhip effect and hence

inventory management can lead to a reduction in cost, resulting in a significant saving. A

potential 6% saving on total cost through effective inventory management is achievable (Barratt,

2014). Studies have been done on the effect of bullwhip on inventory management; Buchmeister,

Pavlinjek and Palcic (2015) studied bullwhip effect problem in supply chains in Slovenia and

deduced increasing variability of production orders and stocks up the supply chain. The effect

indicated a lack of synchronization among supply chain members because of corrupt key

information about actual demand. Locally, Otieno, Ondiek and Odhiambo (2012) attempted to

find out factors causing reversed bullwhip effect on the supply chains of Kenyan firms and

suggested that capacity constraint was the major factor contributing to supply chain inefficiency.

The supply chain was inefficient because of capacity challenges and government intervention.

The above studies have been done in different contexts and thus this study seeks to fill the gap by

focusing on the bullwhip effect on inventory management in Kenya’s’ parastatals with reference

to New Kenya Cooperative Creameries Limited.

Objectives of the Study

The main objective of the study was to examine the Bullwhip effect on inventory management of

the New Kenya Cooperative Creameries Limited. The specific objectives were:-

To establish the effect of demand forecast updating on inventory management at the New Kenya

Cooperative Creameries Limited.

To assess the effect of order batching on inventory management at the New Kenya Cooperative

Creameries Limited.

To analyze the effect of price fluctuations on inventory management at the New Kenya

Cooperative Creameries Limited.

To determine the effect of shortage gaming and rationing on inventory management at the New

Kenya Cooperative Creameries Limited.

LITERATURE REVIEW

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Theoretical Review

Institutional Theory

The institutional environment is defined as an entity that lies outside the boundaries of the

organization. It influences organizational outcomes by imposing constrains on firms’ operations

and demanding adaptation of firms’ processes in order to survive. Institutional theory is

recognized through the pressures of social, cultural, political, and legal sector as main factors

influencing the operation of organizations (Yang & Sheu, 2011). Furusten, (2013) indicated that

according to the institutional approach under organizational field, there are three mechanisms of

pressures by which imitations (isomorphism) in structure and processes between organizations are

motivated: coercive, mimetic, and normative. Coercive isomorphism derives from formal and

informal pressures carried out on organizations by other organizations upon which they depend.

Such forces can be exerted through persuasion, invitation to join shared behavioral models, laws

and regulations, and government mandates.

Supply Chain Operations Reference Theory

The Supply Chain Operations Reference model provides a unique framework that links

performance metrics, processes, best practices, and people into a unified structure (Lysons &

Farrington, 2016). The framework supports communication between supply chain partners and

enhances the effectiveness of supply chain management, technology, and related supply chain

improvement activities. Business value, whether real or perceived, is derived from the

predictability and sustainability of business outcomes (Lysons & Farrington, 2016). Value is

articulated by measuring what is being managed. The SCOR model helps refine strategy, define

structure (including human capital), manage processes, and measure performance (Larsson,

2008).

An organization’s annual strategic priorities are manifest in SCOR’s vertical process integration.

Organizations that have applied SCOR to help with supply chain problem solving, process

improvement, process redesign, or business process engineering, have demonstrated that SCOR is

an effective enabler for aligning an organization’s portfolio of improvement projects with

strategic goals and objectives. SCOR processes extend from your supplier’s supplier to your

customer’s customer. This includes all customer interactions from order entry through paid

invoice; all product (physical material and service) transactions, including equipment, supplies,

spare parts, software, etc.; and all market interactions, from understanding aggregate demand to

the fulfillment of each order (Lee et al., 2016).

Theory of Change

Andreson (2015) states that a theory of change (TOC) is the product of a series of critical-

thinking exercises that provides a comprehensive picture of the early- and intermediate-term

changes in a given community that are needed to reach a long-term goal articulated by the

community or the government. Government initiatives are sometimes planned without an explicit

understanding of the early and intermediate steps required for long-term changes to occur;

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therefore, many assumptions about the change process need to be examined for program planning

or evaluation planning to be most effective. A TOC creates an honest picture of the steps required

to reach a goal. It provides an opportunity for stakeholders to assess what they can influence,

what impact they can have, and whether it is realistic to expect to reach their goal with the time

and resources they have available (Andreson, 2015)According to Clark and Dana (2012) theory of

Change is a specific type of methodology for planning, participation, and evaluation that is used

in the philanthropy, non-profit and government sectors to promote social change. Theory of

Change defines long-term goals and then maps backward to identify necessary preconditions.

