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Challenges of Obtaining Accurate Inventory Forecasting

Distribution channel management is increasingly experiencing challenges due to changes in both domestic and global markets. Choose any one (1) challenge listed below. Briefly discuss the operational impact of the challenge and it may be eased or resolved.

Inventory forecasting is an important aspect of the channel of distribution management that presents the possibility of predicting the inventory behaviors throughout the distribution channel. Inventory forecasting relay on the customer, store and channel partners data obtain from the records that are then used in predicting the demand and purchase abilities of customers along the distribution channel. One challenge remains on the accurate obtaining of this forecasting data and accurately predict the inventory responsiveness throughout the distribution channel. This challenge has a high operational impact on the company's warehousing and distribution channel management. The following paper explores the operational impacts of accurate inventory forecasting challenge and ways to resolve this challenge.

The operational impact of challenges in obtaining accurate inventory forecasting is great within the distribution channel. Some of the operational impacts of the challenges are discussed below.

Channel Inventory management

Inaccurate inventory forecasting has an operational challenge in the management of inventory within the organization as the organization is unable to accurately predict the demand within the distribution channel. Inventory management problems arise to due to the control of the inventory that is beyond the company's operational boundary and this may result to a number of inventory issues within the distribution channel (Babai, Ali & Nikolopoulos, 2012). Firstly, the inventory forecasting may create an unnecessary cost on the price of goods due to many errors in setting the inventory. Secondly, pricing degradation is another operational challenge that may arise due to inaccurate data of inventory forecasting. Thirdly, product returns or write-off is another likely occurring challenge that may highly cost the business with inaccurate inventory focusing (Romeijnders, Teunter & Jaarsveld, 2012).

Overpayment of Channel Incentives 

The challenge of obtaining inaccurate inventory forecasting within the distribution channel has an impact on the company's operation since the strategy may result in overpayment of the incentives. Incorrect booking of products sales and errors payment of commissions are some operational issues that result from obtaining inaccurate inventory forecasting as the company relies on the incorrect data (Teunter, Syntetos & Babai, 2011). In addition, channel partners to channel partners inventory transfer issues form part of this operational challenge and may also result in multiple sales bookings. Lack of visibility of the inventory forecasting data and all the errors within this strategy often result in overpayment of channel incentives resulting pure margin erosion annually. Continuous overpayment of commissions and incentive is detrimental to the business as the challenges may result in integrity issues on part of operational management leading to failures and job loss (Poluha, 2016).

Operational Impacts of Obtaining Inaccurate Inventory Forecasting

Suboptimal channel pricing, incentives, and forecasting 

The visibility errors that result from obtaining accurate inventory forecasting data is associated with the inability to optimally price products within the distribution channel specifically products within the same family. Inaccurate data used in inventory forecasting is normally connected to problems of the setting of advantageous marketing focus. For instance, when the company wants to set special inventory price for channel partners, the problem arises on inaccurate pricing since that data does not give the real picture of inventory within the distribution channel. This makes it difficult to make a reasonable judgment on the appropriate inventory price to approve as price protection (Fildes, Goodwin, Lawrence & Nikolopoulos 2008). Moreover, inaccurate obtaining of inventory forecasting of the distribution channel is also making difficult for the company to reflect on accurate actual selling price and accurately calculate gross margin (Kolassa, Schutz, Boylan & Syntetos 2008). Poor focus on the distribution channel makes it difficult to maintain the stock at an optimum level throughout the distribution channel annually.

Poor financial reporting and compliance issues

Financial reporting is another operational issue that is presented by the challenge of obtaining inventory forecasting within the distribution channel. A good financial report as an operational requirement involves using proper and accurate inventory data (Krajewski, Ritzman & Malhorta, 2013). Poor inventory predictions reduce the ability of the company to complies with the financial reporting especially sale within the distribution channel.  Those companies with strict audit rules and compliance requirements have the problem of financial reporting as they can rely on inaccurate data obtained from inventory forecasting (Snyder, Ord & Beaumont, 2012).  This, therefore, is prone to penalties associated with poor financial reporting on the operational cost of business when inventory forecasting is used within the distribution channel. Inaccurate reporting has an impact on the overall performance of the business and may result in serious operational and integrity issues leading to loss of credibility in the sight of investors.

