Supply Chain Management Case And Techniques Analysis

Supply Chain Management Case and Techniques Analysis

RIMOWER, a cosmetic company operating exclusively within the Spanish market, has established a competitive presence for ten years, with a workforce of 100 employees and an annual sales volume of approximately 2,100,000 units. Anticipating a 12% increase in demand driven by COVID-19 constraints easing, they seek to optimize various aspects of their supply chain. The tasks include applying the Economic Order Quantity (EOQ) model to determine optimal order quantities and total costs, forecasting sales using moving average techniques, and evaluating Industry 4.0 technologies to mitigate risks within different supply chain models.

The company’s supply chain involves sourcing from a supplier 500 km away, negotiations resulting in a new price per unit of €4, with a transportation cost of €2.5 per km per truck. Employee labor costs are €20/hour, and processing an order takes 0.7 hours. Inventory holding costs amount to €5 annually, and the goal is to manage space, shipments, and deliveries efficiently given limited warehouse capacity. Furthermore, their historical sales data, adjusted for COVID impact in 2020, will be used to forecast demand using moving average and weighted moving average models.

In addition to these quantitative analyses, the company aims to explore Industry 4.0 technologies such as RFID, automation, and predictive analytics to improve responsiveness, anticipation, and postponement strategies in their supply chain. The discussion will address how these digital tools can mitigate risks associated with demand variability, supply disruptions, and inventory obsolescence.

Paper For Above instruction

Introduction

Effective supply chain management (SCM) is critical for companies like RIMOWER to stay competitive, especially in dynamic markets influenced by unpredictable factors such as global pandemics. This paper synthesizes quantitative strategies, including the EOQ model and sales forecasting techniques, with technological solutions under the umbrella of Industry 4.0, to enhance operational efficiency and mitigate risks. Recognizing the importance of balancing inventory costs, lead times, and responsiveness, the discussion integrates traditional inventory management with innovative digital tools to propose a comprehensive SCM framework tailored for RIMOWER’s specific context.

Application of EOQ Model

The EOQ model helps firms determine the optimal order quantity that minimizes total inventory costs, including ordering and holding expenses. For RIMOWER, the primary parameters include demand (D), ordering cost (S), unit cost (C), and holding cost (H). Given their projected demand of 2,352,000 units post-12% increase, with an ordering cost of €20 per order for processing time and transportation costs, the EOQ provides a systematic method to streamline procurement.

The EOQ formula is expressed as:

EOQ = √(2 × D × S / H)

Where:

• D = 2,352,000 units
• S = €20 (labor cost linked to order processing)
• H = €5 (annual holding cost per unit)

Calculating the EOQ yields:

EOQ ≈ √(2 × 2,352,000 × 20 / 5) ≈ √(18,816,000) ≈ 4,339 units

This indicates each optimal order should roughly be for 4,339 units. The expected number of orders per year is:

N = D / EOQ ≈ 2,352,000 / 4,339 ≈ 541 orders

The optimal interval between orders is:

T = 365 / N ≈ 365 / 541 ≈ 0.67 days, or approximately every 16 hours, highlighting a need for a continuous replenishment system rather than discrete orders.

The total annual cost (TC) combining ordering and holding costs is:

TC = (D / EOQ) × S + (EOQ / 2) × H + purchase cost

Considering the purchase cost per unit (€4), total costs include procurement, storage, and ordering, summing to a detailed financial plan for procurement cycles.

Demand Forecasting Using Moving Averages

Forecast accuracy is vital for inventory and production planning. Using historical sales data, excluding the COVID-impacted 2020, the models include the four-year moving average and a weighted moving average emphasizing recent trends. These models help smooth out fluctuations and anticipate future demand.

The four-year moving average is calculated by averaging sales from 2016 to 2019 for each period, providing a baseline unaffected by anomalous pandemic effects. The weighted moving average assigns higher importance to the most recent year (0.75) over the second most recent (0.25), reflecting current market trends.

Applying these models ensures that the forecast is flexible and responsive to recent changes, enabling RIMOWER to align production scheduling, inventory levels, and logistics planning effectively.

Industry 4.0 Technologies for Risk Mitigation

Industry 4.0 introduces digital innovations that can significantly enhance supply chain resilience. In the anticipatory model, technologies such as predictive analytics and IoT sensors enable the company to forecast potential disruptions proactively.

For responsiveness, RFID and automation facilitate real-time tracking and rapid response to demand fluctuations or supply delays. RFID tags, for instance, improve accuracy in inventory management, reducing stockouts and overstocks. Automated systems such as robotics streamline order picking and packing, minimizing human error and increasing throughput.

In the postponement model, digital twins and flexible manufacturing systems allow RIMOWER to delay product differentiation until closer to demand fulfillment, reducing obsolescence risk. Cloud-based SCM platforms promote transparency and agility by integrating suppliers, production, and distribution data.

Conclusion

Optimizing RIMOWER’s supply chain requires a synergistic approach that leverages quantitative models like EOQ and forecasting techniques alongside cutting-edge Industry 4.0 technologies. Together, these strategies facilitate cost reduction, risk mitigation, and agility in an increasingly complex and uncertain market environment. Implementing continuous improvement through digital tools will empower RIMOWER to adapt swiftly to future challenges, ensuring sustained competitiveness in the cosmetics industry.

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