This literature review focuses on different and diverse views and opinions on inventory management, the importance of accurate demand information in high inventory supply chains and the challenges and contributions of forecasting/demand management to the achievement of inventory optimisation in the supply chain. This literature review has been conducted via the consulting of current relevant literature in the area of inventory management and forecasting/demand management.
However, during the course of this research, it was discovered that the literature in the area of inventory management theory is so huge that any attempt to provide a comprehensive coverage of even a small part of it in this exercise would be doomed to failure. In the following paragraphs we refer to an array of selected papers, which present valuable research and insight, having common general features with the problem that which we aim to investigate. For the purpose of this research, which looks at inventory and demand management in SME's we assume that a two echelon supply chain is in existence, with the medium sized SME manufacturer a supply chain partner and producer to larger organisation and operating in a Build to Order production/manufacturing system.
2.1 THE UK MANUFACTURING INDUSTRY
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A manufacturing supply chain is a network of suppliers, factories, subcontractors, warehouses, distribution centres, and retailers, through which raw materials are acquired, transformed, produced, and delivered to the end customers. Such a supply chain network must meet customers' service goals at specified service levels at the lowest possible cost (Taskin and Guneri, 2005).
According to the department for business innovation and skills (BIS 2010), manufacturing is currently the third largest sector in the UK economy, after business services and wholesale/retail in terms of share of UK GDP. In 2009, manufacturing contributed around £140 billion in gross value added to the economy and employed some 2.6 million people. UK manufacturing is an established leading manufacturer in the world, being the sixth largest manufacturer globally by output, and a leading exporter of technology intensive manufacturing goods. According to the Financial Times (2012) the UK manufacturing Industry makes up for about 10 percent of the country's economic output, in 2009, four major industries - food, beverages and tobacco, chemicals and pharmaceuticals, publishing and printing and fabricated metals contributors to total manufacturing gross value added and employment, accounting for around 46% and 43% respectively as seen in Figure 1.
Figure 1: Showing the percentage for gross value added by different manufacturing sectors.
Source: BIS Report 2010
According to Anthony, Kumar and Madu (2005), Small and Medium Enterprises (SMEs) are one of the principal driving forces in the development of an economy because of its significant contribution in terms of number of enterprises, employment, output and exports in most developing as well as developed countries. Anthony, Kumar and Madu (2005) suggest that SME's are the life-blood of modern economies, stating that their importance to the economy of the UK cannot be understated.
Employement sector breakdown sme UK.bmp
Source: Aberdeen Group 2009
According to the Statistical report on Business Population Estimates by BIS (2011), there were an estimated 4.5 million private sector businesses in the UK at the start of 2011, an increase of 94,000 (2.1 per cent) since the start of 2010. SME's employed an estimated 23.4 million people, and had an estimated combined annual turnover of £3,100 billion. Almost two thirds (62.4 per cent) of private sector businesses were sole proprietorships, 27.7 per cent were companies and 9.8 per cent were partnerships. SME's accounted for 99.9 per cent of all enterprises, 58.8 per cent of private sector employment and 48.8 per cent of private sector turnover. SME's in the manufacturing sector are a significant part of this contribution, the Department of Business, Innovation and Skills' SME Statistics 2008 indicate that UK manufacturing SME's comprise 32,500 firms providing 1,195,000 jobs.
However, according to Jayawarna, Wilson and Homan (2003), most small manufacturing businesses, as part of a supply chain will be influenced by changes instigated by large partners at the top of the supply chain, which significantly affect the competitiveness of these smaller companies. Such changes include: the rationalisation of the number of suppliers; the introduction of lean production methods; Just in Time delivery and higher quality standards.
Always on Time
Marked to Standard
Managed by few people personnel
Managed by numerous specialists
Short term and reactive approach
Balance between short, medium and Long-term view and proactive approach
Informal management system resulting from low relative investment
Rigid and formal management systems
Decision making is centralised and semi structured, sometimes faster
Decentralised, structured decision making
Limited by scale
Benefits from scale
Predominantly operationally focused
A balance of operational and strategic focus
Corporate and public ownership with professional management teams
Command and control culture
People are empowered within scope of their responsibility
Certain level of flexibility in response to external changes
Organizational inertia can sometimes prevent quick response to change, rather use planned approach.
Use of manual/semi automated systems to support business/operational activities
Employ fully automated, state of the art systems to support business/operational activities
Source: Adapted from the BDO report 2012
A report by the G&E Capital research (2010), which looked at the management skills that were essential for SME's success, pointed out that IT related knowledge and skills are one of the key gaps identified, it estimated that around one third of manufacturing SMEs still have manual budgeting and stock control systems, whilst less than half use IT to control manufacturing processes, which include inventory management (BIS 2010). A report by IFM (2008) suggests that manufacturing SME's need a different approach to business compared to large companies, and interventions must be configurable to suit the SME's specific needs
The BIS (2010) report shows that UK manufacturers faced with increased competition from emerging economies, must continue to develop their productive capabilities in order to remain internationally and domestically competitive. The context in which manufacturing SME's operate often as part of global value chains and a much larger supply chain is swiftly changing due to a range of factors. These include: the scarcity of financial capital; the accelerating pace of technological change; resource scarcity (e.g. hydrocarbons, water, rare-earth metals); climate change; population growth and changing demographics; sovereign debt; reductions in public spending; changing economic balance of power; changing global industrial structures, supply chains and ownership (IFM 2008).
The BDO report (2012), points out that larger scale manufacturers traditionally have forecasted demand for their products into the future and then have attempted to smooth out production to meet that forecasted demand utilizing many software integrated systems for their B2B and B2C business activities (Koh and Simpson 2005). The big ERP system vendors, i.e. SAP, BaaN, ORACLE, usually support larger manufacturers (Tinham, 2002; Wiers, 2002; Worley et al., 2002). Such companies utilize such software in managing inventory as well as other company activities, while today's inventory management software has more computational power than ever segmenting inventory by profitability, calculating safety stocks down to the SKU level and other sophisticated features there are other inventory management challenges that need to be addressed. Koh and Simpson (2005), suggest that the implementation cost of such systems is very high, and with numerous implementation failures it is difficult to rationalize the costs and benefits of these systems to SME manufacturers. It is important to point out that SME's are not smaller versions of larger firms. Their resources, experience, needs and often their decision-making processes differ significantly from those of larger firms (Ozdenli and Bennett 2003; O'Regan, Ghobadian and Gallear 2006).
