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Warehouse Design - Coggle Diagram

- - - - remaining successful in a market is no longer guaranteed by simply pursuing excellence in efficiency and productivity. Apart from traditional factors like cost and function, customer satisfaction nowadays depends increasingly on the achievement of logistical success factors such as lead time, service level, and delivery due-date reliability. The thorough management of inventories plays a key role in the achievement of these goals.
      - But since inventories can cause immense
        costs, a thorough investigation into and support
        for performance management in this area of a
        business is necessary and has a substantial impact
        on a business’s overall performance.
      - Investment in inventory often represents
        one of the largest single commitments of
        capital in business and may amount to 25% of
        total assets
    - - Inventory Management
        
        Delivery delay
        
        Service level
        
        How to evaluate the management process modelling the inventory process
        
        inventory throughput diagrams
        
        Input
        
        output
        
        demand
        
        inventory level
        
        for detailed analysis, a throughput diagram is created for a single article in inventory.
        
        do not assess if stock levels are necessary of sufficient, see inventory operating curves.
        
        inventory operating curves
        
        goods of stock that is needed to fulfil customer demands
        
        Service level operating curve
        
        a more industrial practice
        
        the servicel level is the proportion of customer orders which are delivered at the requested or agreed time to the total numbers of customers orders.
        
        delivery delay operating curve
        
        average delivery delay vs average inventory level
        
        Input
        
        delivery quantity
        
        reliability of the delivery quantity
        
        delivery date
        
        output
        
        Required quantity
        
        reliability of required quantity
        
        reuirement data
        
        Planning quantity and replenishment time is also important
        
        mean inventory level is used to maintain safety stock and thus offers a way to measure the inventories performance
  - - - data-driven approach based on Linear programming under different objectives and service level constraints to optimize a target (linear) inventory function
        
        we assume that true demand is a (linear)
        function of the sales price p plus some normally distributed error
        term u with zero mean and variance s2,
        
        market size between (1000, 2000), slop b (500, 1000), demand volaltilty is generated such that coefficient of variance at mean price eaquals 0.3 or 0.5.
        
        experiment was conducted 500 times
        
        In summary, the impact of violations of the standard OLS
        assumptions varies not only with the type of assumption, but also
        with the kind of service level used. While poor performance of
        OLS can be compensated by applying problem-specific remedies,
        the LP approach still outperforms these countermeasures in
        several cases in terms of inventory level
      - Cost-model
        
        Rather than following the indirect (parametric) approach to
        first estimate a demand model and then determine the inventory
        level B, we propose an integrated approach and assume that the
        required inventory level B is a linear function of the explanatory
        variables
      - service level model
        
        The goal is to find the target inventory function parameters such that inventory holding costs are minimized subject to a service level constraint
    - - The target inventory level B thus consists of two components:
        mean demand and safety inventory
      - and depends on the target P1 or P2 service level or, equivalently, on penalty and holding costs.
      - assumption of normal distributed customer demands
      - deterministic OLS (Ordinary Least Square) approach where normal distributed forecast error are incorporated.
        
        based on method of moments
        
        based on calculus and linear programming
      - It is a common phenomenon in retail that one observes larger
        variations in demand when prices are especially low under
        promotions or extreme weather conditions or vice versa. As a
        consequence, the error term is heteroscedastic and the least
        squares estimator is no longer BLUE. It is still unbiased and linear, but not efficient. This means that standard OLS does not yield the minimum variance estimator
    - - based on 64 stores
      - based on daily sales for salad, customer demands, weather conditions, prices
      - no seasonality or structural breaks are considered
- - - - We consider the allocation process as a discrete stochastic optimization model with uncertain demands represented by sample paths from a normal distribution
      - At the end of each period, the retailer calculates the safety stock
        at the fulllment center (FC)s and may order the amount needed at these FCs, along with the required inventory
      - we use single item orders that include such items as computers,
      - For our analysis, we use the online retailing networks of Amazon and Flipkart as canonical examples.
      - Because of the diculty of solving the safety stock allocation problem in a large network, we use a divide and conquer strategy where the network is divided into multiple clusters of FCs, connected via a Minimum Spanning Tree (MST) graph.
      - Our study proposes a safety stock allocation approach in a multi-location online retailing network.
      - The goal here is to develop an easily implementable safety stock allocation mechanism that may benefit online retailers.
    - - single-item order and do not return the item
      - periodically replenish the single item by a single supplier at the same per unit cost.
      - The supply lead time from the supplier to any FC, L is a fixed constant and is known.
      - Our model includes lost sales;
      - We do not consider backordering.
      - Any FC can ship an item to any customer location
      - shipment and transshipment costs are exogenously given.
      - consumer demand using a normal distribution
      - We do not consider supplier fulllment.
      - Transshipments occur from one FC having a surplus to another FC facing a stock out.
    - - Step 1: Divide the network of n FCs into a number of clusters using MIPCluster, where each cluster groups nearby FCs to form a hub-spoke system.
      - Step 2: Locate the hub FC for each cluster using the p-center algorithm.
      - Step 3: Link the clusters' hubs with a Minimum Spanning Tree (MST) graph to create the cost-effective transshipment links between clusters.
      - Step 4: Following the cluster formations at step 3, we consider each cluster as a node and run MIP_{MP} to estimate the cluster-wise safety stock allocation.
      - Step 5: Model each cluster as a hub-and-spoke network to allocate safety stock at each FC using Theorem 1.
      - Output: Allocation of safety stock at each FC at each cluster.
    - - Distribution of Stock per FC
      - Performance Analysis of PASS
      - Performance evaluation of PASS via robust optimization
  - - - Enterprise A's 2016-2017 Sales volumes was analyzed to predict future sales
        
