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# Implementing a Forecast Methodology as a Precursor to Lean Management

Goda Zvikaite¹, Sean Moore¹, Olivia McDermott²

University of Limerick, Ireland

²National University of Ireland, Galway, Ireland.

olivia.mcdermott@nuigalway.ie

**Abstract.** The paper aims to develop a new forecast methodology to enable an automotive manufacturer to achieve required service level agreements. The research proposes to develop a forecasting model to predict production demand accurately and thus reduce inventory, lead time to customers, improve on-time delivery and save costs. A new forecast methodology facilitated >80% forecast accuracy on distributor shipments to end customers and thus improved the manufacturer's response to customers. The researcher shifted focus from a system driven to actual customer demand-based forecast by focusing on distributor shipments to customers rather than bookings placed with the manufacturer. It was discovered that any forecasting methodology would fail unless the underlying process were stable. Hence, the forecasting model is an enabler for applying Lean principles to enhance inventory reduction and customer lead time further. It is recommended for future research focus on how Lean tools can impact forecasting and lean manufacturing.

**Keywords:** Forecasting, Inventory, Lean

## 1 Introduction

The research organisation requires transforming its manufacturing environment from reactive to proactive, capable of meeting 98-99% on-time delivery targets in ≤ 7 days. Such challenging objectives require a two-step approach, firstly - the development of a forecasting model that can predict production demand with >80% accuracy and implementing of Lean principles. Gligor et al. [1] discuss that accurate forecasting is needed to reduce manufacturing costs. Furthermore, an accurate forecast is needed in the organisation because of its wide variety of parts. In order to establish if implementing a forecasting accuracy model prior to a further Lean deployment would be successful, a pilot area was selected in the manufacturing areas' Cold Forming department. Accurate predictive forecasting should also positively impact inventory levels and product flow. One of the aims of this research is to find a way to implement forecasting to deploy LM later. Implementing Lean without an accurate forecasting system can result in inventory waste, poor customer service level agreements (SLA) and defects. Thus, a forecasting system should enable the manufacturing facility to meet required SLAs and achieve project objectives.

This research aims to validate an all-encompassing model that would enable the research organisation to reconcile its supply and demand process. With the help of LM and new forecasting techniques, such a process would improve flow, and organisational flexibility, increase the company's responsiveness and forecast correct parts. The accurate predictive forecast would also improve production capacity planning [2].

Several key performance indicators (KPIs) will be used to validate project success. These are Lead Time (LT), on-time delivery (OTD), internal inventory and forecast accuracy.
The research questions are:

1. What is the best method of forecasting?

2. What forecast accuracy, lead time and internal inventory level can be achieved?

## 2.0 Literature Review

Gligor et al. [1] discuss that accurate forecasting is needed to reduce manufacturing costs. An accurate and robust forecast methodology should help the company to forecast required parts into the buffer or the finished goods holding area in the warehouse. Both quantitative and qualitative forecasting techniques [3] will be used to evaluate forecast effectiveness. Danese and Kalchschmidt (2010) recommend supplementing statistical forecast methods with qualitative information, such as knowledge of subject matter experts, because >61% of executives do not trust statistical predictions (Wacker and Sprague 1995). A robust forecasting process must be implemented to improve the organisation's responsiveness to market demands. According to Danese and Kalchschmidt [4], a forecaster has to blend techniques with the "knowledge and experience of management". Fildes et al. [5] and the American Production & Inventory Control Society (APIC's) [3] both suggest that a small number of statistical forecasting models should be used to avoid confusion, which should later be supplemented with qualitative methods such as Delphi, end customer feedback, a panel of experts (could be management) or historical analogy.

It is understood from the literature that the selection of the right statistical model will be based on trial-and-error experimentation. Danese and Kalchschmidt [4] advise that companies should be very focused in their efforts to improve forecast accuracy and should use it to improve their decision-making. In addition, qualitative methodologies are used when "forecasted events" cannot be quantified accurately by statistical tools [6]. Quayle [7] recommend combining qualitative and quantitative models and advise starting the forecast at the end user level, then cascading it up to the supply chain partners, ensuring that all partners use the same forecast measure and models. Overall, the literature review identified that LM practices could help to create organisational flexibility, while robust forecasting methodologies can aid in achieving organisational responsiveness. Therefore it is necessary to understand and manage the relationship between both.