According to the BIS report (2010), small and medium sized enterprises (SMEs), may experience difficulties accessing knowledge of the latest industrial ideas, technologies and practices or finding professional support and advice on how these can be applied to their business. It suggests that there is evidence showing that many SME manufacturers are not aware of the economic and financial benefits of greater integration. It also states that the barriers to internationalisation are relatively greater for SMEs because resource barriers are higher due to their more limited supply of human and financial resources, expertise and contacts. Compared to larger companies, the percentage of small and medium manufacturing firms which export is relatively lower. Bititci, Maguire and Gregory (2011), state that managing manufacturing and business processes, including supply chain processes, efficiently and effectively is one of the needs lacking in SME manufacturers, inventory management practice is an essential part of these processes. Research by Laforet and Tann (2006) showed that SMEs in the UK manufacturing industry are similar to SMEs in other industries. According to the research, innovation in SME was based more around developing new ways of working than new product innovations. The use of systems/technology and process innovation was not uniform amongst less innovative companies. It stated that the main constraints of SMEs were customer dependency, skills and knowledge acquisition through training, poor learning attitude and networking because of their tradition of being inward-looking and autonomous.
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The G&E capital research (2010) on small and medium scale companies in the United Kingdom states that:
"Almost two fifths (39%) of companies see high raw materials and inventory costs as one of the three limitations to their success, making it the biggest concern among UK SMEs. The worry over costs was higher than fears over the decline in consumer and business spending in the UK, which 38% of SME's identified as a limitation to their success..." (G&E Capital research 2010).
Many SME's employ recently developed midrange and less complex systems. In conjunction with using such systems as a planning and control tool many SME's combine this with other execution concepts such as just-in-time (JIT) and optimised production technology (OPT).
Figure 3: Showing Inventory costs as a major concern for SME's in the UK
GE SME constraint on growth.bmpSource: G&E Capital Research 2010
Inventory management challenges are a vexing problem for manufacturers both large and small but more so in SME's as is seen in Figure 3, affecting operational efficiency, customer satisfaction and revenue. And, simply implementing inventory management software isn't necessarily a one size fits all solution to the challenges that it brings (Fernandez 2000).
Figure 4: Showing changes currently considered by manufacturing firms
AB G S2.png
A survey of 170 companies by the Aberdeen Group (2009) shows that many companies are actively re-evaluating their inventory management processes and technologies, as seen in Figure 4. It states that companies are facing key pressures that center upon inventory management, such pressures include: a need to improve return on investment capital, shortage of working capital to support operations/expansions and the improvement of service levels.
The following section will examine the current literature on inventory management, forecasting and the relationship and improvements that forecasting provides inventory management systems as well as inventory management effect on firm performance.
2.2 INVENTORY MANAGEMENT
Inventory management in Supply chain is an integrated approach to the planning and control of inventory throughout the entire network of cooperating organizations from the source of supply to the consumer. Inventory management in the supply chain aims to improve customer service, increase product variety, and decrease costs (Giannoccaro, Pontrantrandolfo and Scozzi 2003). Inventory management, in both academic literature and in practice has progressed through the years from a clerical, accounting and store management function to assume a strategic role, significantly affecting a company's working capital, operations and it's bottom-line. Over the years inventory management has attracted a lot of research by supply chain academics and practitioners. It has been researched in the areas of logistic management and operations research and management.
According to the literature, the most used inventory control policies in both large manufacturing companies and in small and medium manufacturing operations are continuous review and periodic review polices. The essential difference between continuous review policy and periodic review policy is the way in which orders are placed. The order placement process in continuous review policies is preceded by some events that require placement of new orders. In a periodic review policy, the order placement process is withheld until the review period. The decision as to whether to place the order or not is only taken at the end of the review period. Hence, this type of policy is advantageous for multiple-product supply chain where batching of orders can result in reduction of ordering (of raw materials) and transportation costs. Typically in a supply chain, for small and medium scale manufacturers raw materials are procured and stored in buffer inventory while finished items are produced in manufacturing centers, stored in internal finished products' inventory or stored in intermediate warehouses and then shipped to buyers or distribution centers (Diponegoro and Sarker, 2006; Mansouri, Gallear and Askariazad 2012).
Narayana (2010) states that an important determinant of the kind of inventory management practice employed by an SME manufacturer is the frequency of material purchase and use, in other words the policy it follows, the size of the company (as a larger company might adopt more sophisticated inventory management practice), and the type of production (machine shop/job shop/batch production). SMEs which follow these processes either do not have any inventory management practice or follow thumb rule/EOQ/ABC based inventory management practice. On the other hand those SMEs which have mass production/flow shop production processes adopt computerized JIT/VMI practices. Managing inventory levels for RMI, WIP, and FGI at different stock points is a complex task involving trade-off analysis between inventory cost, lead times and customer service level. Although carrying inventories is essential to enhance the customer service level and cut shortage costs, excess inventories are usually barriers to achieving high responsiveness and minimum operating costs (Demirli and Yimer, 2008).
Inventory is viewed by literature as both a financial asset and an operational risk to any business (Koumanakos 2008), and its management has become of critical importance to many businesses (Rajeev 2008). It is key to ensuring customer service and is a source of complexity in production. Efficiently managing it directly impacts cash flow, profit and loss accounts and, in turn, the overall balance sheet. Inventories exist as a result of the mismatch between supply and demand (Mcleavey and Narasimhan 1985). In manufacturing circles, inventory is typically understood as (1) the components used to build the finished product and (2) the finished product themselves. Inventory does nothing for the business until it is sold in exchange for a more liquid asset, such as cash. Until then, inventory is a cost (Aberdeen Group 2009).