Dynamic Capability Theory

Dynamic capability is defined as the firm’s ability to integrate, build and reconfigure internal and

external competences to address rapidly changing environments to attain new and innovative

forms of competitive advantage (Teece et al., 2010). The Dynamic Capabilities Framework helps

identify the factors likely to impact enterprise performance. It is gradually developing into a

(interdisciplinary) theory of the modern corporation (Teece, 2010). Dynamic capabilities have

lent value to the RBV arguments as they transform what is essentially a static view into one that

can encompass competitive advantage in a dynamic context (Barney, 2008). Dynamic capabilities

are “the capacity of an organization to purposefully create, extend or modify its resource base”

(Hunt & Kern, 2012).

Teece (2010) recognizes that dynamic capabilities help sustain firm’s evolutionary fitness by

enabling the creation, extension and modification of its resource base thereby creating long-run

competitive success. While some see dynamic capabilities as the key to competitive advantage

(Teece et al., 2010), others seem to doubt that there is actually such a thing. Dynamic capabilities

can usefully be thought of as belonging to three clusters of activities and adjustments: Firstly,

identification and assessment of an opportunity (sensing); secondly, mobilization of resources to

address an opportunity and to capture value from doing so (seizing); and thirdly continued

renewal (transforming). These activities are required if the firm is to sustain itself as markets and

technologies change (Teece, 2010).

http://en.wikipedia.org/wiki/Methodology

http://en.wikipedia.org/wiki/Planning

http://en.wikipedia.org/wiki/Participation_%28decision_making%29

http://en.wikipedia.org/wiki/Evaluation

http://en.wikipedia.org/wiki/Philanthropy

http://en.wikipedia.org/wiki/Not-for-profit

http://en.wikipedia.org/wiki/Government

http://en.wikipedia.org/wiki/Social_change

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.

Dependent Variable

Independent variables

Empirical Review

The section deliberates on the review of the existing empirical literatures on implementation of

water projects.

Demand Forecast Updating

According to a study by Whang et al., (2012) on analyzing the Bullwhip Effects in Supply Chains

at P&G, concluded that every company in a supply chain usually does product forecasting for its

production scheduling, capacity planning, inventory control, and material requirements planning.

Forecasting is often based on the order history from the company's immediate customers. The

outcomes of the beer game are the consequence of many behavioral factors, such as the players'

Demand Forecasting Updating

Capacity planning

Facility planning

Inventory levels

selection

Order Batching

Correlated ordering

Random ordering

Balanced ordering

Price Fluctuations

Product demand

Product Supply

Current economic

conditions

Shortage Gaming & Rationing

Promotions

Free Return Policy

Anticipated price

change

Inventory

Management

Efficiency

Effectiveness

Customer

Satisfaction

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perceptions and mistrust. An important factor is each player's thought process in projecting the

demand pattern based on what he or she observes.

A study by Caroll and Rao (2014) while attempting to establish the causes and remedies of

Bullwhip Effect in supply chains established that when a downstream operation places an order,

the upstream manager processes that piece of information as a signal about future product

demand. Based on this signal, the upstream manager readjusts his or her demand forecasts and, in

turn, the orders placed with the suppliers of the upstream operation. With exponential smoothing,

future demands are continuously updated as the new daily demand data become available. The

order you send to the supplier reflects the amount you need to replenish the stocks to meet the

requirements of future demands, as well as the necessary safety stocks. The future demands and

the associated safety stocks are updated using the smoothing technique. With long lead times, it is

not uncommon to have weeks of safety stocks.

Order Batching

A study by Ryan (2011) titled “Analysis of Inventory Models with Limited Demand Information”

concluded that in a supply chain, most companies’ batches or accumulate demands before issuing

an order. Instead of ordering frequently, companies may order weekly, biweekly or even monthly.

There are many reasons for ordering in batches. For example, a company might order a full truck

or container load from its supplier to receive a quantity discount and minimize transport costs.

Many manufacturers order from their suppliers after they ran their material requirements planning

(MRP) systems. These (MRP) systems often run once a month, resulting in a highly erratic stream

of orders.According to Sterman (2010) study on misperceptions of feedback in a dynamic

decision-making experiment stated that when a company faces periodic ordering by its

downstream neighbor, it sees a higher variability in demand than the downstream neighbor itself.