Channel partner responsiveness and payment delays

Another impact posed by challenges of obtaining accurate inventory forecasting data within the distribution channel is channel partners’ responsiveness and this may also due to delayed payment. The business normally competes with other rivals within similar distribution channel and especially to obtain the goodwill of channel partners. Within the distribution channel, channel partners represent those firms that are somehow connected to the customer and their influence on customers preference of a product is immerse (Disney & Hosoda, 2008). Delays in payment of these partners may demoralize partners leading to low poor channel responsiveness and this is important in customer’s preference and order of product. Companies with inaccurate inventory forecasting data tend to delay in a payout of channel partners leading to these anomalous behaviors of channel partners within the distribution channel. For instance, unhappy channel partners may discourage customers from purchasing a product from those companies with delayed commission payment leading to low customer orders and subsequent operational cost increases (Catt, 2007; Syntetos, Boylan & Disney, 2009).    

Ways to Resolve the Operational Impact of Inventory Forecasting Challenges

To resolve the operational impact accuracy challenge of inventory forecasting, there are some strategies that can be applied to the company’s inventory system.

Visible inventory data

There is need to have store-level and customer-level visible data used inaccurate inventory forecasting. Accurate obtaining of inventory data entails clear and visible data both at the store level and at the end consumer level. These data will assist in giving the necessary and accurate prediction of the behavior of inventory along the distribution channel. In addition, the data from store and customer give the true picture of nature of distribution channel and any expected challenges (Petropoulos, Makridakis, Assimakopoulos & Nikolopoulos, 2014).

Product mix

The product mix is another way to reduce or resolve challenges of inaccurate inventory forecasting that heavily lies in focusing on single product throughout the distribution channel. Product mix creates inventory balance on the distribution channel making the marketing better along the distribution channel. Diversification of products along the distribution channel eliminates the possible failures in accounting for error of the inaccurate inventory forecasting since the failure of one product is offset by the performance of another product within the same distribution channel (Lewis, 2011; Syntetos & Boylan, 2008).   

Implementation of inventory management computer systems

Many firms have diversified their business model to apply high technological inventory monitor systems that ensure there is accurate data along the distribution channel. Incorporation of the online version of the product distribution channel in the current company distribution system as enabled by e-commerce software is a good recipe for efficiencies (Lockard, 2010). Computer automated inventory shows some possibility of obtaining accurate data that enable accurate inventory forecasting along the distribution channel.  This is due to the ability of the system to retain customer and store data that are then used in making a right prediction of inventory behaviors. In addition, inventory management system enables clear analysis of channel partners and possible responsiveness of these channel partners following a particular action by the company (Kourentzes, Petropoulos & Trapero, 2014).

Inventory management system aid reporting since the data that are retained within the system is used in financial reporting. Automated inventory management system offers real-time inventory analysis that is also used in the financial report of the business operation. This tackles the issue of poor financial reporting and compliance with the organizational requirement (Lu, 2014). In the event of inaccuracy of reporting, the inventory data can be retrieved and used as supporting evidence for the integrity of reporting by company operational department. In addition, the inventory forecasting management system has the ability of modeling and showing the predicted picture of inventory behaviors for each product throughout the distribution channel. This is quite important when tackling the POS data collection, reconciliation within the organization’s distribution system (Decker & Gnibba-Yukawa, 2010; Kazemi, Nourelfath & Ait-Kadi, 2010).   

Conclusion

In conclusion, accurate inventory forecasting is a business phenomenon that currently faces operational challenges due to inaccurate data available for prediction. Inaccurate data used in inventory forecasting is coupled with multiple operational issues that need proper resolution. This has an impact on financial reporting, for instance, being one of the operational requirements of companies. Some of the available ways to resolve the inaccurate inventory forecasting challenges include the application of real-time inventory management system, product mix, and visible customer or store data that allow visible inventory forecasting.   