Hedrick et.al (2008) describes inventory as visible, tangible aspects of doing business, suggesting that each type of inventory (RMI, WIPI, FGI) represent a large portion of business investment and must be managed adequately so as to maximize profits. Cannon (2008) and Mangan, Lalwani and Butcher (2008) however, describe inventory as forgone investment opportunities as a result of tied-up capital; ancillary costs incurred in moving, storing or otherwise simply handling inventory; or unsolved process problems that are concealed by the inventory. They argue that reductions in inventory would be viewed as evidence of successful management. Mangan, Lalwani and Butcher (2008) see inventory as a necessary evil and point out that inventory can never be reduced to zero as manufacturing companies must possess each type/level of inventory to cater to different needs at different stages of the manufacturing process and to ensure the firm functions. According to Hedrick et.al (2008), successful inventory management involves balancing the costs of inventory with the benefits of inventory. They state that many small business owners do not fully appreciate the costs of carrying inventory, which include not only direct costs of storage, insurance and taxes, but also the cost of capital tied up in inventory. Nevertheless, in spite of the disparity associated with inventories, they do have positive purposes. Raw material inventories provide a stable source of input required for production. A large inventory requires less replenishment and may lessen ordering costs because of economies of scale. Work In-progress inventories reduce the impacts of the variability on production and guard against failures that may occur in the manufacturing process. Finished goods inventories provide for better customer service. According to Baker (2010) in his research on the role of inventories and warehousing in internal supply chains, though inventories provide some protection against changes in the customer demand levels, there is genuine concern that it reduces a company's or supply chains ability to respond to changes in the nature of demand. As such inventories present a dilemma for many manufacturing companies acting as both a buffer against one risk and are the main source of another type of risk (Baker 2010).
It is observed from literature that pursuing appropriate inventory management practice is one of the ways of acquiring competitiveness among others, by effectively managing and minimizing inventory investment (Chung 2012; Rajeev 2008; Vastag and Whybark 2005; Koumanakos 2008). Inventory acts as a buffer between processes in the supply chain, without inventory the supply chain may encounter various challenges in meeting production targets and satisfying customer demand as a result of variability in supply and demand (Mangan, Chanrdra and Butcher 2008; Harrison and van Hoek 2011; Hedrick et.al 2008).
The researcher agrees with the assertion that inventory management can therefore be one of the crucial determinants of competitiveness as well as operational performance of SME's in inventory intensive manufacturing industries. Kerkfeld and Hartmann (2012), pinpoint excessive inventory levels as a major concern for SME's manufacturers, tying up funds that could be better spent on new product introductions, expanded marketing and sales, modernisation, reengineering, expansion programmes, new acquisitions and debt reduction. According to Kumaran and Ganesan (2011), inventory carrying costs is one of the major factors influencing supply chain performance. Inventory management systems according to the literature are usually considered under conditions of certainty and uncertainty (Gumus, Guneri and Ulengin 2010; Koumanakos 2008; Madadi, Kurz, Ashayeri 2010; Mangan, Chanrdra and Butcher 2008; Mcleavey and Narasimhan 1985; Harrison and van Hoek 2011).
The literature on inventory management recognises it as necessary for the proper conduct of business and the approach adopted by many manufacturing firms, as managing inventory affects the financial well being and long-term viability of a company. They maintain that a firm's decision regarding inventory can lead to its increased levels of inventory, cost and decreased service levels (Wallin, Rungtusanatham and Rabinovich 2006), (Koumanakos 2008), (Slack, Chambers and Johnston 2010), (Rajeev 2008). According to Slack, Chambers and Johnston (2010) inventories can be held for sensible and pragmatic reasons but it must be tightly controlled. They emphasize the need for the proper control and monitoring of the process of inventory in supply chain management. For progressive companies, good production and inventory control can be a competitive weapon. Research by Eroglu and Hofer (2011) reveals the importance of properly managed inventory on firm performance and details the impact of the different types of inventory (RMI, WIPI, FGI) on supply chain performance. The researcher observes that for a manufacturing company, the importance of differentiating inventory and understanding the risks, costs, benefits and impact of holding each on the supply chain is essential in determining the overall impact of the flow of inventory in the supply chain. Rajeev (2008) supports this assertion by stating that inventory costs are determined not only by their level of inventory but also by the time inventory spends in the system. Managing inventory efficiently requires an acceptable balance between costly excess inventories on the one hand and disruptive stock outs on the other (Willemain, Smart and Schwarz 2004).
Stanger, Yates and Cotton (2012), highlight the challenges faced in inventory management, as trade-offs, stock outs and excess on shelf availability occur due to ever changing customer demands and trends in a highly volatile market. The continued imbalance between supply and demand is an increasingly complex problem in manufacturing supply chains resulting in stock-outs, excess inventory, markdowns and increased disposal costs (Taylor 2006); (Simatupang and Sridharan 2002); and (Kaipa, Korhonen and Hartiala 2006). Hedrick et.al (2008), suggests that main objective for many inventory control methods is to determine the minimum possible annual cost of ordering and stocking each item. They state that two major control values are used: 1) the order quantity, that is, the size and frequency of orders; and 2) the reorder point, that is, the minimum stock level at which additional quantities are ordered. Chandra and Grabis (2005) argue that a reduction in the inventory replenishment lead-time allows reducing safety stock and improving customer service. Wallin, Rugtusanatham and Rabinovitch (2006) also view lead-time as an important inventory element. The researcher observes the need for a robust forecasting system to improve inventory control and management systems. The researcher observes that an increase in supply chain complexity also increases the difficulty company's face in trying to effectively manage inventories. The fluctuating patterns of demand and usage of customer companies due to external business environmental factors directly affects the scheduling/production and inventory management practices in a manufacturing company. As such the manufacturer makes trade-offs on improving service levels, reducing logistics costs and inventory holding costs/excess inventory and obsolescence. The observer sees the increased importance of accurate demand forecasting to enable the manufacturer cope with the randomness in demand and lead-times from customers.
