CSCP L4 Case study Exam

Study the case study and answer the following questions:

StarGazer is a leading producer of cutting-edge electrical equipment, specializing in a wide range of products from industrial transformers and circuit breakers to smart home solutions and portable power equipment. With a commitment to innovation and sustainability, the company serves diverse industries, including manufacturing, energy, and consumer electronics. StarGazer is headquartered in Boston, with regional offices across North America and Europe.

The Electrical Equipment Industry:

The electrical equipment industry is rapidly evolving, with advancements in automation, renewable energy integration, and IoT technology. StarGazer competes with giants like PowerTech Industries and VoltEdge Solutions. Customers range from large-scale industrial enterprises to small businesses and tech-savvy consumers. However, the industry's dynamics are shifting due to changing customer demands, including increased customization, faster delivery, and sustainable manufacturing practices.

Changing Customer Requirements
Over the past five years, StarGazer has faced heightened pressure to innovate and adapt to evolving customer expectations:

• Demand for highly specialized components with shorter lead times.
• Increased focus on eco-friendly production.
• High availability for replacement parts to minimize downtime for industrial customers.
• Predictable stock replenishment for retail channels during promotional cycles

Demand History Chart

The data reflects a surge in demand for smart home solutions and portable power units, driven by the pandemic and remote work trends, while industrial components showed steady but gradual growth.

Warehouse Operations at StarGazer

The warehouse operations at StarGazer are a vital part of its supply chain, designed to handle the diverse range of electrical equipment products with precision and efficiency. The central warehouse spans a sprawling 250,000 square feet and is a blend of traditional storage practices and modern automation. The facility is divided into distinct zones tailored to the nature of the products. High-value items like industrial transformers and circuit breakers are housed in secure, restricted-access zones, reflecting their critical importance and the high costs associated with holding or losing these items. Temperature-sensitive goods, such as batteries, are stored in climate-controlled sections to ensure their integrity. However, there have been instances where improper utilization of these controlled areas has led to spoilage, hinting at potential inefficiencies in the allocation of storage resources.

StarGazer’s inventory management relies heavily on perpetual inventory records. Each product is barcoded, and updates are made in real time whenever inventory is received or dispatched. The Warehouse Management System (WMS) integrates seamlessly with the company’s demand forecasting tools to ensure the availability of products when needed. Yet, the effectiveness of this system is sometimes undermined by inaccuracies in  demand forecasts, particularly for seasonal or slow-moving items. As a result, some products remain in storage far longer than expected, occupying valuable space and occasionally incurring additional handling costs. This is especially evident with B-class items, which experience unpredictable demand patterns that the current forecasting models struggle to capture effectively.

The warehouse layout is optimized for space utilization, with high racks accommodating vertical storage and automated picking systems improving the speed and accuracy of order fulfillment. The picking process is zone-based, meaning items from different product classes are retrieved separately before being consolidated. This approach aims to streamline operations, but orders that combine A-class, B-class, and C-class items frequently encounter delays due to a lack of coordination between zones. The complexity of these orders, coupled with high-volume periods, sometimes results in bottlenecks that disrupt the timely dispatch of shipments.

Order fulfillment is another crucial aspect of StarGazer’s warehouse operations. Each order goes through a rigorous quality-check process to ensure customers receive the correct and defect-free products. This is particularly important for perishable items, which require careful handling and monitoring throughout the fulfillment process. However, reports of expired or damaged perishable goods occasionally surface, hinting at gaps in inventory rotation practices or mismanagement of shelf-life-sensitive products.

The storage of style goods, such as customized circuit breaker panels, presents its own set of challenges. These items require longer lead times and specialized handling, and delays in replenishment cycles often leave the warehouse short of the necessary stock. This not only affects customer satisfaction but also puts pressure on production to prioritize custom orders over standard ones.

StarGazer has taken steps to address these challenges, including revising safety stock levels and collaborating with suppliers to create shared pools of critical spare parts. However, logistical delays and inconsistent application of these strategies have limited their effectiveness. The company’s efforts to optimize safety stock for A-class items and reduce spoilage in perishable goods remain works in progress. At the same time, the demand forecasting tools implemented to improve predictions for B-class and style goods have yielded mixed results, failing to adapt to the rapid shifts in market trends.

The warehouse stands as both a testament to StarGazer’s dedication to operational excellence and a reminder of the complexities inherent in managing such a diverse range of products. While many processes are state-of-the-art, the nuances of demand variability, storage optimization, and order fulfillment present ongoing challenges that require careful attention and continuous improvement. The underlying issues are subtle but significant, leaving room for reflection on how the company can evolve to meet these challenges more effectively.

Questions

1. What specific improvements can StarGazer make in their demand forecasting to reduce stockouts and overstock situations for A-class items?
2. How might warehouse operations address the challenges of inconsistent inventory turnover for B-class and style goods?
3. What steps could ensure that climate-controlled zones are effectively utilized to minimize spoilage in perishable items?