## 3.0 The Research Methodology for the Project

The research organisation requires transforming its manufacturing environment from reactive to proactive, capable of meeting 98-99% on-time delivery targets in ≤ 7 days. Such challenging objectives require a two-step approach, firstly - the development of a forecasting model that can predict production demand with >80% accuracy [8].

An accurate and robust forecast methodology should help the company to forecast required parts into the buffer or the finished goods holding area in the warehouse. Both quantitative and qualitative forecasting techniques [3, 7] will be used to evaluate the effectiveness of forecasting techniques. Danese and Kalchschmidt [4] recommend supplementing statistical forecast techniques with qualitative information, such as knowledge of subject matter experts because >61% of executives do not trust statistical forecasts [9]. An accurate forecast is needed in this case study organisation because of its wide variety of parts. In order to establish if a forecasting model would have the desired impact, a pilot area was selected in the Cold Forming department. This approach of the accurate predictive forecast should also have a positive impact on inventory levels and product flow. One of the aims of this research is to find a way for forecasting as a precursor for future projects on LM, e.g. Kanban, Total Preventative Maintenance (TPM), and Overall Equipment Effectiveness (OEE) can be subsequently implemented. Both should enable the manufacturing facility to meet required service level agreements (SLAs) and achieve project objectives.

In parallel, new forecasting methodologies will be developed, ensuring that the company manufactures correct buffer and non-buffer parts (also known as Finished Goods (FG) inventory). Not all products can go into an inventory buffer since some automotive parts are bespoke, governed by strict quality and engineering regulations and have unique configurations from the beginning.

Quality function deployment (QFD) tool will be adopted to identify business requirements (known as the voice of the business (VOB)) related to the development of new forecasting methodologies. Together with technical requirements, VOB should help define the forecast re-design plan. QFD
tool has been selected as a quality improvement tool because of its systemic approach to assessing and prioritising improvements in existing processes [10]. This practical problem-solving tool will help to structure customer needs and assess whether the new process can meet critical project requirements.

Based on the initial organisational assessment and literature review, the researcher identified that only 2 or 3 statistical forecast models should be selected to make the forecast re-design process more manageable [3, 7]. Therefore, the authors plan to experiment with 3 forecasting models, namely (3/6 month moving average, 3 months weighted moving average and exponential smoothing) using the Microsoft Excel platform. Once experiments are complete, the most accurate statistical forecast model will be selected. The research analysis for the forecast models is shown in Figure 1.

<figure><img src="image_2.png" /><figcaption>Figure 1: Forecasting Process – Penn Engineering Supply Chain Included, Source: Quayle [7]</figcaption></figure>

After completion of trials, data will be studied and analysed using SAS JMP statistical software tool. This forecasting methodology with practical problem-solving tools such as QFD should help evaluate forecast accuracy. Moreover, such an approach will ultimately aid in shifting production from push-to-pull systems, meaning that it will be more reactive and authorised by actual demand [11].

## 4.0 Results

Initial organisational current state analysis helped the researcher to understand the company's reliance on short-term customer bookings rather than forecasts. The existing Performance Optimisation platform used as a forecasting tool was abandoned due to its low accuracy levels (<60% accuracy). In addition, this tool used many different forecasting models that were too complex. The company wanted to design a long-term forecasting capability to predict market demand. Lead time (LT) in the cold forming (CF) department was, on average, 10 days, with a 12-day standard deviation (Figure 2).

Some outliers were as high as 272 days. Meeting OTD targets were unattainable, and unreliable machines and bottleneck operations obstructed production flow. This process was not under control and was highly unpredictable. To overcome these organisational challenges, the researcher conducted an extensive literature review which identified a very broad and fragmented volume of literature on the topic.

<figure><img src="image_3.png" /></figure>
<caption>Figure 2: Distribution Diagram Work Order Start on Machine to Inventory Complete Before Improvement</caption>

The research organisation previously relied on customer bookings as a forecast, but the company president requested to develop of a forward-looking forecast to ensure that the right parts could be built into the Kanban buffer or the finished goods warehouse. The president also stated that the company's long-term forecast was based on short-term sales without a long-term horizon, i.e., the organisation relied on 3-6-month direct customer forecasts and distributor quarterly bookings. Operationally the business needed to build long-term forecasting capability that enabled the company to predict market demand trends by getting closer to end-customers.

