PRACTICAL APPLICATION OF THE BASIC STATISTICAL METHODS IN THE SUPPLY CHAIN LOGISTICS. Roman MAROUŠEK

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1 PRACTICAL APPLICATION OF THE BASIC STATISTICAL METHODS IN THE SUPPLY CHAIN LOGISTICS Roman MAROUŠEK ŠKODA AUTO a.s. Vysoká škola, Mladá Boleslav, Abstract The paper shows some practical approaches of data analyses within the Supply Chain warehouse logistics. In the first part, it describes what data usually becomes subject to analyses; it shows how it can be analysed and what the outputs can tell us in relation to the warehouse operations design/improvement. The second part deals with some possibilities of Pareto principle application within the Supply Chain warehouse logistics. Keywords: Supply Chain, Warehouse Operations Design, Pareto, Logistics data analyses 1. INTRODUCTION The Logistics and Supply Chain has been recognized as blood circulation of the current worldwide economy. The material flow, not only the flow of the final products, between continents requires a highly efficient operation in order to support the cost-saving approach within the whole production chain. Detailed future planning of the Logistics and Supply Chain business, together with continuous reviewing of the current set up of the operations, is the daily work of logistics process designers and supply chain optimization professionals. The typical characteristics of logistics is singularity of their solutions. The proper planning and set up are a combination of a) experience of the team members who plan future solutions and b) accuracy of the data based on which the planning and modelling is done. The experience and competence of the team as well as team members is an irreplaceable factor of the design planning and can be strengthened by selecting the right team members and continuously improving their knowledge base. On the other hand, the accuracy of data always becomes a problem. This is because logistics processes have to be in line with the final product of the production chain consumption. The consumption nowadays is responsive to the economical situation and fashion trends the consumption is driven by the consumers and not by producers. And it is very hard to predict the market and consumers' behaviour. For that reason, the logistics providers use various tools and techniques for future process planning, as follows: (1) Pareto analysis, (b) Market/customer segmentation, (c) Customer service studies, (d) Relationship mapping. (e) Process charts, (f) Value/time analysis, (g) Time-based process mapping. [in A. Rushton, P. Croucher, P. Baker. The handbook of logistics and distribution management,4th edition UK, The Chartered Institute of Logistics and Transport, 2010, 635 p.] In the following text, I would like to focus on various examples of data analyses, particularly the use of the Pareto analysis within the warehouse itself and warehouse activities design. 2. DATA FOR THE WAREHOUSE DESIGN AND THEIR ANALYSING 2.1 Data requirements For the intended purpose (warehouse design) we need the data to be able to: design optimal warehouse layout (internal, external), suggest operations procedures, collect data for the WMS requirements,

2 propose suitable storage and handling equipments, calculate staff and scheme of their work, calculate operational budget and investments revise finally suggested design against the business assignment. 2.2 What kind of data to collect, typical data sources In most cases, we have to work with historical data, or we have to map the data from topical operations. The latter is much more suitable for our purpose as we can describe and analyse the situation AS IS and use it as a learning point for a better future solution. The data we collect relates to the storage and movement. Tab. 1 - Example of data to be collected Kind of data Storage Movements Others Subject of data Categories and types of cases to be stored (type and weight of cartons, ugly SKUs, etc.) No. of pallets (or largest equivalents) to be stored per product line Daily movements rate per SKU/pallet equivalents Time-defined throughput Requirements for VAS Time for unloading, quality check and storage Time needed for picking, marshalling and dispatch Seasonal aspects Business growth expected during contract period Product life cycles Special requirements for storage Changes There exist more sources where the data can be found: (1) By selecting it from WMS, if the process is supported by an IT system. (2) By selecting it from archived paperwork, if process is paper-based. (3) Customer budget and business predictions. (4) Operational staff's knowledge and ideas. (5) Data on WH storage system utilization. (6) Existing procedures description (SOP). (7) Procurement related contracts, operational costs of equipment, staff and facilities. (8) Etc. 2.3 Data analyses The collected data should be subject of analyses. For analyses the basic statistical methods are used. Right and experiences based interpretation of the data is the core for successful and professional application of warehouse design. The following table shows examples what data can be compared and analyzed, how they can be analyzed and what possible output from analyses can we get to use it for warehouse operations design.