However, in recent times a number of researchers revisited the theory of inventory management in supply chains and have proposed various new/hybrid models, systems and techniques for the effective management of inventory. Gumus, Guneri and Ulengin (2010), propose a multi-echelon inventory management model under random or fuzzy circumstances, that remedies several deficiencies such as the assumption that demand and lead-times are constant. They maintain that by building accurate forecast data and realistic cost figures into general tree structure supply chains, the elimination of several deficiencies will be realized. Madadi, Kurz, Ashayeri (2010), attempt to minimize inventory costs by considering transport costs as part of it and suggest a model which integrates inventory and transportation management into one mathematical model based on practical application. Sucky (2004), suggests an integrated joint inventory management policy between buyer and supplier (in this case the SME and its larger supply chain partner) using Joint Economic Order Quantity and Joint Economic Lot size Formulas and requiring the coordination of the order and production policy between the buyer and supplier respectively. However, he maintains that it is very difficult to estimate buyer's holding and ordering costs unless the buyer is willing to disclose the true values of his cost structure. Chung (2012) and Wadhwa, Bibhushan and Chan (2009), support both Sucky (2004) and Madadi's et. al. (2010) assertions by proposing an integrated buyer-supplier inventory model which also considers transportation costs. Wadhwa, Bibhushan and Chan (2009) in their research on inventory performance and inventory policies found that the inventory policy, that was most beneficial for one node resulted in overall poor performance of the supply chain. Moreover, the inventory policies within a supply chain that take previous demand information tend to magnify and distort the actual demand variations. This suggests to the researcher that the integrated or coordinated inventory management is a valuable option that can improve the supply chain operations as well as that of the small and medium scale manufacturer enabling them to better plan, schedule production and operations. It also shows that the inventory policies that are best for one supply chain node may not necessarily be the best for the other partners in the supply chain (Wadhwa, Bibhushan and Chan 2009) and stresses the need for SME's to consider integrating their operations with their larger supply chain partners.
However, Emery and Marques (2010) raise concerns about the integrated model pointing out that it will have different effects on different levels of inventory and their transaction costs. They state that a customer/supply chain partner may substitute its own storage related management/production costs for the suppliers when they integrate into the suppliers business by acquiring its finished goods inventory. This presents the subject of where on the supply chain should inventory be located, which more often than not is with the SME's and rather than their larger supply chain partners. For many SME manufacturing companies the desired solution is an appropriate inventory control policy that will guarantee a satisfactory service level without keeping unnecessarily large inventories that are costly and difficult to handle (Nenes, Panagiotidou and Tagaras 2010). Williams and Tokar (2008), in reviewing inventory management research stressed that the collaborative inventory management programs, such as VMI, RFID, CRP and others have been difficult to implement and this lack of success in implementation may be attributed to the absence of business processes to integrate the additional information provided through such programs into decision-making processes. Auramo, Kauremaa and Tanskanen (2005) advocate for the use of information technology in inventory management to assist in keeping inventory lower and reduce holding cost and make ordering more efficient. The researcher agrees with the above scholars and sees that it is important for SME's manufacturers to find methods to reduce inventories without compromising the production process and without increasing costs. And though an integrated system may offer many benefits to the SME manufacturer, it may also saddle it with most of the inventory risk on the supply chain.
As with many SME manufacturers who maintain a different level of inventory, the coordination of two channels of communication rather than one presents a challenge. They must consider supplier-to-materials management and materials management-to-production instead of supplier-to-production. The extra communication channel is costly and risks the loss of important information about product design, product quality and production scheduling that is valuable to the customer and its supplier (Emery and Marques 2010). Nenes, Panagiotidou and Tagaras (2010), state that the archetypal trade-off in inventory management is between high holding and obsolescence costs of too much stock on one hand and poor service and high shortage costs resulting from low inventory levels on the other. This difference stems from the inability of SME's to adequately integrate their systems with that of their larger business and supply chain partners, and manage the imbalance that exist between demand and supply.
However, Niemi, Huiskonen and Karkkainen (2009) and Nenes, Panagiotidou and Tagaras (2010), suggest that such might not always be the case but rather an inability to transform and adapt current practices, they point out that despite all the theory available, the inventory management techniques used in SME's are often very elementary. They suggest that one of the possible reasons for such lack of implementation is that the benefits of inventory management techniques are not clearly seen and/or the techniques themselves are perceived as difficult to learn and use. If the benefits cannot be demonstrated, it is economically reasonable for an SME manufacturer to prioritize other more profitable development projects, especially with limited capital and resources. Nenes, Panagiotidou and Tagaras (2010) suggest that barriers to successful inventory management are organizational, not technical. They argue that though technological solutions and various analytical tools exist, what is needed are new organizational solutions, i.e. redesigning of processes and revising of measurement and incentive schemes to promote the utilization of these new technologies. Niemi, Huiskonen and Karkkainen (2009) support this argument stating that the failure to see these organizational and managerial aspects is one of the main reasons for the slow adoption of supply chain and inventory management techniques in many SME companies. They state that a serious challenge for academic researchers is to provide new means of transferring research-based management knowledge into practice. The researcher agrees with this assertion and has observed that despite the wide range of research on inventory management, basic problems persist in manufacturing companies. However Pong and Mitchell (2012) assert that despite mixed opinions on the issue of inventory management, the possession of inventory has both pros and cons which are dependent on the circumstances of the individual firm and are likely to differ in large, small and medium scale companies. Research by Narayana (2010), revealed that modern inventory management practices are largely absent among SME's, even in an inventory-intensive manufacturing industry. Raymond (2005) highlighted 5 critical success factors for SME manufacturers in a Build to Order Environment, which includes inventory management. He notes that prevalent industry manufacturing applications such as computer based inventory and production scheduling systems have been implemented in less than a one third of the manufacturing firms surveyed. The researcher observes that the advanced software technology application such as MRP, MRP II ERP are not prevalent in SME's indicating that these systems are not properly assimilated by SME's and are problematic for them.