The Quality Function Deployment tool set the foundation for the re-design forecasting process and helped define business requirements (an image of the completed VoB and technical requirements for deployment are shown in Table 1 below). Five senior company executives were interviewed to complete VOB and technical requirement sections. Forecast model technical elements that got the highest scores were not deployed in this project because the organisation could not implement them at this stage. The voice of the business was listed in the order of priority as gathered during the QFD process, while all 19 technical requirements identified during the 5 interviews were listed. It shows that only requirements: 11, 14, 17, 18 and 19 were selected for deployment in this phase. This implementation, with selected technical requirements, focuses on quantitative forecast supplemented by qualitative data; it also focuses on distributor sales, called point of sale (POS), which is explained later. The new forecast model re-design will lay the foundation for sales & operations planning (S&OP) process development and more LM systems integration in the future.

<table><caption>Table 1: The Voice of the Business & Technical Requirements to deliver the Voice of the Business</caption><thead><tr><td></td><td>Voice of Business</td><td>Tech. Req.</td></tr></thead><tbody><tr><td>1.</td><td>Map out supply chain Ecosystem to develop an understanding of who our customers are (start at the end-user level).</td><td>17, 18, 19</td></tr><tr><td>2.</td><td>Develop relationships with customers in the supply chain to get demand depending our growth areas.</td><td>17, 18, 19</td></tr><tr><td>3.</td><td>Develop field sales strategies to collect long term needs and future trends for growth areas and capture that knowledge in a centralised way using technology.</td><td>17, 18, 19</td></tr><tr><td>4.</td><td>Fast development of new innovative product ideas to satisfy market wants/needs based on PESTEL and competitor analysis.</td><td>18, 19</td></tr><tr><td>5.</td><td>Create ability to react to customers faster than our competitors.</td><td>17, 18, 19</td></tr><tr><td>6.</td><td>React to other market supplier issues and capitalise on their weaknesses.</td><td></td></tr><tr><td>7.</td><td>Identify all the data sources that could be used to forecast demand and connect them.</td><td></td></tr><tr><td>8.</td><td>Regular, methodological competitor evaluation against our top market differentiators (quality, reactivity, tech. support, engineering, cust. service, OTD).</td><td></td></tr><tr><td>9.</td><td>Regular, methodological competitor evaluation against our top market differentiators (quality, reactivity, tech. support, engineering, cust. service, OTD).</td><td></td></tr><tr><td>10.</td><td>Improve on product design.</td><td></td></tr><tr><td>11.</td><td>Capture End Customer long terms needs to achieve early booking predictions. Develop structure and methods.</td><td></td></tr><tr><td>12.</td><td>Understand our own existing quantitative (GAINS) methodologies and accuracy.</td><td>11, 14, 17</td></tr><tr><td>13.</td><td>Create Lead Time variation depending on the industry, product and customer e.g., differentiated lead-time. Feed back into supply chain map.</td><td></td></tr><tr><td>14.</td><td>Profiling all aspects of our competitors' businesses e.g., R&D, financials, any new products.</td><td></td></tr><tr><td>15.</td><td>Centralise sales-based forecast using available or new technology and standardised approach.</td><td></td></tr><tr><td>16.