Periodic ordering amplifies variability and therefore contributed to the bullwhip effect. This effect

is small if all customers' order cycles were spread out evenly throughout time in a deterministic

way. Unfortunately, orders are more likely to be randomly spread out or worse, to be correlated.

When order cycles are correlated, most customers order at more or less the same time. This results

in even higher peaks and higher variability.

Price Fluctuations

According to Wilck (2013) study on the bullwhip effect on inventory management established

that companies often buy items in advance of requirements. This forward buying" results from

price fluctuations due to special promotions like price discounts, quantity discounts and coupons.

The result is that companies buy in quantities that do not affect their immediate needs. They often

buy in bigger quantities and stock up for the future. If the cost of holding inventory is less than

the price difference, buying in advance may well be a rational decision. However, when

companies buy more than needed and wait until their inventory is depleted, the variation of the

buying quantities is much bigger than the variation of the consumption rate, which leads to the

bullwhip effect.A study by Lee et al., (2012) study on the Bullwhip Effect in supply chains gave

an example of sale and order patterns of chicken noodle soup from shows how high-low buying

practices can lead to high variability in shipments from manufacturer to distributors. Such wide

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swings often force companies to run their factories overtime at certain times and to be idle at

others. Alternatively, companies may have to build huge piles of inventory to anticipate on big

swings in demand. On the other hand, if the manufacturer would not do price discounting, a

competitor who does, might take over its business. Thus, order batching leads to the bullwhip

effect and the effect is the strongest when the periods in which retailers place their orders are

correlated. The effect is weaker, when these periods are random and the weakest if they are

evenly distributed in time. If the order periods can be balanced completely, that is, if N is a

multiple of R, the bullwhip effect due to order batching does not occur.

Shortage Gaming and Rationing

According to Hunt & Kern (2012), study on seasonal and cyclical behaviour of farm gate milk

prices stated that when product demand exceeds supply, a manufacturer often rations its product

to customers. For example, the manufacturer then allocates its products in proportion to the

amount ordered by the different retailers. Retailers often anticipate on potential shortages by

exaggerating their real needs when they order. If demand drops later, this led to small orders and

cancellations.

A study by Graves (2009), titled: A single-item inventory model for a non stationary demand

process; states that manufacturing & service operations management call this overreaction by

customers, rationing and shortage gaming. This ‘’gaming" results in misleading information on

the product's real demand. To illustrate the effects of rationing gaming on the variance

amplification, consider a supply chain consisting of a manufacturer, multiple wholesalers, and

multiple retailers. If the manufacturer appears to be in short of supply, wholesalers played the

rationing game to get a large share of the supply.

A study by Fransoo & Wouters (2010) on measuring the bullwhip effect in the supply chain

concluded that assessing a possibility of the wholesaler not getting enough from the manufacturer,

retailers also play the rationing game. The effect is that demand and its variance are amplified as

one moves up the supply chain. In practice, there are many examples of this rationing and

shortage gaming. One example is the shortage of DRAM chips in the 1980's. In the computer

industry, orders for these chips grew fast, not because a growth in customer demand, but because

of anticipation. Customers placed duplicate orders with multiple suppliers and bought from the

first one that could deliver, then cancelled all other duplicate orders.

Inventory Management

According to a study by Ledman (2011) on the effect of information technology in amelioration

of bullwhip effect in supply chain in the dairy industry it was deduced that the integration of

information technology allows for the efficient transmission of information throughout the supply

chain which in-turn facilitates supply chain integration for amelioration of bullwhip effect. These

analyses of dairy industry retail outlets and suppliers is deliberated in terms of common strategic

diffusion of the central supply chain distribution (CscD) system, supply chain structure, supply

chain information technology, inventory management, expansion to African countries, improved

efficiency and effect of order variability.Armstrong and Green (2015) study on the influence of

Transaction Cost Economics and the Resource-based View on the Outsourcing Process states that

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in the retail perspective, the retailers must constantly look beyond the organisational boundaries

to evaluate and integrate the resources and capabilities of their suppliers and customers. To a

certain extent, this approach created superior value and a competitive advantage that companies

might sustain over time. Competitive advantage implies the creation of a system that has a unique

advantage over competitors to create customer value in an efficient and sustainable way.