In general, inventory remains a substantial business asset and impacts directly on customer service. It can be seen that the present problem concerning supply chain and inventory management is not lack of data and tools, but rather lack of knowledge of how to use them; this is more evident in SME manufacturing companies. To manage and understand most of these models efficiently, a background in statistics or math is needed, and it is very hard to find personnel with such in an inventory related field especially for SME manufacturers. Many of the software models are difficult to understand and implement because of the mathematical and statistical base they are set upon.
2.3 DEMAND MANAGEMENT/FORECASTING:
Planning for demand enables manufacturing companies ensure that they can produce and/or supply products at the needed time and at an acceptable cost. Forecasting aims to ensure that there are enough goods to sell, but also to make sure, based on sales cycles, inventory turns, and other measurements, that inventories are replenished as needed (Haines, 2008). Demand forecasting is essential for most organizations as it is a necessary source of information for the production and operations management and for other business functions as well. An accurate demand plan will help to deliver product within customer lead times, deploy the right quantity of the right product, make sound operational decisions, ensure financial planning reflects reality and optimize inventory adequately. On the other hand, poor demand plans lead to poor customer service, excess inventory, excessive production changes, and increased distribution costs (Gattorna 2003).
Demand management or Forecasting is a necessary assumption for most inventory control. An absence of customer demand estimation will result in uncontrolled inventory and increased shortage costs. As a supply chain strategy, it evolved from early work on demand amplification by Forrester (1958) and Burbidge (1961) (cited in Taylor 2006). It covers the processes for sales and operations planning, sales forecasting and finished goods inventory deployment planning (Rexhausen, Pibernik and Kaiser 2012). In a generic sense, it is the ability of a company to understand customer demand and requirements and balance them against the capabilities of the supply chain. They are a set of practices aimed at managing the demand chain starting with the customer and ending up at the raw materials supplier. Forecasting models are distinguished by the time frame they forecast: immediate, short-term, medium-term, and long-term. For a small and medium scale manufacturer that is part of a supply chain, production scheduling is very sensitive to inventory levels and control, and as such the importance of forecasting demand for products (either sub assemblies for larger partners or finished goods) for sale to customers cannot be understated.
Forecasts are used for many purposes; marketing, sales, finance/accounting, production/purchasing, and logistics. In this paper, the focus is on forecasting from the perspective of production planning and inventory control. Previous literature on demand management suggests that its core aim is to enable companies serve customers better by understanding their demands, and facilitating interactive/collaborative relationships to create added value for the customer (Kaipa, Korhonen and Hartiala 2006; Klassen and Rohleder 2002). Several researchers opine that the effective management of demand uncertainty significantly improves a company's performance. Liu, Shah and Schroeder (2010), suggest that it would aid manufacturers in make to stock, mass order processes by improving their mass customization ability. They suggest that demand management will increase or improve collaboration with suppliers and other partners of the supply chain. A fundamental aspect of supply chain management and one that is essential to the management and optimisation of inventory is accurate demand forecasting. Increasing the demand forecasts ability to synchronize with sales, marketing and operations also increases the influence it has on the supporting functions and company processes (procurement, manufacturing, and distribution). If these functions are out of sync with the rest of the organization, operational inefficiencies will develop, resulting in the misuse of valuable financial and human resources (Haines, 2008).
Research by Walters and Rainbird (2004) outlines the demand chain processes and activities, highlighting their importance to the value chain, also stating that too much emphasis has been placed on the supply chain and not enough on the demand chain management. Walters (2006) cites the success of 7-eleven Japan whose demand-led management practices kept company performance high during a recession. Hilletofth, Ericsson and Christopher's (2009) research of a Swedish manufacturing company, shows similar results with a focus on demand management and fulfilment leading to improved performance.
Adebanjo (2009), Mentzer (2006) and Walters (2006) discuss the inter-relationship and importance of forecasting suggesting that forecasting consumer demand is requisite for successful demand management, as a failure to do so accurately will result in inventory imbalances and lost sales. Allied to this school of thought, Williams and Tokar (2008) maintain that the assumption of demand uncertainty plays an increasingly important role in collaborative inventory management. Ketzenberg (2007) supports that assertion by stating that the value of information in managing inventory replenishment models depends on the degree of demand uncertainty assumed. Taylor (2006) suggests that a reduced variability in upstream demand will significantly benefit companies in terms of reduced inventory. Willemain, Smart and Schwarz (2004), argue that intermittent nature of demand makes forecasting very difficult, especially for companies in slow moving items who require sales forecasts for planning purposes.
Despite the fact that demand management is a familiar concept in supply chain literature, it remains underutilized in many industries including manufacturing (Holweg et al., 2005; Geary et al., 2006; Taylor 2006). Frohlich and Westbrook (2002) support this assertion stating that though demand management is a potentially powerful weapon, industry use of it or lack of use has led to many unanswered question regarding its practicability. This may be due in part to the wide range of activities that need to be coordinated for its successful implementation (Lambert 2008). Willemain, Smart and Schwarz (2004), argue that in order to be efficient, inventory management models require accurate forecasts of the entire distribution of an aggregated quantity; including cumulative demand over the fixed lead time required to receive replenishment orders. This suggests that forecasting is essential for an inventory management system to function adequately.
Despite its importance, it is essential that forecasts be accurate, research by several scholars (Kerkkanen, Korpela and Huiskonen 2009; Ali, Boylan and Syntetos 2011; Mentzer and Moon 2005) show that forecasting inaccuracies result in inventory spillovers and overproduction or shortages and underproduction, increasing cost (either for warehousing space for excessive inventories or for reduced customer service levels). According to Frohlich and Westbrook (2002), distorted or inaccurate demand information from one end of the supply chain to the other can lead to increased inefficiencies, such as poor customer service, lost revenues, ineffective transportation, misguided capacity plans, missed production schedules and excess inventory investment. Taylor (2006), also states that the attention to the management of demand significantly affects cost by avoiding overproduction, resulting in less product waste, improved customer service and reduced finished goods inventory by providing a platform for more efficient processes.