In light of the challenges that characterize the inventory forecasting in operational management, they are some recommendations for implementation. Firstly, the firm willing to used inventory forecasting needs to diversify its products line to eliminate any uncertainty associated with distribution channel invisibility. This can be done through product mix or product diversification. Secondly, visible data collection and analysis should be applied in inventory system to eliminate inaccuracy of the data used in inventory forecasting. Finally, firms should develop and implement comprehensive inventory management system that uses real-time data analysis system and data storage.

References

Babai, M. Z., Ali, M. M., & Nikolopoulos, K., (2012). Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis. Omega 40 (6), 713–721.

Catt, P.M. (2007). Assessing the cost of forecast error - a practical example. Foresight: the International Journal of Applied Forecasting 7: 5-10.
Forecasting for inventory planning: A... (PDF Download Available). Available from: https://www.researchgate.net/publication/220636331_Forecasting_for_inventory_planning_A_50-year_review [accessed Apr 07 2018].

Decker, R., & Gnibba-Yukawa, K. (2010). Sales forecasting in high-technology markets: A utility-based approach. Journal of Product Innovation Management, 27(1), 15-29.

Disney, S.M.  & Hosoda, T. (2008). The benefit of altruistic behavior achieved by the order-up-to policy with unmatched proportional feedback controllers in a two-echelon supply chain.  International Journal of Intelligent Systems, Technologies, and Applications, in the press. Eaves AHC (2002).

Fildes, R, Goodwin, P, Lawrence M & Nikolopoulos K (2008). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply chain planning. International Journal of Forecasting, in the press.

Kazemi, Z.M., Nourelfath M. & Ait-Kadi D. (2010). A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand. International Journal of Production Research, 48(16), 4701-4723.

Kolassa, S, Schutz, W, Boylan JE & Syntetos AA (2008). Judgmental changes to forecasts: higher inventories, unchanged out-of-stocks. Paper presented at the 28th International Symposium on Forecasting. Nice, France.

Kourentzes, N., Petropoulos, F., & Trapero, J. R., (2014). Improving forecasting by estimating time series structural components across multiple frequencies. International Journal of Forecasting 30 (2), 291–302.

Krajewski, L.J., Ritzman, L. P. & Malhorta, M.J. (2013). Operations Management: Processes and Supply Chains. 10th ed., Pearson.

Lewis, C.D. (eds) (2011). Demand Forecasting and Inventory Control: A computer-aided learning approach. ScienceDirect: Elsevier B.V

Lockard, R. (29 November 2010). 3 Advantages of Using Inventory Management Software. Inventory System Software Blog. Available at: https://inventorysystemsoftware.wordpress.com/2010/11/29/3-advantages-inventory-management-software/  [Retrieved 23 November 2012].

Lu, C. (March 27, 2014). Recent Study Shows that 66% of Warehouses Plan to Expand Technology Investments by 2018. TradeGecko Blog. Available at: https://blog.tradegecko.com/recent-study-shows-66-warehouses-plan-expand-technology-investments-2018/

Petropoulos, F., Makridakis, S., Assimakopoulos, V., & Nikolopoulos, K., (2014). 'Horses for Courses' in demand forecasting. European Journal of Operational Research 237, 152–163

Poluha, R. G. (2016). The Quintessence of Supply Chain Management: What You Really Need to Know to Manage Your Processes in Procurement, Manufacturing, Warehousing and Logistics (Quintessence Series). First Edition. Springer Heidelberg New York Dordrecht London.

Romeijnders, W., Teunter, R., & Jaarsveld, W., (2012). A two-step method for forecasting spare parts demands using information on component repairs. European Journal of Operational Research 220 (2), 386–393.

Snyder, R. D., Ord, J. K., & Beaumont, A., (2012). Forecasting the intermittent demand for slow-moving inventories: A modeling approach. International Journal of Forecasting 28, 485–496.

Syntetos, A.A., Boylan, J.E. & Disney, S.M., (2009), “Forecasting for inventory planning: A 50-year review”, Journal of the Operations Research Society, Vol. 60, Iss. S1, pp149–160. DOI: 10.1057/jors.2008.173.

Syntetos, A.A. & Boylan, J.E. (2008). Demand forecasting adjustments for service-level achievement. IMA J Management Mathematics, 19: 175-192.

Teunter, R. H., Syntetos, A. A., & Babai, M. Z., (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research 214 (3), 606–61

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