Supply Chain Vector [Electronic resources] : Methods for Linking the Execution of Global Business Models With Financial Performance نسخه متنی

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Supply Chain Vector [Electronic resources] : Methods for Linking the Execution of Global Business Models With Financial Performance - نسخه متنی

Daniel L. Gardner

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Chapter 14, at which time major issues related to skyrocketing raw materials costs will indeed be uncovered. Because it suspects that increases in COGS are impacting all areas of the business, NikoTech has also decided to focus on the numerator in the equation, average inventory. This breakdown begins with a horizontal analysis of inventory values relative to sales growth, followed immediately by a vertical analysis of inventory makeup (raw materials, work in process or finished goods) to zero in on the real source of the problem. From there, the team can use other techniques to uncover root causes.


Horizontal analysis of NikoTech's inventory value shows an increase from $480 million in Y1 to $660 million in Y2, a spike of $180 million (37.5%). When inventory investment is compared against Y1 net sales of $1.440 billion, the inventory to net sales ratio reveals that for every dollar of revenue generated, 33 cents in inventory had to be carried to support that sale (inventory/net sales or $480 million/$1.440 billion). Performance was worse in Y2, with 39 cents of inventory for every dollar in sales ($660 million/$1.698 billion). Because inventory values are increasing and not being matched by more sales, the concern is that NikoTech's inventory growth is outpacing its revenue growth, a situation that must be reversed immediately. Table 12.2 validates this concern by showing that inventories are growing twice as fast as net sales (37.5% versus 18%). Based on this information, management decided to look further into the makeup of Y2 inventories with a vertical analysis.






Table 12.2: Year-to-Year Net Sales Versus Inventory Growth














The Y2 inventory breakdown in Table 12.3 shows that of the $660 million total, 55% is in raw materials, 25% is in work in process and the remaining 20% is in finished goods. In order to understand the meaning of this breakdown, one must return to the characteristics of the company's business model. Working off of a build-to-order model, the goal of the organization is to hold as little inventory as possible, regardless of its classification. Unfortunately, the reality of working internationally dictates that certain levels of raw materials be maintained to accommodate an unstable production environment. Because of the characteristics of the model, however, the explanation behind the increase in work-in-process and finished goods levels is a bit harder to justify.






Table 12.3: Y2 Inventory Breakdown













At the end of Y2, raw materials inventory represented $363 million (55%) of the total, so the initial focus of the NikoTech management team was centered there. A simple five whys session conducted by the NikoTech team revealed some interesting facts. First, it did not take long to discover that the growth in raw materials inventory was actually a result of the company's sales growth. Because sales grew much faster than any collaborative planning or forecasting exercise could indicate, the demand on raw materials grew proportionately.


The worst nightmare for any buyer is to be the sole person responsible for a line going down due to lack of raw materials (especially if one is responsible for buying them). When demand blew out the forecast, the buyers started to release orders earlier and in greater quantities. Procurement of specific components became disproportionate to actual demand, creating the growth in raw materials inventories. The additional pressure on suppliers destabilized their production plans, creating a ripple effect through the materials side of the supply chain that complicated NikoTech's situation further.


A second and more disconcerting issue revolved around the accuracy of lead times into the plants. The five whys session also revealed that there was major variation in lead times feeding into the plants. While each plant had established lead time commitments from raw materials suppliers into the factories, actual delivery times could vary as much as ten days from the estimated time of arrival. Because buyers had no confidence in lead times and were uncertain of the validity of the forecast, they continued with their practice of ordering earlier and in greater quantities.


Unfortunately, the second-order effects of the lead time issue resonated across the organization and manifested themselves as inventory inaccuracies. Because inbound shipments were moving in such large quantities and at such a furious pace, the receiving entities at each of the four factories began to lose track of what they were taking in. At one point in Brazil, for example, manufacturing was so desperate for parts that as soon as goods arrived, they were delivered directly to the production floor and were never entered into the system.


Without being entered into the system or at least accounted for via a back-flushing mechanism, inventory accuracy related to raw materials reached a low of 60%. Without the ability to know exactly what is in inventory and what is lined up for scheduled receipts, a production facility will eventually go down. Bypassing the receiving process also resulted in skipping over quality checks on incoming merchandise. The second-order effect of not receiving goods into the system created a third situation where defective merchandise got all the way to the production line before problems were discovered. This once again slowed down or stopped lines completely, with a buildup of work in process as a direct result.


Armed with proof as to why raw materials inventories were growing outside of demand, other tools were used to expose what was driving this problem. First, the team established the mean lead time into each plant and compared it with the agreed-to lead times from suppliers in each region (Asia, North and South America and Europe). From there, the standard deviation of lead times into each plant was calculated. Results of these calculations revealed not only that mean lead times were well outside the parameters but that the spread of individual data was all over the map. Subsequent preparation of a value-added process map uncovered several delays in areas that included availability/accuracy of commercial documents from suppliers, cargo routing and customs delays at destination.


At this point, the team should treat lead times into each plant as a separate issue. Because NikoTech works with so many suppliers, it may be wise to carry out a Pareto analysis that shows who the biggest lead time variance offenders are and focus improvement efforts on them. The improvement process can then be facilitated by either a deployment or detailed process map to cut out bottlenecks, delays and unnecessary handoffs across the entire process. While just a beginning, this procedure will bring relief to the increase in raw materials inventories and help to begin the process of reconciling inventory accuracy.


With regard to the inventory accuracy problem, NikoTech must implement a cycle-counting policy that first determines what it has in raw materials inventory and then forces the company to manage accuracy on a daily basis. Only then can NikoTech begin to effectively plan for Y3 and beyond. Inventory accuracy is critical to the gross to net exploding component of the materials requirements planning system, as it allows future net requirements to be based on what is in inventory and what is in the pipeline. Without that initial accuracy, the materials requirements planning system is completely useless and will only cause more confusion in the future.


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