Figure 5: Showing the Forecasting process and its relationship with manufacturing and inventory management.
Sales and Operations Planning
Materials Planning/inventory management
Source: Klassen and Rohleder 2002
Demand patterns play an important role in forecasting; the type of demand (Cyclical, Seasonal, Trend or Random) has a significant effect on the forecasting, sales and operations planning process, and determines the range of the forecast. For an SME manufacturer, being a member of a supply chain and a partner to a larger organisation would have to respond to the demand patterns of that organisation (Narayana 2008). Many companies make the mistake of using short-term forecasting methods to predict longer-term growth. Ali, Boylan and Syntetos (2011) in their research on forecast errors and inventory performance, state that most research on inventory ignore forecasting and simply assume that demand patterns are stable, and distribution and its other parameters are known thus leading to forecast inaccuracy and errors which affect to other operations. Mentzer and Moon (2005), suggest that measuring forecast errors improves forecast accuracy, but simply measuring forecast errors on a general level does not provide enough information for setting targets for forecast accuracy and finding development areas in demand management.
Table 2: showing Planning horizons
Source: Adapted from Adebanjo 2009.
Issues affecting demand forecasting:
In many organizations and most notably SME's forecasting is perceived as being organizationally tied to one functional area and its bias is increased especially when that area influences forecasting its process (Moon 2006). In other words, forecasters might over or underestimate the real need in order to gain in another aspect of the business and such assumptions result in a situation where the resources are tied in to wrong places and can have differing impacts on the company such as a decreased profit margin due to the over production and an increased number of unsatisfied customers due to inefficient deliveries. Additionally, ripple effects can occur arising from other partners in the supply chain distributing independent forecast through an integrated network. Mis-information transmitted from downstream to other parts of the supply chain or from the upstream will invariably affect inventory positions of all partners involved (Adebanjo and Mann 2000). Moon (2006) highlights some issues that affect forecasting in organization as seen in the list below:
Insufficient resources (human, system or financial) dedicated to the forecasting system.
Forecasting lacks adequate organizational clout to implement process change
Lack of integration between forecasting system and other upstream and downstream systems resulting in manual transfer of data.
When forecasting is done by people who use different tools, make different assumptions and access different sources of data
Over-relying on excel as a forecasting tool
The inadequate training for forecast personnel, as they need detailed instruction on how to perform statistical analysis of historical demand as well as understand the goals procedures of overall forecasting process
Forecast improvement cannot be achieved by technology alone.
According to Spedding and Chan (2000) some of the problems with the adoption of advanced forecasting systems (e.g. Bayesian dynamic linear time series model) are that the software is not readily available in the market as it is not a well-established method, it requires subjective judgement or input of the experts, the mathematics of modelling obstructs the understanding of the forecasting technique, the maintenance of the forecasting model requires a certain level of expertise and the solution obtained may not be optimal. Research by Holly and Turner (2001) suggests that financially stronger firms can adapt easily to changes in demand than smaller firms by altering their production pattern.
Moon (2006), suggests that the strategy of organizations must understand the importance of accurate forecasts and how these forecasts can positively affect the supply chain. Helms, Ettkin and Chapman (2000) discuss the benefits of integrated forecasting sighting collaborative forecasting as an approach that eliminates the isolated analysis methods and opens the supply chain's information flow to the benefit of all partners. By forecasting within an integrated framework organizations can create compact processes which communicate its demand forecast. The researcher observes that the need for accurate forecasting integration is more urgent in small and medium scale manufacturers as with their financial strength they cannot adapt to radical changes in demand patterns
2.4 COLLABORATION AND DEMAND MANAGEMENT:
Several researchers agree on the conception that collaboration both internally and externally are important to improving a company's customer service and integration as it coordinates operations with stakeholders in the supply chain using such collaborative approaches like continuous replenishment CRP, efficient consumer response (ECR), quick response (QR), and Vendor managed inventory (VMI), all designed to better balance demand and supply and thus better manage inventory (Dong, Xu and Dresner 2007; Yao and Dresner 2008; Cachon and Fisher 2000). If an operation can adequately match the rates of supply and demand, it will succeed in reducing its inventory (Slack, Chambers and Johnston 2010).
The coordination of decisions among supply chain members is critical to the performance of supply chains. Coordination may be facilitated by some form of information sharing (Ali, Boylan and Syntetos 2012). Research by Ali, Boylan and Syntetos (2012), shows that Forecast Information Sharing (FIS) has a great potential to improve the forecasting accuracy for a two-level supply chain; however, this improvement depends on both the demand process and the demand parameters. This translates to inventory savings across the supply chain and especially for the manufacturer, as shortages of any type of inventory will lead to a disruption in production. For example, if there is a shortage of RMI it automatically reduces WIPI and as such FGI and vice versa. Inaccurate demand and inventory information also affect the optimal order quantities determined by the manufacturer, thereby reducing the profits of all partners (Marques et.al 2010).
Fliedner (2003) suggests that by implementing CPFR retailers and manufacturers can attain increased forecast accuracy, reduced stock-outs, increased sales, and reduced inventories. Fildes etÂ al. (2009) supports this assertion and presents a similar list of benefits. However, he also lists obstacles to forecasting collaboration: a lack of trust in sharing sensitive information, lack of internal forecast collaboration, availability and cost of technology or expertise, aggregation concerns (number of forecasts and frequency of generation), and fear of collusion. Such obstacles exist and are a major hindrance to supply chains, especially to those in a multi or two echelon supply chain as most SME manufacturers are. The inability of the supply chain partners to share information will possibly lead to an increased bull whip effect. Wang et.al (2011) in their research on improving inventory effectiveness suggests that to lessen the impact of the bullwhip effect the sharing and exchanging of information is necessary among supply chain partners. According to Lee (2010), increasing visibility of the supply chain is necessary to avoid being misled by the distorted demand signals, and thereby counter the bullwhip effect. Hosoda et al. (2008) discuss a potential impact of the market demand mis-specification on the benefit of information sharing, suggesting that market demand mis-specification affects the degree of the benefit coming from the information sharing strategy, and in an extreme case can eliminate this benefit. Research by Sari (2008) on inventory inaccuracy and its impact on supply chain collaboration revealed that inaccurate inventory information had a significant effect on the performance of collaboration initiatives between supply chain partners, with its influence increasing as the companies increased collaboration. Richey et.al (2009) supports this assertion stating that firm performances increase when supply chain partners cooperate to confront unpredictable customer demands. In order to cope with increasing uncertainty of both demand and supply, Comez and Kiessling (2012) suggest that it is crucial for firms to make demand and supply chain decisions via cooperation.