        Exponential Smoothing based on historical data. of 15days
        
        Test how goed the models fit to off-seasons and peak season
      - the predictes sales volume was then used to estimate the area and capacity of the warehouse required to solve future excess warehouse storage problems.
        
        Determine safe storage capacity of warehouses for future sales
      - Entropy and the technique for order preference by similarity to an ideal solution (TOPSIS) were used to construct a decision-making model, and practical plans for solving excess warehouse storage were evaluated.
        
        Determine decision-making criteria by expert seminars 10 Experts were invited to discuss the model for building warehouses to resolve excess storage. The decision-making criteria were determined through brainstorming.
        
        Geographical location and conditions
        
        Infrastructure conditions
        
        Water and power supply conditions
        
        logistics delivery and transportation conditions
        
        material loading, unloading and stacking
        
        Labor costs
        
        warehouse fees
        
        Technical level of warehouse functions
        
        Service level
        
        Determine objective weight of warehouse construction criteria
        
        Entropy is originally a measure of physical phenomena, specifically, the degree of disorder or randomness of molecules in a system in a certain state (Shannon, 1948). The lower the entropy is, the more neatly arranged the molecules are, and the closer the system is to a perfect crystal (Shannon, 1948). Conversely, the higher the entropy is, the less regularly arranged the molecules are, and the greater the randomness in the system becomes.
        
        Relatively even distributed weights
      - concrete optimization plans and strategies for solving the problem were proposed.
        
        Define warehouse optimization plan building (design)
        
        In TOPSIS, proposed by Hwang and Yoon (1981), the decision-maker determines a positive ideal solution (PIS) and a negative ideal solution (NIS). The distance between the evaluated plan and the PIS is then calculated.
- - - - In spite of the importance of warehouse design, a number of reviews of the literature have concluded that relatively little has been written in academic journals on the systematic approach that should be taken by warehouse designers.
      - Typical literature conclusions
        
        very few papers deal with the general warehouse design problem
        
        there is not a procedure for systematically analysing the requirement and designing a warehouse to meet the operational need
        
        theoretical basis for a warehouse design methodology
        still seems to be lacking
        
        A comprehensive and science-based methodology for
        the overall design of warehousing systems does not
        appear to exist
      - The scope for the research is the design phase from the
        time a specific need is identified for a warehouse through to an operational specification being produced, detailing for example, operating methods, equipment, staffing levels, layout and costs.
      - As a validation exercise to identify whether this framework reflected current practice, twelve warehouse design companies were contacted by telephone or face-to-face. Details were sent out by e-mail for their comments and responses were received from seven of the companies
        
        11 Steps used by warehouse design companies by Oxley (1994)
        
        Define Concept
        
        Acquire data
        
        These lists include product details, order profiles, goods arrival and despatch patterns, cost data and site information (where
        a site has already been identified)
        
        Produce functional specifications
        
        Produce technical specifications
        
        select the means and equipement
        
        Develop Layout (CAD)
        
        Select planning and control policies
        
        Analyse data
        
        Customer order profiling
        
        Item activity profiling
        
        inventory profiling
        
        calender-clock profiling
        
        activity relationship profiling
        
        investment profiling
        
        Simulation
        
        Determine operating procedures and methods
        
        including checklists, warehouse zoning, technology assessment charts, concept library, and standard work procedures.
        
        Evaluate and assess