</td><td>Include project ramp curve as a part of formal S&OP (it should be capacity planning) and forecast methodology by including all project life cycle phases: Year 1 - 10%; Year 2 - 30%; Year 3 - 60%; End of life</td><td></td></tr><tr><td>17.</td><td>Track relevant trends in European market in an official, centralised manner as opposed to leaving it to individual initiatives. Then capitalise on this intelligence through actionable plans on new trend development before they grow.</td><td></td></tr><tr><td>18.</td><td>Develop structure and methods to analyse distributor targets and growth areas.</td><td></td></tr><tr><td>19.</td><td>Analyse distributor forecast and question it.</td><td></td></tr><tr><td>20.</td><td>Develop methodologies to collect voice of the customer for the lost projects in our pipeline (customer surveys, interviews, focus groups) and use the feedback to develop our product (pricing, lead times, product offering, tech. support, installation offering) based on customers' requirements.</td><td></td></tr><tr><td>21.</td><td>Improve inventory turns.</td><td>19</td></tr><tr><td>22.</td><td>Improve on Price.</td><td></td></tr></tbody></table>
<table><thead><tr><th>Technical Requirements to Deliver Voice of Business</th><th>Phase 1 of Implementation</th></tr></thead><tbody><tr><td>1. To map SCM ecosystem.</td><td></td></tr><tr><td>2. Review POS against market to identify the makeup of our customers, then rate and rank.</td><td></td></tr><tr><td>3. Develop Sales Force (SF) analytics to track wins, trends etc. Use SF as a central intelligence data collection system.</td><td></td></tr><tr><td>4. Link sales force to JDE via Qlik</td><td></td></tr><tr><td>5. Competitor analytics (using SF): regular info gathering about competitors: create a framework, study competitor parts, financial strengths, history, profitability, build profiles, rate &amp; rank. Action driven.</td><td></td></tr><tr><td>6. Customer analytics: gather info from end customers via customer focus groups, interviews etc. Use centralized database like Sales Force to store info, analyze, drive decisions.</td><td></td></tr><tr><td>7. Implement a new forecasting process 50% system based on statistics, 50% human based qualitative info.</td><td></td></tr><tr><td>8. Build reliable stock quantities during non-busy periods.</td><td></td></tr><tr><td>9. Economic Index review, analysis and comparison to our strengths and weaknesses.</td><td></td></tr><tr><td>10. Allocate sufficient resources.</td><td></td></tr><tr><td>11. Reduce number of forecasting models used by GAINS and use only 2 or 3 models that are easily understood and make sense.</td><td>Y</td></tr><tr><td>12. The same quantitative (statistical) forecasting models and forecast accuracy measure to be used by all SCM partners.</td><td></td></tr><tr><td>13. To start qualitative forecast at the end-user level, then cascade this forecast up to the supply chain partners (SCM).</td><td></td></tr><tr><td>14. To measure statistical forecast accuracy and if it is &lt;2 Sigma, then intervene using qualitative info gathered from the field sales from all SCM partners, starting with end-users.</td><td>Y</td></tr><tr><td>15. Partner with academic institutions and research centers.</td><td></td></tr><tr><td>16. Segment Lead Time based on customer requirements.</td><td></td></tr><tr><td>17. Run continuous improvement programs to meet customer service level in terms of Lead Time.</td><td>Y</td></tr><tr><td>18. Continue review of distributor quarterly reviews and annual plans (to measure, report back and apply metrics, performance reports and actions plans).</td><td>Y</td></tr><tr><td>19. Expand S&OP Process.</td><td>Y</td></tr></tbody></table>