However, Kerkkanen, Korpela and Huiskonen (2009) state that there is a beneficial effect in sharing end-customer demand information with higher echelons for planning inventory and such effect is dependent upon inventory policy and lead time. Liao and Chang (2010) support the assertion stating that the sharing of demand information between supply chain partners reduces inventory cost. Cannella, Ciancimino and Framinan (2011) in their research on inventory policies and information sharing in supply chains, suggest that the real-time sharing of market demand, of information on inventory levels and in-transit items strongly supports the reduction of the bullwhip on dampening effectiveness. Highly collaborative supply chains that create an environment for adequate information sharing presents an opportunity for a win-win solution for all supply chain partners. Lee (2010), states that taming the bullwhip requires collaboration; the collaborative efforts enable the company to move forward faster and more effectively as market demands improve. This however, requires hard work, understanding true causes of demand, gaining visibility, and investing in collaboration with partners.
2.5 DEMAND MANAGEMENT AND INVENTORY PERFORMANCE:
According Smaros (2007), effective inventory management goes hand in glove with demand forecasting, requiring a symbiosis of the demand planning and inventory management systems in an organisation. According to Koh and Simpson (2005), the supply chain competitiveness between supplier and customer (in this case the SME manufacturer and its larger Supply chain partner) relies on how effective and efficient the order and information is being handled between the parties in the supply-chain. Murky supply chain visibility, poor forecast management techniques and reliance on inflexible systems all contribute to frequent stock outages, increased inventory costs and poor customer satisfaction. Heightened visibility of material requirements result in a better allocation of resources and more efficient inventory planning and safety stock. Smaros (2007) suggests that the value of forecasting collaboration increases when the explainable portion of demand variability increases and that the choice of best inventory control approach depends on which supply chain partner posses more explanation power. Research shows that simpler inventory policies are better prepared to dampen or even reduce the impulsive demand fluctuations. According to Kerkfeld and Hartmann (2012), the rapid advancement of information systems enhances visibility and strengthens collaboration along the supply chain which results in increased flexibility, more reliable delivery, and reduced inventory. Closely tied to improved visibility and collaboration is the internal and external performance measurement. However, such systems may not be accessible to smaller partners of the supply chain and their process may not be able to assimilate or integrate such (Narayana 2010).
In practice, forecast and demand information often enters the planning process in many phases and from various sources. In addition, in real life forecast errors are not necessarily random, but may include systematic characteristics that are specific to each company. (Kerkkanen, Korpela and Huiskonen 2009), suggest that production planning, inventory management, and forecasting should be understood as a whole, before targets are set for an individual part of it. The researcher deduces from the above that an accurate and effective implementation of demand management and its practices will positively affect inventory management and improve inventory performance. With the aid of forecast data, inventory review should be carried out based on detailed study of the sales data, demand pattern and order cycles. An understanding of the business and sales cycles specific to a product category aids the improved management of inventory (Chamber, Mullick and Smith 1982). The researcher agrees with this assertion as this helps identify those stocks which are required to be managed at a smaller scale, while also identifying those high value and swift moving goods that always need to be available to avoid stock out situations.
However, the researcher observes that despite the importance of integrating forecasting with inventory management systems, forecasting errors remain a problem for many manufacturing companies, as errors on forecast data and demand variability can lead to excessively high production and inventory holding costs or underproduction and unsatisfied customer demand and possible reduction of customer base (Gupta, Maranas and McDonald 2000). Different types of forecast errors cause different kinds of impacts in production planning and inventory management. One of the biggest hurdles faced by forecasting is that it is largely subjective in nature as assumptions are drawn which seldom involve quantitative fact. Sanders and Ritzman (2004) explore the relationship between forecast errors and organizational performance measures. They reveal that the impact of forecast errors is not constant but varies upon organizational characteristics. According to Chopra and Meindl (2001), measuring forecast accuracy serves two main purposes; it aids managers in the use of error analysis to determine whether the current forecasting method used in a manufacturing company predicts the systematic component of demand accurately and takes appropriate corrective action. It also helps managers estimate forecast error and develop a contingency plan to cater for such an error. Research shows that a positive relationship exists between accurate forecasts and performance in inventory levels, replenishment costs and service levels (Gardner 1990; Chen et.al. 2000). However Liao and Chang (2010) suggest that there is negligible or minimal correlation between forecasting errors and inventory costs, they also state that the best demand forecasting method for minimizing inventory cost is dependent upon the inventory policy used by the company and lead time. Syntetos, Nikolopoulos and Boylan (2010) highlight that the demand forecasting performance and accuracy does not relate directly and had little influence on the efficiency of an inventory management system. Research by Acar and Gardner (2012) on forecasting method selection supports this stance stating that there is no relationship between operational performance and average forecast accuracy across products in a supply chain. However, they highlight that the consequences of forecast errors are complex as a result of the influential interactions between products competing for the same production capacity.
Kerkkanen, Korpela and Huiskonen (2009), state that analyzing the impacts of forecast errors requires the defining of the planning flow between forecasts and sales, and an analysis of the role of demand information in the planning process. In addition, how the forecasts are produced must be understood, questions such as; what are the most substantial sources of errors and how can these sources can be affected; must be answered. Research by Xie et al. (2004) and Zhao and Xie (2002), show that forecasting errors have significant impacts on the total cost, schedule instability, and system service level. Companies should assess forecasting accuracy in terms of its impact on business performance (Mentzer and Moon 2005).