3 Tab. 2 Examples of the data analyses What data we analyze (example) How the data are presented Data analyses output Inbound data: Time period, Delivery type, No. of deliveries, No. of order lines, No. of pieces, No. of SKUs, volume in m3 Outbound data: Time period, No. of order lines, No. of pieces, No. of SKUs, volume in cbm, business profile Pallet/Case order increment distribution - inbound: Compares percentage of pallets/cases against percentage of order lines Receipt lines per order distribution: Number of lines compared to volume of individual part numbers. Comparison of inbound and outbound deliveries: Inbound vs outbound deliveries, orders, order lines and volume. Outbound form distribution by order: Compares data of order lines against data of order type. Sales order increment distribution per distributed form: Compares order lines against type of package (pallet, case, ) Pareto distribution (ABC analysis) Table form is sufficient Table form is sufficient Chart Axis X % of pallets/cases received Axis Y - % of order lines Table: data split: range of order lines vs range of volume filled by No. of Table form is sufficient Chart form Chart form Chart form General overview of material flow profile in. General overview of material flow profile out. Information on throughput. Evaluation of which one of the contractors delivers optimal volume of pallets/cases. This can have a positive impact on handling reduction and reduction of distribution cost. Presentation of inbound split according to the volume. The evaluation shows how exact the put-away will be. The data analyses can show whether all material in has to be put away or whether there is a cross-docking possibility. The output of this analyses helps in designing a way of picking process and the picking area. The split of unit load requested by order. E.g. how many full pallets/full cases will be required to pick up per order. See next. The above mentioned analyses do not show the final number of all views on the material flow we may analyse. The number of analyses and the depth of analyses depends on the data availability. The outcomes from analyses can be used for improvement (re-design) of already established warehouse operations and for design of new warehouse operations when planning a new business. 3. PARETO PRINCIPLE 3.1 A few words about Pareto principle This principle was introduced by an Italian economist Vilfred Pareto who found out that 80 % of wealth is distributed among 20 % of the population. This rule goes against the statement that everything in the world has to be balanced. The principle has also been known under the names of: 80/20 rule, principle of factor sparsity and law of the vital few. The rule was abused by the Italian fascistic ideology and for that reason was dismissed for years. It was reintroduced by George Zipf after the Second World War. He observed that % of activities only use % of sources and he named this the rule of smallest effort. In 1951,

4 the massive use of the principle for the quality control started. The application of the rule was made by Joseph Juran who found that 80 % of bad quality has been caused by 20 % of causes. In 1963, IBM engineers found that 80 % of computer time has been wasted by 20 % of work with operational code. [in R. Koch.The 80/20 principle, the secret of achieving more with less, 1 st Czech edition, Management Press Praha, 2000, 244 p.] It is very important to say that the function distribution cannot only be 80:20, but it can also be 70:20 or 80:30. It depends on the number of data observed and also on the Pareto distribution parameter which can be expressed by the formula: α = log Pareto principle application for the logistics The principle is known in the logistics practice as ABC/XYZ analysis. In other words, its use is based on the presumption that: 20 % of products create 80 % of revenue (or profit) 80 % of profit are created by 20 % of customers. Those hypotheses are used to formulate the levels of customer service performance. [in Lambert, Stock, Ellram, Fundamentals of Logistics Management, 1 st Czech edition, Computer Press Praha, 2000, 589 p.]. The authors of this book also describe another approach for the use of the ABC analysis in order to optimize the storage locations in the warehouse and stored supplies. The practical explanation of Pareto principle used for the purpose of WH design planning is stated in the book [P.Baker, D.Hill, G.Newbury, J. van Geersdaele, The Principles of Warehouse Design, 3rd edition, the Chartered Institute of Logistics and Transport in the UK, 2010, 139 p.]. Mr. Hill, the author of the section named Data requirements and planning base, defines that based on this principle, 20 % of the inventory lines account for about 80 % of movements, and a significant proportion of the inventory and picking activity (relating to the business uniqueness). Other views on the stored material flow through analysing via Pareto principle can be by ranking the SKUs in each group in terms of the number of picking (in the same). In the first half of this year I have made little investigation when I asked the group of logistics professionals the question if they use Pareto within their organization. The output from this investigation is showed in the chart 1. Although the numbers do not prove the distribution of the Pareto principle use, it shows that there are various levels of its use. The deeper investigation for what Pareto is used would be also interesting. 3.1 Practical application of the Pareto principle for WH design planning In this section we will show practical application of the Pareto principle in the process of WH design and operations planning. It has to be pointed out that this application is not only one we may use. The task logic and objective is presented in the picture 1. Chart 1. Investigation of the Pareto principle use within the logistics organization