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Study the case study and answer the following questions:

Due to competitive pressures, firms in the computer industry are constantly looking to reduce costs. Computer manufacturers compete fiercely for contracts based on meeting the technology, quality, and price requirements of customers. Profit margins and return-on-investment targets are almost always under pressure. Dell Computer recently saw its operating margins slip to a slim 7%.
Most computer manufacturers have programs designed to improve quality and reduce the costs associated with their products. One strategy that many producers use is to contract only with high quality suppliers and develop longer-term buyer-seller relationships.
One major computer company, Porto, also initiated a program requesting suppliers to continually improve productivity, which should lead to cost reductions. The objective of the program was to reduce purchase costs over the foreseeable future. Porto also expects its suppliers to contribute cost-saving ideas whenever possible.
The high-technology industry features high fixed costs due to large investments in plant and equipment. These companies also commit large expenditures to research and development.
Porto currently has a requirement for an electronic component termed “New Prod,” which is part of a recently designed product. The estimated volume requirement of New Prod is 200,000 units with additional follow-on orders likely. For the New Prod component, Porto felt there were five to eight highly competitive suppliers capable of producing the item. These suppliers are located primarily along the East and West Coasts of the United States. After a request for quote and preliminary analysis, the buyer for Porto decided to pursue further discussions with Technotronics.
Negotiation Session Requirements Each negotiator must plan and prepare before conducting the negotiation. The group leader has information packets for the buyer and the seller that provide addi- tional information and

assignments required for conducting the negotiation. Buyers and sellers can share as little or as much of the information with each other as they desire during the actual negotiation.
In last two negotiations with Technotronics was mixed experience. In first negotiation, the suppliers were seemed cooperative. Tried to present the side benefits of working with them- in the form of expanding business opportunity. But in later negotiation, Representatives of Technotronics focused more on global business scenario. The increased product cost and shipping cost was highlighted by them. This time, third sitting with this supplier, Porto has to decide what to do with this supplier.
Your negotiation strategy should be developed prior to the negotiation session. Remember, price is not the only variable subject to negotiation. In highly volatile industries like the computer industry, for example, capacity guarantees from suppliers are often critical. Be creative when crafting your procurement agreement.
• What should be Porto’s procurement strategy
• What game suppliers were playing in pervious negotiations ? How?
• What game they will play in final negotiation ? why?
• What will be your negotiation game strategy this time? How you are planning to achieve your goal?
• Which procurement agreement you are planning to sign? Explain your strategy.

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Study the case study and answer the following questions:

CPFR pilots have been a popular diversion in the FMCG retail sector. Often, they show – as in this case – that considerable opportunities for improvement exist, but that the problems of scaling up the pilot are too great. The scenario for this pilot, researched by Alexender Oliveira, was a manufacturer that supplied a major grocery retailer in the Germany. Figure I shows the typical demand series for one of the ten products in the study, all of which were in the high-volume ambient category. Total sales through the till (EPOS) for a given week were really quite stable.
While there is apparent high demand variation, most of this is due to predictable behaviour such as that due to different store opening hours. The day-by-day demand for this product was actually relatively stable over the course of a year. Figure II bellow places the pilot in context. The manufacturer’s national distribution centre (NDC) supplied one of the retailer’s regional distribution centres (RDCs), which in turn served 10 stores in the pilot.
The starting situation that Alexander found was that forecasting methods were based on a history of the last 2–3 months. While this gave the correct day-by-day pattern, it was insensitive to actual demand during a given week. As can be seen in Figure I, the actual demand pattern varies from day to day across the series due to a proportion of randomness in the pattern. The replenishment cycle was unresponsive because daily deliveries were based on forecasts. This resulted in high safety stocks and poor on-shelf availability. Figure III provides an inventory profile across the supply chain. The sum of the vertical (average days of stock) and horizontal (average lead time in days) gives the total time for a new

batch of product to progress from manufacturing site to shelf. This totals an massive 4–5 weeks!
Alexander coordinated the provision of forecast data from both manufacturer and supplier. Both forecasts were posted on a web site, and he was asked to provide instructions as to how much product the manufacturer should supply each day. Stock for the 10 stores was ‘ring fenced’ at the retailer’s RDC – that is, it could not be supplied to any other than the 10 stores in the pilot.
Alexander soon found that current forecast data did not take daily fluctuations into account, and was based on far too long a history period. By tracking daily demand, it was possible to allow for the randomness without anything like the current quantity of safety stock in the system. He devleoped a new replenishment algorithm that was based on the daily error between forecast and actual, and which added an extra day’s buffer stock. It soon became obvious that it was possible to run the system on far lower stock levels at the retailer’s NDC, as shown in Figure IV:
Alexander’s work had succeeded in reducing the stock level at the NDC from 7 days to 36 hours. In spite of the huge potential savings, the retailer did not go ahead with scaling up the pilot. This can be attributed to several factors. First, many other improvement initiatives were under way. The CPFR initiative would have needed further scarce resources. Second, scaling up would have required a different operating routine at all NDCs and the supporting (continue to next page)

IT infrastructure would need to have been changed. Third, what worked with one relatively efficient manufacturer may not have worked with others. Nevertheless, Alexander came up with the following five enablers for CPFR implementation:
1. Define single point of contact for each trading partner: to ensure that information is neither lost nor deteriorates during the exchange.
2 Define agenda for collaboration (short–medium–long term): to stabilise the collaborative goals over time.
3 Expand collaborative projects (scope and complexity): to gain critical mass.
4 Ensure continuous sharing of information: a key enabler of collaborative planning.
5 Development of trust: this takes time. Smaller problems are gradually removed from the CPFR process to help partners develop confidence that the long-term goal is achievable.

Questions
1. How valid do you see Alexender’s methodology to revisit CPFR process? Explain your logic.
2. What is your proposal to handle the case study’s problem apart from Alexender’s ?
3. Which demand foresting method can be useful in above scenario? Explain.
4. Do you propose CPFR or any other Supply Chain system to handle above scenario ?

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