Following the agreement to reduce BOMs and start forecasting model testing. In addition, a kanban buffer was created for high-volume parts, which would also have to be pushed into the buffer using a new forecast and then pulled based on customer demand. Therefore, it was important to work on machine schedules, available capacity, and demand during the lead-time assessment and set up safety stock (SS) and Economic Order Quantities (EOQs). These parameters were simulated on Excel sheets; then, a pilot was launched on the S-M6 group of parts, which proved this project's viability; as a result, the roll-out commenced. Table 2 shows the calculated Weekly Demand, which was based on a historical 52-week production average. Production Run Lengths were set based on reduced changeover numbers. LT Weeks' value reflected the period between production runs. Pieces Made determined the number of pieces made during each run. Product bin sizes limited EOQs; SS was set up using supply chain standards from APICS Certified Planning & Inventory Management (CPIM) 2019 manual. Re-order Point reflected production pieces excluding weekly demand with Safety Stock (SS) and EOQ levels. The weight of the re-order point quantity then determined the minimum number of bins required to keep in the Kanban buffer.

<table><caption>Table 2: Pull System Simulation Parameters</caption><thead><tr><th>SA #</th><th>LT Weeks</th><th>Production Run Length</th><th>#Shifts</th><th>EOQ</th><th># of EOQ</th><th>Pieces Made</th><th>Re-order Point: SS+EOQ</th><th># Bins for Re-order Point</th><th>Weekly Demand</th></tr></thead><tbody><tr><td>S-M6-1SA</td><td>2</td><td>2 weeks (10 days)</td><td>3</td><td>350,000.00</td><td>6</td><td>2,100,000.00</td><td>819,870.04</td><td>3.06</td><td>737,163.32</td></tr><tr><td>S-M6-2SA</td><td>2</td><td>1.6 week (8 days)</td><td>3</td><td>350,000.00</td><td>5</td><td>1,750,000.00</td><td>899,752.81</td><td>3.72</td><td>437,864.04</td></tr><tr><td>S-M6-OSA</td><td>3</td><td>0.9 week (4.5 days)</td><td>3</td><td>350,000.00</td><td>2</td><td>700,000.00</td><td>550,131.24</td><td>1.98</td><td>83,343.60</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>4,550,000.00</td><td>2,269,754.09</td><td>9 bins</td><td></td></tr></tbody></table>

Inventory re-order point required significant amounts of stock to be built into the buffer; therefore, at the time of writing this project, the roll-out was partially completed for M6, M8 and M10 parts. The remaining M3, M4 and M5 parts were still outstanding; nevertheless, a 33% reduction in FG inventory was observed from this partial completion, with a further 25% to come from the full roll-out. WIP in the Kanban buffer increased accordingly, meaning that around 5 million pieces (worth around 100,000 USD) of inventory were no longer at risk of obsolescence in the warehouse. While the target was to reduce FG by 58%, these results proved that the objective to reduce inventory would be achieved after the full roll-out. Regarding the response to customer improvement, Figure 3 shows a 99.78% on-time delivery to customers for launched M6, M8 and M10 parts, while the overall OTD metric for the company is 92%, as shown in Figure 4.
<figure><img src="image_4.png" /><figcaption>Figure 3: Kanban Part on-Time Delivery to Customers</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 4: All Part On-Time-Delivery to Customers</figcaption></figure>

Aside from OTD improvements and reduced finished goods - significant lead-time improvements have also been noticed. Figures 5 and 6 show the Kanban buffer impact on lead-time reduction and compare the before and after stages. Such results show significant improvements compared with a 10-day lead-time average for all CF parts in the before sections of Figure 5.

<figure><img src="image_6.png" /><figcaption>Figure 5: Levey Jennings Chart: WO Lead-time Performance Before and After#</figcaption></figure>

Regarding forecast, Quayle [7] recommends creating a process that supplements statistical results with qualitative information (Figure 3, Appendix 5). As a result, senior company executives agreed to focus on one or two quantitative forecast models, supplemented with qualitative aspects such as management expertise and historical analogy [3]. The statistical forecasting tool based on the GAINS software had been switched off before writing this project because this system gave forecast accuracy that was <60%. This tool used multiple
quantitative forecasting models and generated too many messages that had to be actioned on the Enterprise Requirements Planning (ERP) system. In order to re-design forecasting processes, the researcher selected 3 models as recommended by the APICS CPIM [3] and Quayle [7] and tested them using different levels. A design of experiments methodology did not appear viable because only one factor was at play.

Findings show that CF Kanban buffer parts could be forecasted with 88-100% accuracy. However, the researcher discovered that distributor bookings forecast for non-Kanban parts resulted in around 60% forecast accuracy. Therefore, senior executives during November 2021 meeting recommended forecasting distributor sales, known as POS, instead. They further advised comparing POS forecast findings against bookings that distributors placed. According to executives, it made more sense to forecast POS instead of bookings because the former reflected the real customer demand. As a result, POS forecast resulted in >80% forecast accuracy, mainly because there is less volatility in distributor shipments compared to the orders they place with the manufacturer.