Methodology

The study employed both qualitative and quantitative data analysis methods. Qualitative research

provided insights and understanding while quantitative data was used to generalize those insights

to a population pattern. Quantitative data was edited, summarized and coded for easy

classification in order to facilitate interpretation of the data expressed in the form of figures, tables

and charts. The data was analyzed using SPSS. Qualitative data was analyzed by content analysis.

ANOVA was used to test the significance of the fitted model. Data was presented in the form of

charts, tables and figures. Qualitative methods of data analysis were adopted to analyze the study

variables. To minimize subjectivity and make it possible to measure qualitative data the Likert

scale method and open-ended questions was used. The numbers in the Likert scale were ordered

such that they showed the absence or presence of the measurable characteristic (Kothari and

Gang, 2014). This mix of information was important as it gave out wide array of information in

regards to the study.

Further, Pearson's product moment correlation analysis and it's a powerful technique for exploring

the relationship among variables was used. Correlation coefficient was also used to analyze the

strength of the relations between variables. In addition, to know the strength of the variable

association, a correlation coefficient was also calculated.

A sequence of multiple regression analysis was used because they provide estimates of net effects

and explanatory power. Analysis of variance (ANOVA) was used to test the significance of the

model. R2 was used in this research to measure the extent of goodness of fit of the regression

model. The multiple linear was used to estimate the coefficient was as follows:

Y = α + β0 + β1X1 + β2X2 + β3 X3 + β4 X4 + є

Where: α = Constant Term; β1 = Demand Forecasting Updating coefficient; β2 = Order Batching

coefficient; β3 = Price Fluctuations coefficient; β4 = Shortage Gaming and Rationing coefficient;

Y = Inventory Management; X1 = Demand Forecasting Updating; X2 = Order Batching; X3 =

Price Fluctuations; X4 = Shortage Gaming and Rationing

Results

Test of Multicollinearity

Multicollinearity is a measurable marvel in which at least two indicator variables in a different

relapse model are profoundly connected, the bothersome circumstance where the relationships

among the autonomous factors are solid. A lot of factors are impeccably 103 multicollinear if

there exists at least one accurate direct relationship among a portion of the factors. Resistance of

the variable and the VIF esteem were utilized where esteems more than 0.2 for resilience and

qualities under 10 for VIF implies that there is no multicollinearity. For numerous relapses to be

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pertinent there ought not be solid relationship among factors. Insights used to quantify

multicollinearity incorporate resilience and fluctuation swelling factor. From the discoveries, the

factors had a resilience esteems >0.2 and VIF values <10 as appeared in table 1 show that there is

no multicollinearity among the autonomous factors.

Table 1: Multicollinearity test for Tolerance and VIF

Study Variable Collinearity Statistics

Tolerance VIF

Demand Forecasting Updating 0.621 1.612

Order Batching 0.530 1.887

Price Fluctuations 0.568 1.759

Shortage gaming and Rationing 0.548 1.826

Inventory Management 0.542 1.846

Multiple Linear Regressions for all Variables

To assess the model fit, a coefficient of determination was conducted by the study. The adjusted

R Square, usually known as the coefficient of multiple determinations, embodies a percentage of

variance in the dependent variable explained together or differently by the independent variables.

From the model fit, we got an average adjusted coefficient of determination (R Square) of 0.884

signifying that 78.4% of the variations in implementation of water projects could be described by

the independent variables under the study (OB: Demand Forecasting Updating DFU: Price

Fluctuations PF : and Shortage gaming and Rationing SGR). The difference of 11.6% is

attributed by other factors that are exclusive of this study

Table 2 Multiple Linear Regressions

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 .891 .794 .884 .317

Table 3 ANOVA for Multiple Regression Analysis

Model Sum of Squares df Mean Square F Sig.

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Regression 26.481 5 5.296 7.23 0.004

Residual 158.217 216 0.732

Total 184.698 221

The ANOVA test is used to determine whether the model is important in predicting the Inventory

Management. At 0.05 level of significance the ANOVA test indicated that in this model the

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independent variables namely; Oder batching OB: Demand Forecasting Updating DFU: Price

Fluctuations PF : and Shortage gaming and Rationing SGR were predictors of and Inventory

Management IM as indicated by significance value=0.005 which is less than 0.05 level of

significance (p=0.001<0.05).