5 CARTON CARTON PALET CARTON TROUGHPUT? WHAT IS THE RELATION BETWEEN SKUs BANDS AND NUMBER OF S/CARTONS /PALETS? The entry data for these analyses are: (a) Annual throughput in units (b) Nbr. of working days a year (c) Average nbr. of units in carton (d) Average nbr. of cartons/pallet (e) Nbr. of SKUs BASED ON THIS INFORMATION WE CAN CONSIDER FOLOWING PARAMETRES: - HOW MANY S AND PALET/CARTON EQUIVALENTS WILL BE PUT THROUGH DAILY - WHICH HAVE IMPACT ON THE WH/PICKING AREA SIZE - WHICH HAVE INFLUENCE ON THE STAFF WORKLOAD AND PLANNING Fig. 1. The logic and objectives of material flow Pareto analyses Outcome of the analyses should be base for evaluation of WH requirements and WH staff workload built on the material flow. The procedure of the analyses: Collect entry data and verify their correctness From annual throughput units calculate daily throughput in units Calculate annual/daily carton/pallets equivalent from the throughput units Define number of intervals within which the data will be distributed (eg. 10) For each interval define % of SKUs from total Input total SKUs and calculate total for each of intervals + calculate cumulative nbr. Propose the % of throughput units/cartons/pallets against SKUs. For our case presume that the balance 80% of throughput is created by 20% of SKUs Calculate nbr. of units / cartons / pallets according to pre-defined % split Calculate units/cartons/pallets per SKU within each interval The following pictures show model of this analyzes. Entry data Nbr. of working days a year 290 Annual throughput (in units) Daily throughput (units) Units / carton 25 Daily throughput (cartons) 552 Units / palet 10 Daily throughput (palets) 55 Number of SKUs 6000 Fig. 2. Model entry data

6 Interval % cumulative % Total SKUs Cumulative SKUs 1 10% % 20% % 35% % 50% % 65% % 75% % 85% % 90% % 95% % 100% Nbr. Of SKUs 6000 Fig. 3. SKU calculation Throughput in % Cummulative throughput in % Units Cartons Pallets Units/SKU Cartons/SKU Pallets/ SKU 30% ,90 0,28 0,00 50% 80% ,49 0,05 0,00 10% 90% ,53 0,00 0,00 5% 95% ,77 0,00 0,00 2% 97% ,31 0,00 0,00 1% 98% ,23 0,00 0,00 0,50% 99% ,11 0,00 0,00 0,50% 99% ,23 0,00 0,00 0,25% 99% ,11 0,00 0,00 0,25% 100% ,11 0,00 0,00 Fig. 4. Throughput calculation 3.2 Output from analyses, data interpretation The above data indicates that we may expect that the majority of throughput will be disintegrated into small locations and material manipulation will be in pieces mainly. BUT the final conclusion has to be done based on the data from more sources holistic view is needed to achieve most probable assessment. 4. CONCLUSION Successful supply chain planning requires competent professionals and very professional data assessment. For fruitful WH operations planning and improvement we need to analyze historical data. Various collations of those data give us various information. There exist a lot of ways to use Pareto principle for logistic data analyses. We have to keep holistic review of all data to achieve most probable assessment. LITERATURE [1] A. Rushton, P. Croucher, P. Baker. The handbook of logistics and distribution management,4th edition UK, The Chartered Institute of Logistics and Transport, 2010, 635 p. [2] R. Koch.The 80/20 principle, the secret of achieving more with less, 1 st Czech edition, Management Press Praha, 2000, 244 p. [3] in Lambert, Stock, Ellram, Fundamentals of Logistics Management, 1 st Czech edition, Computer Press Praha, 2000, 589 p. [4] P.Baker, D.Hill, G.Newbury, J. van Geersdaele, The Principles of Warehouse Design, 3 rd edition, the Chartered Institute of Logistics and Transport in the UK, 2010, 139 p.