In order to find out which model could give the best forecast accuracy, the following were tested: moving average, moving weighted average and exponential smoothing[3, 7]. The researcher used the Microsoft Excel platform to test each model at different levels. For example, moving average and weighted moving average models were tested using 3- and 6-month sample data, while the exponential smoothing model was tested using 0.1, 0.2, 0.3, 0.7, 0.8 and 0.9 alpha smoothing constants. Figure 6 shows in - One-way Analysis of Predictive 2022 Q1 Forecast Accuracy by Experimental Setting that the best results were obtained from the exponential smoothing model, and the average forecast accuracy of >80% was achieved when POS was forecasted. APIC's CPIM [3] recommends using the following formula to calculate the exponential smoothing forecast:

$$ \text{New Forecast} = (\alpha * \text{Latest Demand}) + ((1 - \alpha) * (\text{Previous Forecast})) $$

<figure><img src="image_7.png" /><figcaption>Figure 6: One-way Analysis of Predictive 2022 Q1 Forecast Accuracy by Setting</figcaption></figure>

There was a small challenge related to these forecast models, which can predict the new forecast only when the latest demand data is available. Unfortunately, the latest demand data in the company is not available for the previous period until the middle of the next period for which the forecast is needed. To overcome this challenge, a qualitative technique called historical analogy was used [3], which looks at historical forecasts and accuracy. In this case, this technique uses the last available information to predict the next period. To illustrate this, Table 3 has been prepared, where the researcher recommends forecasting 2022 Q1 using the 2021 Q4 forecast and 2021 Q3 accuracy.

<caption>Table 3: Forecast and Accuracy for Catalogue POS 2019-2021</caption>
Catalogue POS Forecast Accuracy 2019-2021

<table><thead><tr><th rowspan="2">Experimental<br>Setting</th><th rowspan="2">Part Number</th><th colspan="2">2021 Q3<br>Accuracy</th><th>2021 Q4<br>Forecast</th></tr><tr><th>80%</th><th></th><th></th></tr></thead><tbody><tr><td>Alpha 0.3</td><td>S-M3-1ZI</td><td></td><td>80%</td><td>4,479,425.33</td></tr><tr><td>Alpha 0.3</td><td>S-M4-1ZI</td><td></td><td>81%</td><td>3,589,284.22</td></tr><tr><td>Alpha 0.3</td><td>S-M4-2ZI</td><td></td><td>94%</td><td>3,192,926.37</td></tr></tbody></table>

The above information was presented to senior executives, who recommended comparing predictive exponential smoothing forecast for POS and actual bookings while considering distributor channel inventory. As the exponential smoothing forecast is based on distributor shipments, this analysis will allow the management team to ascertain whether distributors book orders with the company in line with their shipments, also known as POS. Table 4, *Bookings vs Exponential Smoothing Forecast on Distributor Shipments* column marks in red bookings that are under-booked compared to POS forecast; and overbooked bookings marked in black. The table shows that some distributors are overbooked by 71%, while others are underbooked by 58%. Distributor inventory should also be close to *Predicted Exponential Smoothing Forecast for Distributor Shipments 2022/Q1* value because the company has a legal agreement to keep 25-35% of annual distributor shipments in the distributor warehouse. However, as seen from *the Distributor Inventory* analysis, some distributor inventory is too low.