Research by Syntetos, Nikolopoulos and Boylan (2010), on judgemental adjustments shows the existence of the potential to improve forecast accuracy over longer horizons, resulting in improvements which translate to substantial inventory reductions showing that such investments may reap considerable financial returns. According to Ali, Boylan and Syntetos (2012), one major factor which an organization must consider when attempting to improve its forecasting system is the use of measurable and meaningful forecast accuracy measures that can be linked with costs.
Table 3: The impacts of forecasting inaccuracy on firm activities including inventory.
Uneconomical use of capacity
Increased inventory holding cost
Lost sales cost
Source: Adapted from Kerkkanen, Korpela and Huiskonen 2009.
The literature shows that many researchers have proposed and expanded many models to improve forecasting accuracy and reduce errors. According to Kerkkanen, Korpela and Huiskonen (2009), an abundance of forecasting techniques exists and it often seems that too many techniques are available, so that the choice decision can border on an overload of information. Forecasting demand for products can have its drawbacks which include large inventories, long production times, high defect rates, production obsolescence, inability to meet delivery schedules, and (ironically) high costs (Panzuto and Rodrigues 2011).
According to Chamber, Mullick and Smith (1982) the selection of a forecasting method depends on many factors such as, the context of the forecast, the relevance and availability of historical data, the degree of accuracy desirable, the time period to be forecast, the cost/ benefit (or value) of the forecast to the company, and the time available for making the analysis. Forecasting with reference to a particular product requires the consideration of the stage of the product's life cycle for which it is making the forecast. The availability of data and the possibility of establishing relationships between the factors depend directly on the maturity of a product, and hence the life-cycle stage is a prime determinant of the forecasting method to be used. The researcher observes that for most manufacturing companies forecasting is a very important factor for inventory management and improving forecasting consequently improves inventory planning and control. However, research on forecasting practices among SME manufacturers is still an area where academic literature is scarce.
2.6 INVENTORY MANAGEMENT AND FIRM PERFORMANCE:
Supply chain literature suggests that competitive advantage and performance results are a consequence of firm-specific resources and capabilities (O'Regan, Ghobadian and Gallear 2006). The core of the resource-based view is that firms differ in fundamental ways as each has its own bundle of resources (Grant, 2002: 139; Fleisher and Bensoussan, 2003). According to Hawawini et. al (2003) the erratic nature of performances among companies within an industry suggests that the RBV perspective may not necessarily be the only determinant of competitive advantage but will also include the firms ability to position itself in way that will enable it take advantage of opportunities within and outside itself. Fleisher and Bensoussan (2003: 208) state that the source of competitive advantage within a firm is often multifactorial in that it usually cannot be attributed to only one type of resource. They suggest that it is the interaction between the different types of resources that drive a firm's competitive advantage. As such inventory performance and its impact on manufacturing and overall firm performance can be seen as a contributor to a firm's competitive advantage.
The empirical evidence suggested by literature on the value of inventory reduction in terms of its impact on firm performance has been mixed. A review of the literature reveals that two major views on inventory and firm performance emerge. The first sees inventory, though at times necessary, as fundamentally a driver of costs that manifest themselves in, forgone investment opportunities as the result of tied-up capital; ancillary costs incurred in moving, storing or otherwise simply handling inventory; or unnoticed or unsolved process problems that are concealed by the inventory. According to this view, efficient reductions in inventory would be seen as proof of successful management (Cannon 2008; Demeter 2003; Fullerton etÂ al. 2003; Chen et al., 2005; Mangan, Chanrdra and Butcher 2008). The second standpoint on inventory and performance sees it as management/operations decision among many options available to balance capacity with demand and thus perceives no essential relationship between inventory and firm performance. That is, in this view the systematic reduction of inventory would be interpreted as simply a shift in resource usage, with the firm choosing to confront its fundamental challenges with a different resource blend, of which inventory is only a part (Demeter and Matyusz 2011; Vastag and Whybark, 2005; Hedrick et.al 2008; Koumanakos 2008, Rajeev 2008).
Though it is not easy to show a direct connection between inventory management and the firm's performance there is some evidence according to literature that suggests a positive relationship in the long perspective that may lead to financial and manufacturing performance. However, besides direct financial benefits, it has been claimed that the adoption of inventory management techniques may have positive side-effects, sort of a ripple effect on other activities and operations as well as other management techniques, which in turn may lead to better performance (Vastag and Whybark, 2005; Chen, Murray and Owen 2005). Stapleton et.al (2002) support this assertion stating that reducing inventory holding costs through improved inventory management improves company profitability. Cannon (2008) argues that improvements in inventory performance could lead to the direct benefits of reduced capital requirements and the indirect benefits of process improvements such as; reductions in setup or ordering costs, that lead to reduced inventory requirements. However he also states that, substantive changes in inventory performance could be merely a remix of resource usage, as firms vary in their usage and value of inventory with little or no performance consequences. Vastag and Whybark (2005) support Cannons assertion arguing that the impact of inventory turnover on a manufacturing firm's performance is minimal, thus indicating that inventory management does not play an anchoring role in cumulative company capability. The researcher observes that it would be ideal to develop approaches which could show the improvement gap as well as the improvement path for adopting inventory management techniques.
Fullerton etÂ al. (2003) detect a significant positive effect of lower inventory levels on ROA and ROS in their survey, whereas Vastag and Whybark (2005) find no significant overall relationship between inventory turnover and firm performance. Research by Demeter and Matyusz (2011), show that firms applying lean practices have a higher inventory turnover than those that do not rely on lean techniques. They argue that lean manufacturing practices usually fit with line production systems and are not really applicable to job/machine shop environments as can be found in most SME manufacturers. They maintain that the use of Lean manufacturing practice in line production improves inventory turnover than with job shops. On the other hand Hofer, Eroglu and Hofer (2011), raise concerns over Lean manufacturing inventory practice and suggest that there is an optimum level of inventory leanness beyond which firm performance deteriorates. According to Pong and Mitchell (2012), the adoption of lean, JIT and other syste