Correlation analysis between the study variables

The results of correlation analysis are as shown in Table 4.6.1. The findings indicated that there

was strong positive and significant relationship between Oder batching and Inventory

Management. With a Pearson correlation coefficient r=0.684, p-value <0.05 which was significant

at 0.05 level of significance. This implies that improved Oder batching results in increase of

Inventory Management. There was strong positive and significant relationship between Demand

Forecasting Updating and Inventory Management. With a Pearson correlation coefficient r=0.485,

p-value <0.01 which was significant at 0.01 level of significance. This implies that increased

Demand Forecasting Updating results in increase of Inventory Management. There was strong

positive and significant relationship between Price Fluctuations and Inventory Management.

With a Pearson correlation coefficient r=0.891, p-value <0.05 which was significant at 0.05 level

of significance. This indicates that improved Price Fluctuations results in improved Inventory

Management. There was strong positive and significant relationship between Shortage gaming

and rationing and Inventory Management. With a Pearson correlation coefficient r=0.569, p-value

<0.05 which was significant at 0.05 level of significance. This implies that improved Shortage

gaming and rationing results to improved Inventory Management.

Table 4: Correlation Matrix

IM OB DFU PF SGR

IM

Pearson

Correlation 1

Sig. (2-tailed) 0

OB Pearson

Correlation .684

* 1

Sig. (2-tailed) 0.036

DFU

Pearson

Correlation .485** 0.023 1

Sig. (2-tailed) 0 0.805

PF

Pearson

Correlation .891** .516

** 0.143 1

Sig. (2-tailed) 0 0 0.123

SGR

Pearson

Correlation .569* .297

** .197

* .189

* 1

Sig. (2-tailed) 0.009 0.001 0.033 0.041

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The study concludes by stating that community participation had a strong positive relationship on

successful implementation of water projects. In order to aid in community participation, local

community should participate in implementation decision making matters of the project,

volunteer in implementation activities, identification of water projects of their preference and

community should be selected into water committees. The study results are consistent with

stakeholder’s theory that stresses that in every project exists a group of individuals with interest

which management should endeavor to take care of.

Conclusion

In the examination of the influence of Oder batching, the study concluded that the Oder batching

have a significant influence on Inventory Management. The study also concluded that a unit

increase in Oder batching on their own results in an increase in on Inventory Management.

In the examination of the effect of Demand Forecasting Updating, the study concluded that the

use of Demand Forecasting Updating have a significant influence on Inventory Management. The

study also concluded that a unit increase in the use of Demand Forecasting Updating on its own

results in an increase in Inventory Management.

In the assessment of the influence of Price Fluctuations, the study concluded that the use of Price

Fluctuations has a significant influence on Inventory Management. The study also concluded

that a unit increase in the Price Fluctuations on its own results in an increase in Inventory

Management.

Finally, in the examination of the influence of Shortage gaming and rationing, the study

concluded that Shortage gaming and rationing has a significant influence on Inventory

Management. A unit increase in Shortage gaming and rationing on its own results in an increase

in Inventory Management.

Recommendations

The researcher has suggested pertinent recommendation citing information from theoretical

review and the study findings in line with specific objectives of the study. The main objectives

were Oder batching, Price Fluctuations Shortage gaming and rationing and Demand Forecasting

Updating and their Influence in Inventory Management.

In the context of Oder batching, the study recommends that emphasis be placed on only

framework development that is fool proof. This will ensure that the employees in the procurement

department can competently perform their duties within the department.

In the context of use of Price Fluctuations, the study recommends that more emphasis be put on

collection of information and monitoring price fluctuations using computers for ease of access to

information and for transparency purposes. Within the context of Demand Forecasting Updating,

the research recommends that forecasting models should be put in place to arrest unexpected

demand surge.

Finally, in the context of Shortage gaming and rationing, the research recommends the

implementation of the Shortage gaming and rationing policy which advocates for fair rationing in

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every Inventory management. This will guarantee that not only specific people get inventory from

new KCC.

Areas for Further Research

The researcher suggests further research to be conducted to investigate other factors which

influenced Inventory Management for example, environmental factors like infrastructure,

facilities and location. Other scholars could also evaluate external factors that influence Inventory

Management in the other state corporations. Finally, the study suggests for further studies an

examination of determinants of effective inventory management in selected State Corporations in

Kenya.

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BULLWHIP EFFECT ON INVENTORY MANAGEMENT IN KENYAS ...

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