<table><caption>Table 4: Exponential Smoothing Forecast vs. Actual Bookings</caption><thead><tr><th>Part Number</th><th>Distributor Name</th><th>Predicted Exponential Smoothing Forecast for Distributor Shipments 2022/Q1</th><th>Distributor Inventory December 2021</th><th>Distributor Bookings 2022/Q1</th><th>Bookings v.s. Exponential Smoothing Forecast in Pieces</th><th>Bookings v.s. Quarterly Average Distributor Shipments</th><th>Bookings v.s. Exponential Smoothing Forecast on Distributor Shipments</th></tr></thead><tbody><tr><td>S-M3-1ZI</td><td>Distributor 1</td><td>230,604.99</td><td>15,720.00</td><td>200,000.00</td><td>-</td><td>30,604.99</td><td>8%</td></tr><tr><td>S-M3-1ZI</td><td>Distributor 2</td><td>168,181.75</td><td>232,700.00</td><td>400,000.00</td><td>231,818.25</td><td>46%</td><td>58%</td></tr><tr><td>S-M3-1ZI</td><td>Distributor 3</td><td>146,170.27</td><td>228,500.00</td><td>#N/A</td><td>#N/A</td><td>#N/A</td><td>#N/A</td></tr><tr><td>S-M3-1ZI</td><td>Distributor 4</td><td>643,596.94</td><td>1,118,595.00</td><td>500,000.00</td><td>-</td><td>143,596.94</td><td>13%</td></tr><tr><td>S-M3-1ZI</td><td>Distributor 5</td><td>2,629,440.39</td><td>2,865,200.00</td><td>3,000,000.00</td><td>370,559.61</td><td>18%</td><td>12%</td></tr><tr><td>S-M3-1ZI</td><td>Distributor 6</td><td>137,107.45</td><td>263,797.00</td><td>105,000.00</td><td>-</td><td>32,107.45</td><td>25%</td></tr><tr><td>SS-M5-2ZI</td><td>Distributor 1</td><td>158,495.45</td><td>44,000.00</td><td>100,000.00</td><td>-</td><td>58,495.45</td><td>76%</td></tr><tr><td>SS-M5-2ZI</td><td>Distributor 2</td><td>160,662.00</td><td>365,800.00</td><td>200,000.00</td><td>39,338.00</td><td>23%</td><td>20%</td></tr><tr><td>SS-M5-2ZI</td><td>Distributor 3</td><td>114,813.18</td><td>169,600.00</td><td>-</td><td>-</td><td>114,813.18</td><td>#DIV/0!</td></tr><tr><td>SS-M5-2ZI</td><td>Distributor 4</td><td>360,773.48</td><td>322,220.00</td><td>754,000.00</td><td>393,226.52</td><td>52%</td><td>52%</td></tr><tr><td>SS-M5-2ZI</td><td>Distributor 5</td><td>869,661.26</td><td>705,700.00</td><td>1,250,000.00</td><td>380,338.74</td><td>35%</td><td>30%</td></tr><tr><td>SS-M5-2ZI</td><td>Distributor 6</td><td>96,483.55</td><td>38,810.00</td><td>336,000.00</td><td>239,516.45</td><td>73%</td><td>71%</td></tr></tbody></table>

## 5. Discussion

The research findings indicate significant improvements in lead time, inventory, on-time delivery and forecast accuracy. Furthermore, accurate predictive forecasts can significantly improve operations and smooth demand to meet required customer service level agreements. Even though inventory improvements were achieved *only* in the CF department and predictive forecast methodology was newly developed, this project provided confidence that the same methodologies could be used for consecutive roll-outs. Therefore, this work proved that LM practices and robust forecasting methods must be in place to achieve the required customer SLAs in volatile environments to enable more Lean pull and JIT production. In summary, this work proved that robust forecasting could be a precursor for LM to improve JIT and OTD measures.

Despite several challenges faced throughout the implementation of this project, the research objectives were achieved as outlined in the results and findings section. The organisation also needed to reduce the risk of
inventory obsoletion and FG levels. A historical 52-week average forecasting method was selected to predict buffer parts requirements. Such a simplistic method was chosen to forecast Kanban buffer parts because selected parts were in high demand, and not much volatility was present in bookings. However, non-Kanban/non-buffer parts required the implementation of more complex forecasting methodologies because of their demand volatility.

Most of the literature on forecasting discussed either qualitative or quantitative models, while some academics believe that forecasts are rarely accurate and that company executives do not trust them [9]. Industry research revealed that combining qualitative and quantitative methods would improve forecasts [3, 7]. However, the research findings suggest that the company was not in a position to consider more advanced qualitative forecasting techniques, such as the Delphi model [3] or end customer information collection because of time constraints. Only management expertise and historical analogy [3]could be used as qualitative methodologies at this stage. However, the Delphi model or end user feedback should provide a forward-looking forecast; therefore, the organisation must investigate these possibilities in the future.

Overall, >80% forecasting accuracy was achieved for POS forecasts. Senior executives confirmed that forecasting distributor bookings were not as important, as these did not reflect the real demand, while POS more accurately showed customer needs. Table 5 shows the company's POS and bookings forecast accuracy using exponential smoothing alpha constant 0.3 for 2019, 2020 and 2021. Bookings show lower forecast accuracy, which indicates higher volatility. POS results show higher accuracy, which indicates that distributor sales are less volatile than their bookings with the research company.

<table><caption>Table 5: Exponential Smoothing Forecast to Forecast POS and Distributor Bookings</caption><thead><tr><th>Average</th><th>2019</th><th>2020</th><th>2021 till September</th></tr></thead><tbody><tr><td>Monthly POS Shipments Qty</td><td>68,647,760.00</td><td>63,103,992.17</td><td>80,699,824</td></tr><tr><td>POS Forecast Accuracy</td><td>84%</td><td>70%</td><td>88%</td></tr><tr><td>Channel Monthly Ending Inventory Qty</td><td>300,016,675.00</td><td>315,319,790</td><td>248,819,518</td></tr><tr><td>PEM Backlog</td><td>233,762,224.00</td><td>168,553,999</td><td>295,656,927</td></tr><tr><td>PEM Europe Monthly Bookings Qty</td><td>60,985,115.00</td><td>56,780,471</td><td>96,469,430</td></tr><tr><td>Bookings Forecast Accuracy</td><td>78%</td><td>59%</td><td>61%</td></tr><tr><td>PEM Europe Inventory</td><td>83,404,847.00</td><td>87,317,458</td><td>61,444,300</td></tr></tbody></table>

This paragraph discusses how >80% of POS forecast findings should be used together with LM practices by the research organisation to meet required SLAs. When the researcher compared bookings versus forecast, it was noted that some distributors were over-booked, and some were under-booked (Table 6). This is because distributors use inaccurate forecast models, and price incentives drive their bookings. Such practices create difficulties at the manufacturing facility because they cause demand volatility. Overbooked Distributors compromise the organisation's manufacturing capability to produce parts to customers that are under-booked, e.g., *Distributor 1* is under-booked by 58% for the part number: SS-M5-2ZI. The research organisation will supply the same part to *Distributor 6*, which is over-booked by 71%, and at the same time will ensure that *Distributor 1* also gets all demand. This puts much pressure on production lines at the facility, even if the right LM systems are in place. Therefore, the company should communicate forecast and over-booked/under-booked values to distributors requesting deferrals in line with the forecast, which has high accuracy levels. Such actions should smooth out demand and reduce pressure on the manufacturing facility. In addition, distributor inventory is not aligned with the contractual manufacturer's requirement (i.e., 25-35% of annual POS). It is either too low or too high, meaning that distributors place orders to replenish their inventory when it is too low. At the time of writing this project, the customer service team was asked to reach out to distributors to smooth their bookings in line with the forecast, and due to time constraints, results were not available to report here.

By having high accurate POS forecast, the company overcomes volatility related to orders that distributors place with the factory. When writing this project, the researcher could not show smoothed demand based on POS forecast accuracy, but the recommendation to the organisation has been provided. In addition, significant inventory turnover improvements have been observed since the beginning of this project, as demonstrated in the *before* and *after* phases in Table 6. It is highly recommended that management continue to monitor these KPIs and identify building blocks that link LM to increased inventory turns.

<caption>Table 6: KPIs Before and After improvements</caption>
<table><thead><tr><th>High-Level Project KPIs</th><th>Before</th><th>After</th></tr></thead><tbody><tr><td>Lead-time:</td><td>10 days</td><td>&lt;5 days</td></tr><tr><td>Forecast Accuracy:</td><td>NA for POS*</td><td>70-88% for Point of Sale</td></tr></tbody><thead><tr><th>Critical Impact KPIs</th><th>Before</th><th>After</th></tr></thead><tbody><tr><td>Inventory Turnover:</td><td>4.9</td><td>8.98</td></tr></tbody></table>

## 6. Conclusion

The paper aims to develop a new forecast methodology to enable an automotive manufacturer to achieve its required service level agreements. The research developed a forecasting model to predict production demand accurately, thus reducing inventory and lead-time to customers, improving on-time delivery and saving on costs. The new forecast methodology facilitated >80% forecast accuracy on distributor shipments to end customers and thus improved the manufacturer's response to customers. The forecasting model is an enabler for the future stage of the research to apply Lean principles to enhance inventory reduction and customer lead time further. Lean tools will be applied to impact forecasting efficiencies and enable a leaner manufacturing process.

A limitation of this research was that Production planning process inefficiencies are not discussed in this research due to scope considerations, although this problem can be addressed by management. In summary, it is necessary to emphasise that while the company is trying to implement correct forecasting and LM practices using pull and JIT principles, it should also focus on other production aspects, such as the production planning process, because ignoring it may have a negative impact on the outcomes of this project.

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